LIFEx - Publications - Journal papers

Here is a list of publications related to LIFEx software.

LIFEx-texture: Cepeda, S.; Luppino, L.T.; Pérez-Núñez, A.; Solheim, O.; García-García, S.; Velasco-Casares, M.; Karlberg, A.; Eikenes, L.; Sarabia, R.; Arrese, I.; et al. Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric Postoperative MRI. Cancers 2023, 15, 1894. https://doi.org/10.3390/cancers15061894
LIFEx-texture: Watanabe, M., Ashida, R., Miyakoshi, C. et al. Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features. European J Hybrid Imaging 7, 5 (2023). https://doi.org/10.1186/s41824-023-00163-8
LIFEx-texture: Dondi, F.; Gatta, R.; Albano, D.; Bellini, P.; Camoni, L.; Treglia, G.; Bertagna, F. Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [18 F]FDG PET/CT: A Comparison between Two PET/CT Scanners. J. Clin. Med. 2023, 12, 255. https://doi.org/10.3390/jcm12010255
LIFEx-texture: Lara Cavinato, Noemi Gozzi, Martina Sollini, Margarita Kirienko, Carmelo Carlo-Stella, Chiara Rusconi, Arturo Chiti, Francesca Ieva, Explainable domain transfer of distant supervised cancer subtyping model via imaging-based rules extraction, Artificial Intelligence in Medicine, Volume 138, 2023, 102522, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2023.102522
LIFEx-texture: Awais, Muhammad; Khan, Shahmeer; Wasay, Mohammad; Azeemuddin, Muhammad; Shoukat, Ayesha; and Khan, Hafsa (2022) "Mr Textural Features (Radiomics) For Predicting Response to Treatment in Patients with Intracranial Tuberculoma: A Retrospective Cross-Sectional Study," Pakistan Journal of Neurological Sciences (PJNS): Vol. 17: Iss. 3, Article 9. https://ecommons.aku.edu/pjns/vol17/iss3/9
LIFEx-texture: Mazzara S, Travaini L, Botta F, Granata C, Motta G, Melle F, Fiori S, Tabanelli V, Vanazzi A, Ramadan S, Radice T, Raimondi S, Lo Presti G, Ferrari M.E, Jereczek-Fossa B.A, TarellaC, Ceci F, Pileri S, Derenzini E. Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL. Blood Adv (2023) 7 (4): 630–643. http://dx.doi.org/10.1182/bloodadvances.2022007825
LIFEx-MTV: Chan KC, Perucho JAU, Subramaniam RM, Lee EYP. Utility of pre-treatment 18F-fluorodeoxyglucose PET radiomic analysis in assessing nodal involvement in cervical cancer. Nucl Med Commun. 2023 Feb 27:e001672. doi: 10.1097/MNM.0000000000001672. Epub ahead of print. PMID: 36826394. http://dx.doi.org/10.1097/MNM.0000000000001672
LIFEx-texture: Costa, G., Cavinato, L., Fiz, F. et al. Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible. J Digit Imaging (2023). https://doi.org/10.1007/s10278-023-00799-9
LIFEx-texture: Troiano G, Fanelli F, Rapani A, et al. Can radiomic features extracted from intra-oral radiographs predict physiological bone remodeling around dental implants: A hypothesis-generating study. Journal of Clinical Periodontology. 2023 Feb. DOI: 10.1111/jcpe.13797. PMID: 36843362. https://doi.org/10.1111/jcpe.13797
LIFEx-texture: Chilaca-Rosas M-F, Garcia-Lezama M, Moreno-Jimenez S, Roldan-Valadez E. Diagnostic Performance of Selected MRI-Derived Radiomics Able to Discriminate Progression-Free and Overall Survival in Patients with Midline Glioma and the H3F3AK27M Mutation. Diagnostics. 2023; 13(5):849. https://doi.org/10.3390/diagnostics13050849
LIFEx-texture: Colelli G, Barzaghi L, Paoletti M, Monforte M, Bergsland N, Manco G, Deligianni X, Santini F, Ricci E, Tasca G, Mira A, Figini S and Pichiecchio A (2023) Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Front. Neurol. 14:1105276. doi: 10.3389/fneur.2023.1105276
LIFEx-texture: Basso Dias, A., Mirshahvalad, S.A., Ortega, C. et al. The role of [18F]-DCFPyL PET/MRI radiomics for pathological grade group prediction in prostate cancer. Eur J Nucl Med Mol Imaging (2023). https://doi.org/10.1007/s00259-023-06136-0
LIFEx-texture: Amirhossein Sanaat, Hossein Shooli, Andrew Stephen Böhringer, Maryam Sadeghi, Isaac Shiri, Yazdan Salimi, Nathalie Ginovart, Valentina Garibotto, Hossein Arabi, Habib Zaidi. A cycle‑consistent adversarial network for brain PET partial volume correction without prior anatomical information. European Journal of Nuclear Medicine and Molecular Imaging. 20 Feb 2023. https://doi.org/10.1007/s00259-023-06152-0
LIFEx-texture: Na Wang, Meng Dai, Yan Zhao, Zhaoqi Zhang, Jianfang Wang, Jingmian Zhang, Yingchen Wang, Yunuan Liu, Fenglian Jing, Xinming Zhao. Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer. European Journal of Radiology Open 10 (2023) 100480. https://doi.org/10.1016/j.ejro.2023.100480
LIFEx-viewer: Jing Gao, Si Xu, Huijun Ju, Yu Pan and Yifan Zhang. The potential application of MR‑derived ADCmin values from 68Ga‑DOTATATE and 18F‑FDG dual tracer PET/MR as replacements for FDG PET in assessment of grade and stage of pancreatic neuroendocrine tumors. Gao et al. EJNMMI Research. https://doi.org/10.1186/s13550-023-00960-z
LIFEx-texture: Agüloğlu N, Aksu A, Unat DS. Machine learning approach using 18F-FDG PET-based radiomics in differentiation of lung adenocarcinoma with bronchoalveolar distribution and infection. Nucl Med Commun. 2023 Feb 9. doi: 10.1097/MNM.0000000000001667. Epub ahead of print. PMID: 36756766
LIFEx-texture: Crimì, F.; Agostini, E.; Toniolo, A.; Torresan, F.; Iacobone, M.; Tizianel, I.; Scaroni, C.; Quaia, E.; Campi, C.; Ceccato, F. CT Texture Analysis of Adrenal Pheochromocytomas: A Pilot Study. Curr. Oncol. 2023, 30, 2169–2177. https://doi.org/10.3390/curroncol30020167
LIFEx-texture: N. Stogiannos, H. Bougias, E. Georgiadou, S. Leandrou, P. Papavasileiou. Analysis of radiomic features derived from post-contrast T1-weighted images and apparent diffusion coefficient (ADC) maps for breast lesion evaluation: A retrospective study. Radiography 29 (2023) 355e361. https://doi.org/10.1016/j.radi.2023.01.010
LIFEx-texture: Nicosia, L.; Pesapane, F.; Bozzini, A.C.; Latronico, A.; Rotili, A.; Ferrari, F.; Signorelli, G.; Raimondi, S.; Vignati, S.; Gaeta, A.; et al. Prediction of the Malignancy of a Breast Lesion Detected on Breast Ultrasound: Radiomics Applied to Clinical Practice. Cancers 2023, 15, 964. https://doi.org/10.3390/cancers15030964
LIFEx-texture: Ju, H.M.; Kim, B.-C.; Lim, I.; Byun, B.H.; Woo, S.-K. Estimation of an Image Biomarker for Distant Recurrence Prediction in NSCLC Using Proliferation-Related Genes. Int. J. Mol. Sci. 2023, 24, 2794. https://doi.org/10.3390/ijms24032794
LIFEx-texture: Wang, X., Dai, Y., Lin, H. et al. Shape and texture analyses based on conventional MRI for the preoperative prediction of the aggressiveness of pituitary adenomas. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09412-7
LIFEx-texture: Cavinato, L.; Sollini, M.; Ragni, A.; Bartoli, F.; Zanca, R.; Pasqualetti, F.; Marciano, A.; Ieva, F.; Erba, P.A. Radiomics-Based Inter-Lesion Relation Network to Describe [18 F]FMCH PET/CT Imaging Phenotypes in Prostate Cancer. Cancers 2023, 15, 823. https://doi.org/10.3390/cancers15030823
LIFEx-texture: Seda Gülbaha Ates, Gülay Bilir Dilek, Gülin Ucmak. Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy. 2253-8089/© 2023 Sociedad Espanola de Medicina Nuclear e Imagen Molecular. https://doi.org/10.1016/j.remnie.2023.01.001
LIFEx-CalciumQuantitation: Nardone, V.; Reginelli, A.; De Marco, G.; Natale, G.; Patanè, V.; De Chiara, M.; Buono, M.; Russo, G.M.; Monti, R.; Balestrucci, G.; Salvarezza, M.; Di Guida, G.; D’Ippolito, E.; Sangiovanni, A.; Grassi, R.; D’Onofrio, I.; Belfiore, M.P.; Cimmino, G.; Della Corte, C.M.; Vicidomini, G.; Fiorelli, A.; Gambardella, A.; Morgillo, F.; Cappabianca, S. Role of Cardiac Biomarkers in Non-Small Cell Lung Cancer Patients. Diagnostics 2023, 13, 400. https://doi.org/10.3390/diagnostics13030400
LIFEx-texture: Mendes, B.; Domingues, I.; Dias, F.; Santos, J. Cone Beam Computed Tomography Radiomics for Prostate Cancer: Favourable vs. Unfavourable Prognosis Prediction. Appl. Sci. 2023, 13, 1378. https://doi.org/10.3390/app13031378
LIFEx-texture: Degtiarova G, Garefa C, Boehm R, CianconeD, Sepulcri D, Gebhard C, Giannopoulos Aju, Pazhenkottil P, Kaufmann P.A. and Buechel R.R Radiomics for the detection of diffusely impaired myocardial perfusion: A proof-of concept study using 13N-ammonia positron emission tomography. J Nucl Cardiol 1071-3581. http://dx.doi.org/10.1007/s12350-022-03179-y
LIFEx-texture: Annovazzi, A.; Ferraresi, V.; Covello, R.; Ascione, A.; Vari, S.; Petrongari, M.G.; Baldi, J.; Biagini, R.; Sciuto, R. Prognostic Value of Pre-Treatment [18F]FDG PET/CT Texture Analysis in Undifferentiated Soft-Tissue Sarcoma. J. Clin. Med. 2023, 12, 279. https://doi.org/10.3390/jcm12010279

About LIFEx (but not verified):

About LIFEx (Failure to comply with the LIFEx license agreement):

LIFEx-texture: Xie Y, Teng Y, Jiang C, Ding C, Zhou Z. Prognostic value of 18F-FDG lesion dissemination features in patients with peripheral T-cell lymphoma (PTCL). Jpn J Radiol. 2023 Feb 8. doi: 10.1007/s11604-023-01398-y. Epub ahead of print. PMID: 36752954.
LIFEx-texture: Bhatt, M., Shende, P. Advancement in Machine Learning: A Strategic Lookout from Cancer Identification to Treatment. Arch Computat Methods Eng (2023). https://doi.org/10.1007/s11831-023-09886-0
LIFEx-texture: Zhao, X., Zhao, Y., Zhang, J. et al. Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics. EJNMMI Res 13, 4 (2023). https://doi.org/10.1186/s13550-023-00956-9
LIFEx-texture: Yutao Yang, Hao Chen, Min Ji, Jianzhang Wu, Xiaoshan Chen, Fenglin Liu, Shengxiang Rao, A new radiomics approach combining the tumor and peri-tumor regions to predict lymph node metastasis and prognosis in gastric cancer, Gastroenterology Report, Volume 11, 2023, goac080, https://doi.org/10.1093/gastro/goac080

Thesis:

LIFEx-texture: Artificial intelligence in molecular imaging: from machine to deep learning. Riccardo Laudicella. https://iris.unime.it/retrieve/e234d14a-b211-415e-8bef-0640248279de/Tesi.pdf

Poster:

LIFEx-texture: Giulia Martini, Valerio Nardone, Davide Ciardiello, Marco De Chiara, Teresa Troiani, Luca D'ambrosio, Stefania Napolitano, Claudia Cardone, Chiara Cremolini, Filippo Pietrantonio, Evaristo Maiello, Antonio Avallone, Salvatore Cappabianca, Fortunato Ciardiello, Alfonso Reginelli, and Erika Martinelli. Journal of Clinical Oncology 2023 41:4_suppl, 241-241(link)

Article not in English:

LIFEx-texture: SG Ates, GB Dilek, G Uçmak. Heterogeneidad del tumor primario en la18F-FDG PET/TC pretratamiento para predecir el pronóstico en pacientes con cáncer de recto sometidos a cirugía tras. Revista Española de Medicina Nuclear e Imagen …, 2023, ISSN 2253-654X. https://doi.org/10.1016/j.remn.2023.01.001

Review:

LIFEx-texture: Mirestean, C.C.; Iancu, R.I.; Iancu, D.P.T. Simultaneous Integrated Boost (SIB) vs. Sequential Boost in Head and Neck Cancer (HNC) Radiotherapy: A Radiomics-Based Decision Proof of Concept. J. Clin. Med.2023,12,2413. https:// doi.org/10.3390/jcm12062413
LIFEx-texture: Oliveira C, Oliveira F, Vaz SC, Marques HP, Cardoso F. Prediction of pathological response after neoadjuvant chemotherapy using baseline FDG PET heterogeneity features in breast cancer. Br J Radiol (2023) https://doi.org/10.1259/bjr.20220655
LIFEx-texture: Zhou Huijie, Luo Qian, Wu Wanchun, Li Na, Yang Chunli, Zou Liqun. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front. Immunol., 01 March 2023, Sec. Cancer Immunity and Immunotherapy, Volume 14 - 2023 | https://doi.org/10.3389/fimmu.2023.1088874
LIFEx-texture: Abou Karam, G.; Malhotra, A. PET/CT May Assist in Avoiding Pointless Thyroidectomy in Indeterminate Thyroid Nodules: A Narrative Review. Cancers 2023, 15, 1547. https://doi.org/10.3390/cancers15051547
LIFEx-texture: Role of Artificial Intelligence in PET/CT Imaging for Management of Lymphoma. Eren M., Veziroglu, Faraz Farhadi, Navid Hasani, Moozhan Nikpanah, Mark Roschewski, Ronald M. Summers and Babak Saboury. Semin Nucl Med 00:1-23. https://doi.org/10.1053/j.semnuclmed.2022.11.003

LIFEx-texture: Altay C, Başara Akın I, Özgül AH, Adıyaman SC, Yener AS, Seçil M. Machine learning analysis of adrenal lesions: Preliminary study evaluating texture analysis in the differentiation of adrenal lesions. Diagn Interv Radiol. DOI: 10.5152/dir.2022.21266 (doi)
LIFEx-texture: Annovazzi, A.; Ferraresi, V.; Covello, R.; Ascione, A.; Vari, S.; Petrongari, M.G.; Baldi, J.; Biagini, R.; Sciuto, R. Prognostic Value of Pre-Treatment [18F]FDG PET/CT Texture Analysis in Undifferentiated Soft-Tissue Sarcoma. J. Clin. Med. 2023, 12, 279. https://doi.org/10.3390/jcm12010279 (doi)
Vuijk, F.A.; Kleiburg, F.; Noortman, W.A.; Heijmen, L.; Feshtali Shahbazi, S.; van Velden, F.H.P.; Baart, V.M.; Bhairosingh, S.S.; Windhorst, B.D.; Hawinkels, L.J.A.C.; et al. Prostate-Specific Membrane Antigen Targeted Pet/CT Imaging in Patients with Colon, Gastric and Pancreatic Cancer. Cancers 2022, 14, 6209. https://doi.org/10.3390/cancers14246209 (doi)
LIFEx-texture: Hinzpeter, R.; Kulanthaivelu, R.; Kohan, A.; Avery, L.; Pham, N.-A.; Ortega, C.; Metser, U.; Haider, M.; Veit-Haibach, P. CT Radiomics and Whole Genome Sequencing in Patients with Pancreatic Ductal Adenocarcinoma: Predictive Radiogenomics Modeling. Cancers 2022, 14, 6224. https://doi.org/10.3390/cancers14246224 (doi)
LIFEx-texture: Peter McAnena, Brian M. Moloney, Robert Browne, Niamh O’Halloran, Leon Walsh, Sinead Walsh, Declan Sheppard, Karl J. Sweeney, Michael J. Kerin and Aoife J. Lowery. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer. McAnena et al. BMC Medical Imaging. (2022) 22:225 https://doi.org/10.1186/s12880-022-00956-6 (doi)
LIFEx-texture: Wu FZ, Wu YJ, Tang EK. An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules. Quant Imaging Med Surg 2022. doi:10.21037/qims-22-308 (doi)
LIFEx-MTV: Winkelmann, M., Blumenberg, V., Rejeski, K. et al. Prognostic value of the International Metabolic Prognostic Index for lymphoma patients receiving chimeric antigen receptor T-cell therapy. Eur J Nucl Med Mol Imaging (2022). https://doi.org/10.1007/s00259-022-06075-2 (doi)
LIFEx-texture: Assadi M, Manaﬁ-Farid R, Jafari E, Keshavarz A, Divband G, Moradi MM, Adinehpour Z, Samimi R, Dadgar H, Jokar N, Mayer B and Prasad V (2022) Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer treated with 177Lu-PSMA. Front. Oncol. 12:1066926. doi: 10.3389/fonc.2022.1066926 (doi)
LIFEx-texture: De Robertis, R.; Tomaiuolo, L.; Pasquazzo, F.; Geraci, L.; Malleo, G.; Salvia, R.; D’Onofrio, M. Correlation between ADC Histogram-Derived Metrics and the Time to Metastases in Resectable Pancreatic Adenocarcinoma. Cancers 2022, 14, 6050. https://doi.org/ 10.3390/cancers14246050 (doi)
LIFEx-texture: Stoehr F, Kloeckner R, Pinto dos Santos D, Schnier M, Müller L, Mähringer-Kunz A, Dratsch T, Schotten S, Weinmann A, Galle PR, Mittler J, Düber C, Hahn F. Radiomics-Based Prediction of Future Portal Vein Tumor Infiltration in Patients with HCC—A Proof-of-Concept Study. Cancers. 2022; 14(24):6036. https://doi.org/10.3390/cancers14246036 (doi)
LIFEx-texture: Yu K, Ying J, Zhao T, Lei L, Zhong L, Hu J, Zhou JW, Huang C, Zhang X. Prediction model for knee osteoarthritis using magnetic resonance–based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative. Quant Imaging Med Surg 2023;13(1):352-369. doi: 10.21037/qims-22-368 (doi)
LIFEx-texture: Gong H, Tang B, Li T, Li J, Tang L, Ding C. The added prognostic values of baseline PET dissemination parameter in patients with angioimmunoblastic T-cell lymphoma. eJHaem. 2022;1–11 (doi)
LIFEx-texture: Alencar NRG, Machado MAD, Mourato FA, Oliveira ML, Moraes TF, Mattos Junior LAR, Chang TC, Azevedo CRAS and Brandão SCS (2022) Exploratory analysis of radiomic as prognostic biomarkers in ¹⁸F-FDG PET/CT scan in uterine cervical cancer. Front. Med. 9:1046551. doi: 10.3389/fmed.2022.1046551 (doi)
LIFEx-main: Ahrari, S.; Zaragori, T.; Bros, M.; Oster, J.; Imbert, L.; Verger, A. Implementing the Point Spread Function Deconvolution for Better Molecular Characterization of Newly Diagnosed Gliomas: A Dynamic 18F-FDOPA PET Radiomics Study. Cancers 2022, 14, 5765. https://doi.org/10.3390/cancers14235765 (doi)
LIFEx-texture: Xue, Xq., Yu, WJ., Shao, XL. et al. Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer. Abdom Radiol (2022) https://doi.org/10.1007/s00261-022-03738-4 (doi)
LIFEx-main: Leslie Guzene, Arnaud Beddok, Christophe Nioche, Romain Modzelewski, Cedric Loiseau, Julia Salleron, Juliette Thariat. Assessing inter-observer variability in the delineation of structures in radiation oncology: A systematic review. International Journal of Radiation Oncology*Biology*Physics, 2022, ISSN 0360-3016 (doi)
LIFEx-texture: Zhang, T.; Xiang, Y.; Wang, H.; Yun, H.; Liu, Y.; Wang, X.; Zhang, H. Radiomics Combined with Multiple Machine Learning Algorithms in Differentiating Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor: More Hands Produce a Stronger Flame. J. Clin. Med. 2022, 11, 6789. https://doi.org/10.3390/jcm11226789 (doi)
LIFEx-texture: Berchiolli, R.; Torri, L.; Bertagna, G.; Canovaro, F.; Zanca, R.; Bartoli, F.; Mocellin, D.M.; Ferrari, M.; Erba, P.A.; Troisi, N. [18 F]-Fludeoxyglucose Positron Emission Tomography/Computed Tomography with Radiomics Analysis in Patients Undergoing Aortic In-Situ Reconstruction with Cryopreserved Allografts. Diagnostics 2022, 12, 2831. https://doi.org/10.3390/diagnostics12112831 (doi)
LIFEx-texture: Chen, NB., Xiong, M., Zhou, R. et al. CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment. Radiat Oncol 17, 184 (2022). https://doi.org/10.1186/s13014-022-02136-w (doi)
LIFEx-texture: Li J, Zhang B, Ge S, Deng S, Hu C and Sang S (2022) Prognostic value of 18F-FDG PET/CT radiomic model based on primary tumor in patients with non-small cell lung cancer: A large single-center cohort study. Front. Oncol. 12:1047905. doi: 10.3389/fonc.2022.1047905 (doi)
LIFEx-texture: Rozenblum, L., Zaragori, T., Tran, S. et al. Differentiating high-grade glioma progression from treatment-related changes with dynamic [18F]FDOPA PET: a multicentric study. Eur Radiol (2022)(doi)
LIFEx-texture: Analisi radiomica di tumori cerebrali studiati con 18F-DOPA PET/CT= Radiomics analysis of brain tumor studied with 18F-DOPA PET/CT - Thesis - AM Torre - 2022 (link)
LIFEx-texture: Gruzdev I.S., Karmazanovsky G.G., Lapteva M.G., Zamyatina K.A., Tikhonova V.S., Kondratyev E.V., Struchkov V.Yu., Glotov A.V., Proskuryakov I.S., Podluzhny D.V., Revishvili A.Sh. Texture and CT-features in differentiation of hypervascular pancreatic neuroendocrine tumors from renal cell carcinoma metastases: diagnostic model. Medical Visualization. 2022. https://doi.org/10.24835/1607-0763-1247 (doi)
LIFEx-texture: Meng Dai, Na Wang, Xinming Zhao, Jianyuan Zhang, Zhaoqi Zhang, Jingmian Zhang, Jianfang Wang, Yujing Hu, Yunuan Liu, Xiujuan Zhao and Xiaolin Chen. Value of Presurgical 18F-FDG PET/CT Radiomics for Predicting Mediastinal Lymph Node Metastasis in Patients with Lung Adenocarcinoma. CANCER BIOTHERAPY AND ADIOPHARMACEUTICALS Volume 00, Number 00, 2022 - Mary Ann Liebert, Inc. - DOI: 10.1089/cbr.2022.0038 (doi)
LIFEx-texture: Moataz A.S. Soliman, Linda C. Kelahan, Michael Magnetta, Hatice Savas, Rishi Agrawal, Ryan J. Avery, Pascale Aouad, Benjamin Liu, Yue Xue, Young K. Chae, Riad Salem, Al B. Benson, Vahid Yaghmai and Yuri S. Velichko. A Framework for Harmonization of Radiomics Data for Multicenter Studies and Clinical Trials. JCO Clinical Cancer Informatics. September 21, 2022 (doi)
LIFEx-texture: : Hannequin P, Decroisette C, Kermanach P, Berardi G, Bourbonne V. FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer. Transl Lung Cancer Res 2022;11(10):2051-2063. doi: 10.21037/tlcr-22- 158 (doi)
LIFEx-texture: M. Hatt · A. K. Krizsan · A. Rahmim · T. J. Bradshaw · P. F. Costa · A. Forgacs · R. Seifert · A. Zwanenburg · I. El Naqa · P. E. Kinahan · F. Tixier · A. K. Jha · D. Visvikis. Joint EANM/SNMMI guideline on radiomics in nuclear medicine. European Journal of Nuclear Medicine and Molecular Imaging (doi)
LIFEx-texture: Qian LD, Feng LJ, Zhang SX, Liu J, Ren JL, Liu L, Zhang H, Yang J. 18F-FDG PET/CT imaging of pediatric peripheral neuroblastic tumor: a combined model to predict the International Neuroblastoma Pathology Classification. Quant Imaging Med Surg 2022. (doi)
LIFEx-texture: Kulanthaivelu R, Kohan A, Hinzpeter R, Liu ZA, Hope A, Huang SH, Waldron J, O’Sullivan B, Ortega C, Metser U and Veit-Haibach P (2022) Prognostic value of PET/CT and MR-based baseline radiomics among patients with non-metastatic nasopharyngeal carcinoma. Front. Oncol. 12:952763 (doi)
LIFEx-texture: Hinzpeter, R.; Mirshahvalad, S.A.; Kulanthaivelu, R.; Ortega, C.; Metser, U.; Liu, Z.A.; Elimova, E.; Wong, R.; Yeung, J.; Jang, R.W.: Veit-Haibach, P. Prognostic Value of [18F]-FDG PET/CT Radiomics Combined with Sarcopenia Status among Patients with Advanced Gastroesophageal Cancer. Cancers 2022, 14, 5314 (doi)
LIFEx-texture: Sun R, Lerousseau M, Briend-Diop J, et al. Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy. Journal for ImmunoTherapy of Cancer 2022;10:e004867 (doi)
LIFEx-texture: Adrián A. Negreros‑Osuna, Diego A. Ramírez‑Mendoza, Claudio Casas‑Murillo, Abraham Guerra‑Cepeda, David Hernández‑Barajas, Guillermo Elizondo‑Riojas. Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors. ONCOLOGY LETTERS 24: 446, 2022 (doi)
LIFEx-texture: Roy, S.; Meena, T.; Lim, S.-J. Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine. Diagnostics 2022, 12, 2549 (doi)
LIFEx-texture: Negreros‑Osuna, A. A., Ramírez‑Mendoza, D. A., Casas‑Murillo, C., Guerra‑Cepeda, A., Hernández‑Barajas, D., Elizondo‑Riojas, G."Clinical‑radiomic model in advanced kidney cancer predicts response to tyrosine kinase inhibitors". Oncology Letters 24.6 (2022): 446 (doi)
LIFEx-texture: Feng, C.; Zhou, Z.; Huang, Q.; Meng, X.; Li, Z.; Wang, Y. Radiomics Nomogram Based on High-b-Value Diffusion-Weighted Imaging for Distinguishing the Grade of Bladder Cancer. Life 2022, 12, 1510 (doi)
LIFEx-texture: Ling liang, Haiyan zhang, Haike Lei, Hong Zhou,Yongzhong Wu, and Jiang shen. Diagnosis of Benign and Malignant PulmonaryGround-Glass Nodules Using ComputedTomography Radiomics Parameters. Technology in Cancer Research & Treatment. October 19, 2022 (doi)
LIFEx-texture:18F-FDG PET maximum intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients. Kibrom Berihu Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat. Journal of Nuclear Medicine Jun 2022, jnumed.121.263501(doi)
LIFEx-texture: Yi J, Lei X, Zhang L, et al. The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technology in Cancer Research & Treatment. 2022;21 (doi)
LIFEx-texture: Ahn, H.; Song, G.J.; Jang, S.-H.; Son, M.W.; Lee, H.J.; Lee, M.-S.; Lee, J.-H.; Oh, M.-H.; Jeong, G.C.; Yun, J.H.; et al. Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT. Int. J. Mol. Sci. 2022, 23, 11985 (doi)
LIFEx-texture: Ahn, H.; Song, G.J.; Jang, S.-H.; Son, M.W.; Lee, H.J.; Lee, M.-S.; Lee, J.-H.; Oh, M.-H.; Jeong, G.C.; Yun, J.H.; et al. Predicting the Recurrence of Gastric Cancer Using the Textural Features of Perigastric Adipose Tissue on [18F]FDG PET/CT. Int. J. Mol. Sci. 2022, 23, 11985 (doi)
LIFEx-texture: Teng Y, Chen C, Zhang Y, Xu J. The feasibility of MRI texture analysis in distinguishing glioblastoma, anaplastic astrocytoma and anaplastic oligodendroglioma. Transl Cancer Res 2022 (doi)
LIFEx-texture: Tran, V.T.; Tu, S.-J.; Tseng, J.-R. 68Ga-PSMA-11 PET/CT Features Extracted from Different Radiomic Zones Predict Response to Androgen Deprivation Therapy in Patients with Advanced Prostate Cancer. Cancers 2022, 14, 4838 (doi)
LIFEx-MTV: Durmo R, Donati B, Rebaud L, etal. Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine‐treated, interimPET‐negative classical Hodgkin Lymphoma patients: A radio‐genomic study. Hematol Oncol. 2022;40(4):645‐657 (doi)
LIFEx-texture: Botta, F.; Ferrari, M.; Raimondi, S.; Corso, F.; Lo Presti, G.; Mazzara, S.; Airò Farulla, L.S.; Radice, T.; Vanazzi, A.; Derenzini, E.; et al. The Impact of Segmentation Method and Target Lesion Selection on Radiomic Analysis of 18 F-FDG PET Images in Diffuse Large B-Cell Lymphoma. Appl. Sci. 2022, 12, 9678 (doi)
LIFEx-texture: Saveria Mazzara, Laura Travaini, Francesca Botta, Chiara Granata, Giovanna Motta, Federica Melle, Stefano Fiori, Valentina Tabanelli, Anna Vanazzi, Safaa Ramadan, Tommaso Radice, Sara Raimondi, Giuliana Lo Presti, Mahila E. Ferrari, Barbara Alicja, Jereczek-Fossa, Corrado Tarella, Francesco Ceci, Stefano Pileri, Enrico Derenzini. Gene expression profiling and FDG-PET radiomics uncover radiometabolic signatures associated with outcome in DLBCL American Society of Hematology. ADV-2022-007825R1 (link)
LIFEx-texture: Huang Y-M, Wang T-E, Chen M-J, Lin C-C, Chang C-W, Tai H-C, Hsu S-M and Chen Y-J (2022) Radiomics-based nomogram as predictive model for prognosis of hepatocellular carcinoma with portal vein tumor thrombosis receiving radiotherapy. Front. Oncol. 12:906498. (doi)
LIFEx-texture: Anne-Leen, D., Machaba, S., Alex, M. et al. Principal component analysis of texture features derived from FDG PET images of melanoma lesions. EJNMMI Phys 9, 64 (2022)(doi)
LIFEx-texture: Anne-Leen, D., Machaba, S., Alex, M. et al. Principal component analysis of texture features derived from FDG PET images of melanoma lesions. EJNMMI Phys 9, 64 (2022) (doi)
LIFEx-texture: Hannequin P, Decroisette C, Kermanach P, Berardi G, Bourbonne V. FDG PET and CT radiomics in diagnosis and prognosis of non-small-cell lung cancer. Transl Lung Cancer Res 2022 (doi)
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LIFEx-texture: De Robertis, R., Geraci, L., Tomaiuolo, L. et al. Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis. Radiol med (2022) (doi)
LIFEx-texture: Mori N, Mugikura S, Endo T, Endo H, Oguma Y, Li L, Ito A, Watanabe M, Kanamori M, Tominaga T, Takase K. Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area. Neuroradiology. 2022 Aug 31. doi: 10.1007/s00234-022-03045-1. Epub ahead of print. PMID: 36044063.
LIFEx-texture: Crimì, F.; Zanon, C.; Cabrelle, G.; Luong, K.D.; Albertoni, L.; Bao, Q.R.; Borsetto, M.; Baratella, E.; Capelli, G.; Spolverato, G.; et al. Contrast-Enhanced CT Texture Analysis in Colon Cancer: Correlation with Genetic Markers. Tomography 2022, 8, 2193–2201. (doi)
LIFEx-texture: Toshihiro Nakao, Mitsuo Shimada, Kozo Yoshikawa, Takuya Tokunaga, Masaaki Nishi, Hideya Kashihara, Chie Takasu, Yuma Wada and Toshiaki Yoshimoto. Computed tomography texture analysis for the prediction of lateral pelvic lymph node metastasis of rectal cancer. World Journal of Surgical Oncology (2022) 20:281 (doi)
LIFEx-texture: Sun R, Henry T, Laville A, et al. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? Journal for ImmunoTherapy of Cancer 2022;10:e004848 (doi)
LIFEx-texture: Oliver Barachini, Michaela Schaer, Siroos Mirzaei, Klaus Hergan, Shahin Zandieh. Evaluation of MRI-based radiomic features in heart morphologic variations as a consequence of autoimmune thyroid disorders. Medicine (2022) 101:34 (doi)
LIFEx-texture: Yunus, M.M.; Sabarudin, A.; Karim, M.K.A.; Nohuddin, P.N.E.; Zainal, I.A.; Shamsul, M.S.M.; Yusof, A.K.M. Reproducibility and Repeatability of Coronary Computed Tomography Angiography (CCTA) Image Segmentation in Detecting Atherosclerosis: A Radiomics Study. Diagnostics 2022, 12, 2007 (doi)
LIFEx-texture: Tikhonova, V.S., Karmazanovsky, G.G., Kondratyev, E.V. et al. Radiomics model–based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade. Eur Radiol (2022) (doi)
LIFEx-texture: Pasini, G.; Bini, F.; Russo, G.; Comelli, A.; Marinozzi, F.; Stefano, A. matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model. J. Imaging 2022, 8, 221 (doi)
LIFEx-texture: Ahn, H.; Song, G.J.; Jang, S.-H.; Lee, H.J.; Lee, M.-S.; Lee, J.-H.; Oh, M.-H.; Jeong, G.C.; Lee, S.M.; Lee, J.W. Relationship of FDG PET/CT Textural Features with the Tumor Microenvironment and Recurrence Risks in Patients with Advanced Gastric Cancers. Cancers 2022, 14, 3936 (doi)
LIFEx-texture: Jinling Yi, Xiyao Lei, Lei Zhang, Qiao Zheng, Juebin Jin, Congying Xie, Xiance Jin and Yao Ai. The Influence of Different Ultrasonic Machines on Radiomics Models in Prediction Lymph Node Metastasis for Patients with Cervical Cancer. Technology in Cancer Research & Treatment Volume 21: 1-11 2022 (doi)
LIFEx-texture: Laudicella, R.; Comelli, A.; Liberini, V.; Vento, A.; Stefano, A.; Spataro, A.; Crocè, L.; Baldari, S.; Bambaci, M.; Deandreis, D.; et al. [ 68Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [177Lu]DOTATOC PRRT: The “Theragnomics” Concept. Cancers 2022, 14, 984 (doi)
LIFEx-texture: Xue X-q, Yu W-J, Shi X, Shao X-L and Wang Y-T (2022) 18F-FDG PET/CTbased radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer. Front. Oncol. 12:911168 (doi)
LIFEx-texture: Fiz F, Bottoni G, Bini F, et al. Prognostic value of texture analysis of the primary tumour in high-risk neuroblastoma: An 18F-DOPA PET study. Pediatr Blood Cancer. 2022; e29910 (doi)
LIFEx-texture: Dopamine dysfunction in depression: Takehiro Tamura, Genichi Sugihara, Kyoji Okita, Yohei Mukai, Hiroshi Matsuda, Hiroki Shiwaku, Shunsuke Takagi, Hiromitsu Daisaki, Ukihide Tateishi and Hidehiko Takahashi. Application of texture analysis to dopamine transporter single-photon emission computed tomography imaging. Translational Psychiatry (2022) 12:309 (doi)
LIFEx-MTV: Francesca Tutino, Giulia Puccini, Flavia Linguanti, Benedetta Puccini, Luigi Rigacci, Sofya Kovalchuk, Roberto Sciagrà and Valentina Berti. Baseline metabolic tumor volume calculation using different SUV thresholding methods. 0143-3636 Copyright © 2020 Wolters Kluwer Health (doi)
LIFEx-texture: Anai, K., Hayashida, Y., Ueda, I. et al. The effect of CT texture-based analysis using machine learning approaches on radiologists' performance in differentiating focal-type autoimmune pancreatitis and pancreatic duct carcinoma. Jpn J Radiol (2022) (doi)
LIFEx-texture: Seong-O Shim, Monagi H. Alkinani, Lal Hussain, Wajid Aziz. Feature Ranking Importance from Multimodal Radiomic Texture Features using Machine Learning Paradigm: A Biomarker to Predict the Lung Cancer. Big Data Research 29 (2022) 100331 (doi)
LIFEx-texture-review: Dhivya Venkatesan, Ajay Elangovan, Harysh Winster, Md Younus Pasha, Kripa Susan Abraham, Satheeshkumar J, Sivaprakash P, Ayyadurai Niraikulam, Abilash Valsala Gopalakrishnan, Arul Narayanasamy, Balachandar Vellingiri. Diagnostic and therapeutic approach of artificial intelligence in neuro-oncological diseases. Biosensors and Bioelectronics: X, volume 11, sept 2022, 100188 (doi)
LIFEx-texture-review: Sun R, Henry T, Laville A, et al. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Journal for ImmunoTherapy of Cancer 2022;10:e004848 (doi)
LIFEx-texture: Valladares, A, Beyer, T, Papp, L, Salomon, E, Rausch, I. A multi-modality physical phantom for mimicking tumor heterogeneity patterns in PET/CT and PET/MRI. Med Phys. 2022; 1- 11 (doi)
LIFEx-texture: Bianconi, F.; Palumbo, I.; Fravolini, M.L.; Rondini, M.; Minestrini, M.; Pascoletti, G.; Nuvoli, S.; Spanu, A.; Scialpi, M.; Aristei, C.; et al. Form Factors as Potential Imaging Biomarkers to Differentiate Benign vs. Malignant Lung Lesions on CT Scans. Sensors 2022, 22, 5044 (doi)
LIFEx-texture: Nakajo M, Kawaji K, Nagano H, Jinguji M, Mukai A, Kawabata H, Tani A, Hirahara D, Yamashita M, Yoshiura T. The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment [18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer. Mol Imaging Biol. 2022 Jul 21. (doi)
LIFEx-texture: Yunus, M.M.; Mohamed Yusof, A.K.; Ab Rahman, M.Z.; Koh, X.J.; Sabarudin, A.; Nohuddin, P.N.E.; Ng, K.H.; Kechik, M.M.A.; Karim, M.K.A. Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics 2022, 12, 1660 (doi)
LIFEx-texture: Zhang R, Hohenforst-Schmidt W, Steppert C, Sziklavari Z, Schmidkonz C, Atzinger A, Kuwert T, Klink T, Sterlacci W, Hartmann A, Vieth M, Förster S. Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer. Nuklearmedizin. 2022 Jun 29. English. doi: 10.1055/a-1816-6950. Epub ahead of print. PMID: 35768005. (doi)
LIFEx-texture: Reza Jahangir, Alireza Kamali-Asl and Hossein Arabi. Deep Learning-Based Attenuation and Scatter Correction of Brain 18F-FDG PET Images in the Image Domain. arXiv preprint arXiv:2206.14673, 2022 (link)
LIFEx-texture: Zhang R, Hohenforst-Schmidt W, Steppert C, Sziklavari Z, Schmidkonz C, Atzinger A, Kuwert T, Klink T, Sterlacci W, Hartmann A, Vieth M, Förster S. Standardized 18F-FDG PET/CT radiomic features provide information on PD-L1 expression status in treatment-naïve patients with non-small cell lung cancer. Nuklearmedizin. 2022 Jun 29. English (doi)
LIFEx-texture: Soliter Pulmoner Nodüllerin Sınıflandırılmasında 18F-FDG PET/BT Radyomik Özelliklerine Dayalı Makine Öğrenme Modellerinin Tanısal Performansı. Diagnostic Performance of Machine Learning Models Based on 18F-FDG PET/CT Radiomic Features in the Classification of Solitary Pulmonary Nodules. Mol Imaging Radionucl Ther 2022;31:82-88 (doi)
LIFEx-texture: Nardone V, Reginelli A, Grassi R, Vacca G, Giacobbe G, Angrisani A, Clemente A, Danti G, Correale P, Carbone SF, Pirtoli L, Bianchi L, Vanzulli A, Guida C, Grassi R, Cappabianca S. Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers. 2022; 14(12):3004 (doi)
LIFEx-texture: Nakagawa M, Nakaura T, Yoshida N, Azuma M, Uetani H, Nagayama Y, Kidoh M, Miyamoto T, Yamashita Y, Hirai T. Performance of Machine Learning Methods Based on Multi-Sequence Textural Parameters Using Magnetic Resonance Imaging and Clinical Information to Differentiate Malignant and Benign Soft Tissue Tumors. Acad Radiol. 2022 Jun 17:S1076-6332(22)00255-0 (doi)
LIFEx-texture: Kibrom B. Girum, Louis Rebaud, Anne-Ségolène Cottereau, Michel Meignan, Jérôme Clerc, Laetitia Vercellino, Olivier Casasnovas, Franck Morschhauser, Catherine Thieblemont, Irène Buvat. 18F-FDG PET maximum intensity projections and artificial intelligence: a win-win combination to easily measure prognostic biomarkers in DLBCL patients. Journal of Nuclear Medicine, published on June 16, 2022 (doi)
LIFEx-main: Zhu S, Wang W, Wu W, Lou W, Zeng M, Rao S. MR quantitative 3D shape analysis helps to distinguish mucinous cystic neoplasm from serous oligocystic adenoma. Diagn Interv Radiol. 2022;28(3):193-199 (doi)
LIFEx-texture: De Leo, A., Vara, G., Paccapelo, A. et al. Computerized tomography texture analysis of pheochromocytoma: relationship with hormonal and histopathological data. J Endocrinol Invest (2022) (doi)
LIFEx-MTV:Wallis, D., Soussan, M., Lacroix, M. et al. Correction to: An [18F]FDG‑PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging (2022)(doi)
LIFEx-texture: Xie F, Zheng K, Liu L, Jin X, Fu L and Zhu Z (2022) A Pilot Study of Radiomics Models Combining Multi-Probe and Multi-Modality Images of 68Ga-NOTA-PRGD2 and 18F-FDG PET/CT for Differentiating Benign and Malignant Pulmonary Space-Occupying Lesions. Front. Oncol. 12:877501 (doi)
LIFEx-texture: Ungan, G., Lavandier, AF., Rouanet, J. et al. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J CARS (2022) (doi)
LIFEx-texture: Hayato Tomita, Tsuneo Yamashiro, Gyo Iida, Maho Tsubakimoto, Hidefumi Mimura and Sadayuki Murayama. Radiomics analysis for differentiating of cervical lymphadenopathy between cancer of unknown primary and malignant lymphoma on unenhanced computed tomography. Nagoya J. Med. Sci. 84. 269–285, 2022 (doi)
LIFEx-CalciumQuantitation: Nappi, C.; Megna, R.; Volpe,F.; Ponsiglione, A.; Caiazzo, E.;Piscopo, L.; Mainolfi, C.G.; Vergara,E.; Imbriaco, M.; Klain, M.; et al.Quantification of Coronary ArteryAtherosclerotic Burden and MuscleMass: Exploratory Comparison ofTwo Freely Available SoftwarePrograms. Appl. Sci. 2022, 12, 5468 (doi)
LIFEx-MTV: Durmo, R, Donati, B, Rebaud, L, et al. Prognostic value of lesion dissemination in doxorubicin, bleomycin, vinblastine, and dacarbazine-treated, interimPET-negative classical Hodgkin Lymphoma patients: A radio-genomic study. Hematol Oncol. 2022; 1- 13 (doi)
LIFEx-texture: Zhao H, Su Y, Wang M, Lyu Z, Xu P, Jiao Y, Zhang L, Han W, Tian L and Fu P (2022) The Machine Learning Model for Distinguishing Pathological Subtypes of Non-Small Cell Lung Cancer. Front. Oncol. 12:875761 (doi)
LIFEx-texture: Comte, V., Schmutz, H., Chardin, D. et al. Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. Eur J Nucl Med Mol Imaging (2022) (doi)doi
LIFEx-Main: Müller, L., Kloeckner, R., Mähringer-Kunz, A. et al. Fully automated AI-based splenic segmentation for predicting survival and estimating the risk of hepatic decompensation in TACE patients with HCC. Eur Radiol (2022). (doi)
LIFEx-texture: Zhu S, Wang WT, Wu WC, Lou WH, Zeng MS, Rao SX. MR quantitative 3D shape analysis helps to distinguish mucinous cystic neoplasm from serous oligocystic adenoma. Diagn Interv Radiol 2022; (doi)
LIFEx-texture: Jang, S.J.; Lee, J.W.; Lee, J.-H.; Jo, I.Y.; Lee, S.M. Different Prognostic Values of Dual-Time-Point FDG PET/CT Imaging Features According to Treatment Modality in Patients with Non-Small Cell Lung Cancer. Tomography 2022, 8, 1066–1078 (doi)
LIFEx-COMP: Fiz, Francesco; Bini, Fabiano; Gabriele, Edoardo; Bottoni, Gianluca; Garrè, Maria Luisa; Marinozzi, Franco; Milanaccio, Claudia; Verrico, Antonio; Massollo, Michela; Bosio, Victoria; Lattuada, Marco; Rossi, Andrea; Ramaglia, Antonia; Puntoni, Matteo; Morana, Giovanni, Piccardo, Arnoldo. Role of Dynamic Parameters of 18F-DOPA PET/CT in Pediatric Gliomas, Clinical Nuclear Medicine: March 30, 2022 - Volume - Issue - 10.1097/RLU.0000000000004185 (doi)
LIFEx-texture: Uchida Y, Yoshida S, Arita Y, Shimoda H, Kimura K, Yamada I, Tanaka H, Yokoyama M, Matsuoka Y, Jinzaki M, Fujii Y. Apparent Diffusion Coefficient Map-Based Texture Analysis for the Differentiation of Chromophobe Renal Cell Carcinoma from Renal Oncocytoma. Diagnostics. 2022; 12(4):817 (doi)
LIFEx-texture: Fiz, F., Masci, C., Costa, G. et al. PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival. Eur J Nucl Med Mol Imaging (2022) (doi)
LIFEx-texture: Beleù A, Autelitano D, Geraci L, Aluffi G, Cardobi N, De Robertis R, Martone E, Conci S, Ruzzenente A, D'Onofrio, Mirko. Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. European Journal of Radiology. 18 March 2022, 110250 (doi)
LIFEx-texture: T. Escobar, S. Vauclin, F. Orlhac, C. Nioche, P. Pineau, L. Champion, H. Brisse, I. Buvat. Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns. Medical Physics, 2022 18 March 2022 (doi)
LIFEx-texture: Alongi, P.; Stefano, A.; Comelli, A.; Spataro, A.; Formica, G.; Laudicella, R.; Lanzafame, H.; Panasiti, F.; Longo, C.; Midiri, F.; et al. Artificial Intelligence Applications on Restaging [ 18 F]FDG PET/CT in Metastatic Colorectal Cancer: A Preliminary Report of Morpho-Functional Radiomics Classification for Prediction of Disease Outcome. Appl. Sci. 2022, 12, 2941 (doi)
LIFEx-texture: Jo, J.-H.; Chung, H.W.; So, Y.; Yoo, Y.B.; Park, K.S.; Nam, S.E.; Lee, E.J.; Noh, W.C. FDG PET/CT to Predict Recurrence of Early Breast Invasive Ductal Carcinoma. Diagnostics 2022, 12, 694 (doi)
LIFEx-texture: Annovazzi, A., Ferraresi, V., Rea, S. et al. Correction to: Prognostic value of total metabolic tumour volume and therapy-response assessment by [¹⁸F]FDG PET/CT in patients with metastatic melanoma treated with BRAF/MEK inhibitors. Eur Radiol (2022) (doi)
LIFEx-texture: Cattell, R., Ying, J., Lei, L. et al. Preoperative prediction of lymph node metastasis using deep learning-based features. Vis. Comput. Ind. Biomed. Art 5, 8 (2022) (doi)
LIFEx-texture: Orkun Sarıoğlu, Fatma Ceren Sarıoğlu, Bahar Konuralp Atakul, Deniz Öztekin, Özgür Öztekin. The Role of Fetal MRI-based Texture Analysis in Differentiating Congenital Pulmonary Airway Malformation and Pulmonary Sequestration. J Pediatr Res 2022;9(1):52-59 (doi)
LIFEx-texture: Cabini, R.F., Brero, F., Lancia, A. et al. Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center “Blue Sky Radiomics” study on stage III unresectable NSCLC. Insights Imaging 13, 38 (2022) (doi)
LIFEx-texture: Hasan Önner, Nazım Coşkun, Mustafa Erol, Meryem İlkay Eren Karanis. The Role of Histogram-Based Textural Analysis of 18F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma. Mol Imaging Radionucl Ther. 2022 Feb; 31(1): 33–41 (doi)
LIFEx-MTV: Ezgi Başak Erdoğan, Mehmet Aydın. Investigation of Added Value of Imaging Performed in a Prone Position to Standard 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Imaging for Staging in Patients with Breast Cancer. Mol Imaging Radionucl Ther. 2022 Feb; 31(1): 23–32 (doi)
LIFEx-main: Ying, P., Chen, J., Ye, Y. et al. Adipose tissue is a predictor of 30-days mortality in patients with bloodstream infection caused by carbapenem-resistant Klebsiella pneumoniae. BMC Infect Dis 22, 173 (2022) (doi)
LIFEx-texture: Hasan Önner, Nazım Coşkun, Mustafa Erol, Meryem İlkay Eren Karanis. The Role of Histogram-Based Textural Analysis of 18 F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma. Mol Imaging Radionucl Ther 2022;31:33-41 (doi)
LIFEx-MTV: Gaia Ninatti, Martina Sollini, Beatrice Bono, Noemi Gozzi, Daniil Fedorov, Lidija Antunovic, Fabrizia Gelardi, Pierina Navarria, Letterio S. Politi, Federico Pessina, Arturo Chiti. Preoperative [11C]methionine PET to personalize treatment decisions in patients with lower-grade gliomas. Neuro-Oncology, 2022 (doi)
LIFEx-texture: Laudicella, R.; Comelli, A.; Liberini, V.; Vento, A.; Stefano, A.; Spataro, A.; Crocè, L.; Baldari, S.; Bambaci, M.; Deandreis, D.; et al. [ 68Ga]DOTATOC PET/CT Radiomics to Predict the Response in GEP-NETs Undergoing [177Lu]DOTATOC PRRT: The “Theragnomics” Concept. Cancers 2022, 14, 984 (doi)
LIFEx-Main: Lee, S.M.; Lee, J.W.; Kim, W.C.; Min, C.K.; Kim, E.S.; Jo, I.Y. Effects of Tumor-Rib Distance and Dose-Dependent Rib Volume on Radiation-Induced Rib Fractures in Patients with Breast Cancer. J. Pers. Med. 2022, 12, 240. (doi)
LIFEx-MTV: Jiang, C., Chen, K., Teng, Y. et al. Deep learning–based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images. Eur Radiol (2022) (doi)
LIFEx-texture: Zhang, L., Zhao, H., Jiang, H. et al. Correction to: ¹⁸F‑FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (2022) (doi)
LIFEx-Main: Arnaud Beddok, Leslie Guzene, Alexandre Coutte, David Thomson, Sue Yom, Valentin Calugaru, Eivind Blais, Olivier Gilliot, Séverine Racadot, Yoann Pointreau, June Corry, Kenneth Jensen, Sandro Porceddu, Nazim Khalladi, Vianney Bastit, Audrey Lasne-Cardon, Pierre-Yves Marcy, Florent Carsuzaa, Christophe Nioche, Jean Bourhis, Julia Salleron, Thariat Juliette & on behalf of the GORTEC (2022) International assessment of interobserver reproducibility of flap delineation in head and neck carcinoma, Acta Oncologica (doi)
LIFEx-texture: Hasan Önner, Nazım Coşkun, Mustafa Erol, Meryem İlkay Eren Karanis. The Role of Histogram-Based Textural Analysis of 18 F-FDG PET/CT in Evaluating Tumor Heterogeneity and Predicting the Prognosis of Invasive Lung Adenocarcinoma. Mol Imaging Radionucl Ther 2022;31:33-41 DOI:10.4274/mirt.galenos.2021.79037 (doi)
LIFEx-texture: Тихонова В.С., Груздев И.С., Кондратьев Е.В., Михайлюк К.А., Кармазановский Г.Г. Differential diagnosis of pseudotumorous pancreatitis and pancreatic ductal adenocarcinoma: characteristics of contrast-enhanced CT and texture analysis. medical imaging. (doi)
LIFEx-texture: Zhou Y, Li J, Zhang X, Jia T, Zhang B, Dai N, Sang S and Deng S (2022) Prognostic Value of Radiomic Features of ¹⁸F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells. Front. Oncol. 12:834288. doi: 10.3389/fonc.2022.834288 (doi)
LIFEx-texture: Flaus, A.; Habouzit, V.; de Leiris, N.; Vuillez, J.-P.; Leccia, M.-T.; Simonson, M.; Perrot, J.-L.; Cachin, F.; Prevot, N. Outcome Prediction at Patient Level Derived from Pre-Treatment 18F-FDG PET Due to Machine Learning in Metastatic Melanoma Treated with Anti-PD1 Treatment. Diagnostics 2022, 12, 388 (doi)
LIFEx-texture: Dondi, F.; Pasinetti, N.; Gatta, R.; Albano, D.; Giubbini, R.; Bertagna, F. Comparison between Two Different Scanners for the Evaluation of the Role of 18 F-FDG PET/CT Semiquantitative Parameters and Radiomics Features in the Prediction of Final Diagnosis of Thyroid Incidentalomas. J. Clin. Med.2022, 11, 615. (doi)
LIFEx-MTV-texture: Pedraza, S., Seiffert, A.P., Sarandeses, P. et al. The value of metabolic parameters and textural analysis in predicting prognosis in locally advanced cervical cancer treated with chemoradiotherapy. Strahlenther Onkol (2022) (doi)
LIFEx-texture: Han, E.J.; O, J.H.; Yoon, H.; Ha, S.; Yoo, I.R.; Min, J.W.; Choi, J.-I.; Choi, B.-O.; Park, G.; Lee, H.H.; Jeon, Y.-W.; Min, G.-J.; Cho, S.-G., on behalf of Catholic University Lymphoma Group. Comparison of FDG PET/CT and Bone Marrow Biopsy Results in Patients with Diffuse Large B Cell Lymphoma with Subgroup Analysis of PET Radiomics. Diagnostics 2022, 12, 222 (doi)

About LIFEx (but not verified):

LIFEx-texture: Erol, M., Önner, H. & Küçükosmanoğlu, İ. Association of Fluorodeoxyglucose Positron Emission Tomography Radiomics Features with Clinicopathological Factors and Prognosis in Lung Squamous Cell Cancer. Nucl Med Mol Imaging (2022) (doi)
LIFEx-texture: Iafrate, F., Ciccarelli, F., Masci, G.M. et al. Predictive role of diffusion-weighted MRI in the assessment of response to total neoadjuvant therapy in locally advanced rectal cancer. Eur Radiol (2022) (doi)
LIFEx-texture: Agüloğlu N, Aksu A, Akyol M, Katgı N, Doksöz TÇ. Importance of pretreatment 18F-FDG PET/CT Texture analysis in predicting EGFR and ALK mutation in patients with non-small cell lung cancer. Nuklearmedizin. Nuclear medicine [Nuklearmedizin] 2022 Aug 17 (doi)
LIFEx-texture: Tikhonova VS, Karmazanovsky GG, Kondratyev EV, Gruzdev IS, Mikhaylyuk KA, Sinelnikov MY, Revishvili AS. Radiomics model-based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade. Eur Radiol. 2022 Aug 20. doi: 10.1007/s00330-022-09046-1. Epub ahead of print. PMID: 35986774 (doi)
LIFEx-texture: Kirienko M. (2022) Imaging Biomarkers: Radiomics and the Use of Artificial Intelligence in Nuclear Oncology. In: Volterrani D., Erba P.A., Strauss H.W., Mariani G., Larson S.M. (eds) Nuclear Oncology. Springer, Cham (doi)
LIFEx-texture: Oğuz Lafcı, Pınar Celepli, Pelin Seher Öztekinn Pınar Nercis Koşa. DCE-MRI Radiomics Analysis in Differentiating Luminal A and Luminal B Breast Cancer Molecular Subtypes. Academic Radiology. Published:May 17, 2022 (doi)
LIFEx-texture: DCE-MRI Radiomics Analysis in Differentiating Luminal A and Luminal B Breast Cancer Molecular Subtypes. Oğuz Lafcı, Pınar Celepli, Pelin Seher Öztekin, Pınar Nercis Koşar. Academic Radiology, 2022 (17 May 2022) (doi)
LIFEx-main: Park, J., Kang, S.K., Hwang, D. et al. Automatic Lung Cancer Segmentation in [¹⁸F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nucl Med Mol Imaging (2022) (doi)
LIFEx-texture: Anconina R, Ortega C, Metser U, Liu ZA, Elimova E, Allen M, Darling GE, Wong R, Taylor K, Yeung J, Chen EX, Swallow CJ, Jang RW, Veit-Haibach P. Combined 18F-FDG PET/CT Radiomics and Sarcopenia Score in Predicting Relapse-Free Survival and Overall Survival in Patients With Esophagogastric Cancer. Clin Nucl Med. 2022 May 11. (doi)
LIFEx-texture: Anconina R, Ortega C, Metser U, Liu ZA, Elimova E, Allen M, Darling GE, Wong R, Taylor K, Yeung J, Chen EX, Swallow CJ, Jang RW, Veit-Haibach P. Combined 18F-FDG PET/CT Radiomics and Sarcopenia Score in Predicting Relapse-Free Survival and Overall Survival in Patients With Esophagogastric Cancer. Clin Nucl Med. 2022 May 11. (doi)
LIFEx-texture: Aydos, Uğuray; Sever, Tayyibe; Vural, Özge; Topuz Türkcan, Büşra; Okur, Arzu; Akdemir, Ümit Özgür; Poyraz, Aylar; Pinarli, Faruk Güçlü; Atay, Lütfiye Özlem; Karadeniz, Ceyda. Prognostic value of fluorodeoxyglucose positron emission tomography derived metabolic parameters and textural features in pediatric sarcoma, Nuclear Medicine Communications: May 04, 2022 - Volume - Issue - 10.1097/MNM.0000000000001577 (doi)
LIFEx-main: Park, J., Kang, S.K., Hwang, D. et al. Automatic Lung Cancer Segmentation in [¹⁸F]FDG PET/CT Using a Two-Stage Deep Learning Approach. Nucl Med Mol Imaging (2022) (doi)
LIFEx-texture: Karahan Şen, Nazli Pinar; Alataş, Özkan; Gülcü, Aytaç; Özdoğan, Özhan; Derebek, Erkan; Çapa Kaya, Gamze. The role of volumetric and textural analysis of pretreatment 18F-fluorodeoxyglucose PET/computerized tomography images in predicting complete response to transarterial radioembolization in hepatocellular cancer, Nuclear Medicine Communications: May 04, 2022 - Volume - Issue - 10.1097/MNM.0000000000001572 (doi)
LIFEx-texture: M. M. Yunus, A. Sabarudin, N. I. Hamid, A. K. M. Yusof, P. N. E. Nohuddin and M. K. A. Karim, "Automated Classification of Atherosclerosis in Coronary Computed Tomography Angiography Images Based on Radiomics Study Using Automatic Machine Learning," 2022 International Conference on Electronics and Renewable Systems (ICEARS), 2022, pp. 1895-1903 (doi)
LIFEx-texture: Matsumoto, S., Arita, Y., Yoshida, S. et al. Utility of radiomics features of diffusion-weighted magnetic resonance imaging for differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma: model development and external validation. Abdom Radiol (2022) (doi)
LIFEx-texture: M. M. Yunus, A. Sabarudin, N. I. Hamid, A. K. M. Yusof, P. N. E. Nohuddin and M. K. A. Karim, "Automated Classification of Atherosclerosis in Coronary Computed Tomography Angiography Images Based on Radiomics Study Using Automatic Machine Learning," 2022 International Conference on Electronics and Renewable Systems (ICEARS), 2022, pp. 1895-1903, doi: 10.1109/ICEARS53579.2022.9752423 (doi)
LIFEx-texture: Okuda K, Saito H, Yamashita S, et al. Beads phantom for evaluating heterogeneity of SUV on 18 F-FDG PET images. Annals of nuclear medicine. April 2022 (doi)
LIFEx-texture: Sahin, S., Yildiz, G., Oguz, S.H. et al. Discrimination between non-functioning pituitary adenomas and hypophysitis using machine learning methods based on magnetic resonance imaging‑derived texture features. Pituitary (2022) (doi)
LIFEx-texture: Eleonora D'Arnese, Guido Walter Di Donato, Emanuele Del Sozzo, Martina Sollini, Donatella Sciuto, Marco Domenico Santambrogio. On the Automation of Radiomics-based Identification and Characterization of NSCLC. IEEE Journal of Biomedical and Health Informatics. 07 March 2022 (doi)
LIFEx-texture: Hideyuki orikai, Masanori Inoue, Jitsuro Tsukada, Koji Togawa, Yosuke Yamamoto, Manabu Hase, Masashi Tamura, Nobutake Ito, Shigeyoshi Soga, Seishi Nakatsuka, Masahiro Jinzaki, Comparison of foaming properties between Shirasu porous glass membrane device and Tessari’s three-way stopcock techniques for polidocanol and ethanolamine oleate foam production: A Benchtop Study. Journal of Vascular and Interventional Radiology 2022, 022/02/02, SN - 1051-0443 (doi)
LIFEx-texture: Masci, G.M., Ciccarelli, F., Mattei, F.I. et al. Role of CT texture analysis for predicting peritoneal metastases in patients with gastric cancer. Radiol med (2022)(doi)
LIFEx-texture: Kelahan, L.C., Kim, D., Soliman, M. et al. Role of hepatic metastatic lesion size on inter-reader reproducibility of CT-based radiomics features. Eur Radiol (2022) (doi)
LIFEx-texture: Franzese, C., Cozzi, L., Badalamenti, M. et al. Radiomics-based prognosis classification for high-risk prostate cancer treated with radiotherapy. Strahlenther Onkol (2022) (doi)

About LIFEx (Failure to comply with the LIFEx license agreement):

Yang, Xiaozhen; Yuan, Chunwang; Zhang, Yinghua; Li, Kang; Wang, Zhenchang. Predicting hepatocellular carcinoma early recurrence after ablation based on magnetic resonance imaging radiomics nomogram. Medicine 101(52):p e32584, December 30, 2022. | DOI: 10.1097/MD.0000000000032584 (doi)
LIFEx-viewer: Dondi, F.; Gatta, R.; Albano, D.; Bellini, P.; Camoni, L.; Treglia, G.; Bertagna, F. Role of Radiomics Features and Machine Learning for the Histological Classification of Stage I and Stage II NSCLC at [18 F]FDG PET/CT: A Comparison between Two PET/CT Scanners. J. Clin. Med. 2023, 12, 255. https://doi.org/10.3390/jcm12010255 (doi)
LIFEx-texture: Mahmoud, H.A., Oteify, W., Elkhayat, H. et al. Volumetric parameters of the primary tumor and whole-body tumor burden derived from baseline 18F-FDG PET/CT can predict overall survival in non-small cell lung cancer patients: initial results from a single institution. European J Hybrid Imaging 6, 37 (2022). https://doi.org/10.1186/s41824-022-00158-x (doi)
LIFEx-texture: Li, M., Yao, H., Zhang, P. et al. Development and validation of a [¹⁸F]FDG PET/CT-based radiomics nomogram to predict the prognostic risk of pretreatment diffuse large B cell lymphoma patients. Eur Radiol (2022). https://doi.org/10.1007/s00330-022-09301-5 (doi)
Chae Hong Lim, Young Wha Koh, Seung Hyup Hyun and Su Jin Lee. A Machine Learning Approach Using PET/CT-based
Radiomics for Prediction of PD-L1 Expression. ANTICANCER RESEARCH 42: 5875-5884 (2022) (doi)
in Non-small Cell Lung Cancer
Rui-Fang Wang, Yan-Peng Li, Han-Yue Zhang, Sha-Sha Xu, Zhuo Wang, Xing-Min Han, Bao-Ping Liu. Sleep benefit in patients with Parkinson’s disease is associated with the dopamine transporter expression in putamen, Brain Research,
2022, 148173, ISSN 0006-8993 (doi)
LIFEx-texture: Jing Jing Liu, Yan Zhou Wang, Na Chen, Qian Nan Wang, Li Liu, Ying Li, Ling Lei and Yi Wu. Hypothesis generation: Quantitative research to levatorani muscle injury based on MRI texture analysis. J. Obstet. Gynaecol. Res. 2022 (doi)
LIFEx-texture: Sanaat, A., Akhavanalaf, A., Shiri, I., Salimi, Y., Arabi, H., & Zaidi, H. (2022). Deep-TOF-PET: Deep learning-guided generation of time-of-flight from non-TOF brain PET images in the image and projection domains. Human Brain Mapping, 1–12 (doi)
LIFEx-texture: Tsai, Y.-L.; Chen, S.-W.; Kao, C.-H.; Cheng, D.-C. Neck Lymph Node Recurrence in HNC Patients Might Be Predicted before Radiotherapy Using Radiomics Extracted from CT Images and XGBoost Algorithm. J. Pers. Med. 2022, 12, 1377 (doi)
LIFEx-texture: Wang Q, Xu S, ZhangG, et al. Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis. JApplClinMedPhys.2022;e13759 (doi)
LIFEx-texture: Amandine Crombé, Mathilde Lafon, Stéphanie Nougaret, Michèle Kind, Sophie Cousin. Ranking the most influential predictors of CT-based radiomics feature values in metastatic lung adenocarcinoma. European Journal of Radiology 155 (2022) 110472 (doi)
LIFEx-texture: Kenta Anai,Yoshiko Hayashida, Issei Ueda, Eri Hozuki, Yuuta Yoshimatsu, Jun Tsukamoto, Toshihiko Hamamura, Norihiro Onari, Takatoshi Aoki, Yukunori Korogi. The effect of CT texture‑based analysis using machine learning approaches on radiologists' performance in differentiating focal‑type autoimmune pancreatitis and pancreatic duct carcinoma.

Japanese Journal of Radiology, 2022 (doi)
LIFEx-texture: Tumay Bekci, Ismet Mirac Cakir, Serdar Aslan. Differentiation of affected and nonaffected ovaries in ovarian torsion with magnetic resonance imaging texture analysis. Rev. Assoc. Med. Bras. vol.68 no.5 São Paulo May 2022 Epub May 13, 2022 (doi)
LIFEx-texture: Sparacia, G., Parla, G., Cannella, R. et al. Brain magnetic resonance imaging radiomics features associated with hepatic encephalopathy in adult cirrhotic patients. Neuroradiology (2022). (doi)
LIFEx-texture: E. Abenavoli, F. Linguanti, M. Barbetti, F. Mungai, V. Miele, L. Nassi, B. Puccini, I. Romano, R. Santi, A. Passeri, R. Sciagrà, C. Talamonti, A. M Vannucchi, V. Berti. Machine-Learning Approach Using FDG-PET-based Radiomics in the Characterization of Mediastinal Bulky lymphomas. Research Square, February 9th, 2022 (doi)
LIFEx-texture: PA Erba, M Sollini, R Zanca, L Cavinato, A Ragni, D Ten Hove, AWJM Glaudemans, MN Pizzi, A Roque, F Ieva, RHJA Slart, [18F]FDG-PET/CT radiomics in patients suspected of infective endocarditis, European Heart Journal - Cardiovascular Imaging, Volume 23, Issue Supplement_1, February 2022, jeab289.443, (doi)
LIFEx-texture: Mine Araz, Çiğdem Soydal, Pınar Gündüz, Ayça Kırmızı, Batuhan Bakırarar, Serpil Dizbay Sak, Elgin Özkan, Can Radiomics Analyses in 18 F-FDG PET/CT Images of Primary Breast Carcinoma Predict Hormone Receptor Status? Mol Imaging Radionucl Ther 2022;31:49-56 DOI:10.4274/mirt.galenos.2022.59140 (doi)

Review:

LIFEx-texture: Mirestean C.C., Iancu R.I. Iancu D.T. Delta-radiomics Entropy Based on Tumor Heterogeneity Concept-Response Predictor to Irradiation for Unresectable/recurrent Glioblastoma. Modern Medicine | 2022, Vol. 29, No. 4. (link)
Hung, K.F.; Ai, Q.Y.H.; Wong, L.M.; Yeung, A.W.K.; Li, D.T.S.; Leung, Y.Y. Current Applications of Deep Learning and Radiomics on CT and CBCT for Maxillofacial Diseases. Diagnostics 2023, 13, 110. https://doi.org/ 10.3390/diagnostics13010110 (doi)
LIFEx-texture: Sotiris Raptis, Christos Ilioudis, Vasiliki Softa and Kiki Theodorou. Artificial Intelligence in Predicting Treatment Response in Non-Small-Cell Lung Cancer (NSCLC) BioMedical Journal of Scientific & Technical Research, Dec 2022 DOI: 10.26717/BJSTR.2022.47.007497 (doi)
LIFEx-texture:Li, S., Zhou, B. A review of radiomics and genomics applications in cancers: the way towards precision medicine. Radiat Oncol 17, 217 (2022). https://doi.org/10.1186/s13014-022-02192-2 (doi)

Thesis:

LIFEx-texture: Negreros Osuna A.A. Análisis de la textura tomográfica en tumores renales en etapa avanzada como biomarcador para la predicción de respuesta al tratamiento sistémico con inhibidores de la tirosina quinasa. Doctor en medicina, Nov 2022. http://eprints.uanl.mx/24772/1/1080328720.pdf

LIFEx-viewer: Martinez- Perez R, Kortz MW, Ung TH, Rayo N, Lagares A, Cepeda S. Third Ventricle Volume Predicts Functional Outcome in Chronic Subdural Hematoma. Acta Neurol Scand. 2021;00:1– 8 (doi)
LIFEx-texture: Urraro F, Nardone V, Reginelli A, Varelli C, Angrisani A, Patanè V, D’Ambrosio L, Roccatagliata P, Russo GM, Gallo L, De Chiara M, Altucci L and Cappabianca S (2021) MRI Radiomics in Prostate Cancer: A Reliability Study. Front. Oncol. 11:805137 (doi)
LIFEx-texture: Guilherme D. Kolinger, David Vállez García, Gerbrand Maria Kramer, Virginie Frings, Gerben J.C. Zwezerijnen, Egbert F. Smit, Adrianus Johannes de Langen, Irène Buvat and Ronald Boellaard. Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics. Journal of Nuclear Medicine, published on December 21, 2021 (doi)
LIFEx-texture: Fatma Ceren Sarioglu, Orkun Sarioglu, Handan Guleryuz, Burak Deliloglu, Funda Tuzun, Nuray Duman, Hasan Ozkan. The role of MRI-based texture analysis to predict the severity of brain injury in neonates with perinatal asphyxia. British Institute of Radiology. Published Online:17 Dec 2021 (doi)
LIFEx-texture: Li J, Ge S, Sang S, Hu C and Deng S (2021) Evaluation of PD-L1 Expression Level in Patients With Non-Small Cell Lung Cancer by 18F-FDG PET/CT Radiomics and Clinicopathological Characteristics. Front. Oncol 11:789014 (doi)
Russo, G.; Stefano, A.; Alongi, P.; Comelli, A.; Catalfamo, B.; Mantarro, C.; Longo, C.; Altieri, R.; Certo, F.; Cosentino, S.; et al. Feasibility on the Use of Radiomics Features of 11[C]-MET PET/CT in Central Nervous System Tumours: Preliminary Results on Potential Grading Discrimination Using a Machine Learning Model. Curr. Oncol. 2021, 28, 5318–5331 (doi)
LIFEx-texture: Ahrari, S.; Zaragori, T.; Rozenblum, L.; Oster, J.; Imbert, L.; Kas, A.; Verger, A. Relevance of Dynamic 18F-DOPA PET Radiomics for Differentiation of High-Grade Glioma Progression from TreatmentRelated Changes. Biomedicines 2021, 9, 1924 (doi)
LIFEx-texture: Julie Faudemer, Nicolas Aide, Anne‑Claire Gac, Ghandi Damaj, Jean‑Pierre Vilque, Charline Lasnon. Diagnostic value of baseline 18 FDG PET/CT skeletal textural features in follicular lymphoma. Scientific Reports (2021) 11:23812 (doi)
LIFEx-viewer: Martina Sollini, Francesco Bartoli, Lara Cavinato, Francesca Ieva, Alessandra Ragni, Andrea Marciano, Roberta Zanca, Luca Galli, Fabiola Paiar, Francesco Pasqualetti and Paola Anna Erba. [18 F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Research (2021) 11:119 (doi)
LIFEx-texture: Sollini, M., Bartoli, F., Cavinato, L. et al. [¹⁸F]FMCH PET/CT biomarkers and similarity analysis to refine the definition of oligometastatic prostate cancer. EJNMMI Res 11, 119 (2021) (doi)
LIFEx-texture: Guilherme D. Kolinger, David Vállez García, Gerbrand M. Kramer, Virginie Frings, Gerben J.C. Zwezerijnen, Egbert F. Smit, Adrianus J. de Langen, Irène Buvat, and Ronald Boellaard. Use-cases for 18F-FDG PET radiomics based on uptake time dependence in non-small cell lung cancer. Journal of Nuclear Medicine, SNMMI, 2021 (doi)
LIFEx-texture:Yeye Zhou, Yuchun Zhu, Zhiqiang Chen, Jihui Li, Shibiao Sang and Shengming Deng. Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes. Contrast Media & Molecular Imaging Volume 2021, Article ID 6347404, 8 pages (doi)
LIFEx-MTV-texture: M Kimura, I Kato, K Ishibashi, K Hashimoto, H Tsuji, Y Sone, M Umemura, T Nagao. Texture analysis of 18F-FDG PET images for the detection of cervical lymph node metastases in patients with oral squamous cell carcinoma. Advances in Oral and Maxillofacial Surgery. Volume 5, January–March 2022, 100228 (doi)
LIFEx-MTV-texture: Hyein Ahn, Jeong Won Lee, Si-Hyong Jang, Hyun Ju Lee, Ji-Hye Lee, Mee-Hye Oh, Sang Mi Lee,
Prognostic significance of imaging features of peritumoral adipose tissue in FDG PET/CT of patients with colorectal cancer,
European Journal of Radiology, Volume 145, 2021 (doi)
LIFEx-texture: Kotaro Ito, Hirotaka Muraoka, Naohisa Hirahara, Eri Sawada, Shoya Hirohata, Kohei Otsuka, Shunya Okada, Takashi Kaneda. Quantitative assessment of mandibular bone marrow using computed tomography texture analysis for detect stage 0 medication-related osteonecrosis of the jaw. European Journal of Radiology, Volume 145, 110030, December 01, 2021 (doi)
LIFEx-MTV-texture: Xue Beihui, Jiang Jia, Chen Lei, Wu Sunjie, Zheng Xuan, Zheng Xiangwu, Tang Kun. Development and Validation of a Radiomics Model Based on 18F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front. Oncol., 26 October 2021 (doi)
LIFEx-viewer: Stefano, A.; Leal, A.; Richiusa, S.; Trang, P.; Comelli, A.; Benfante, V.; Cosentino, S.; Sabini, M.G.; Tuttolomondo, A.; Altieri, R.;et al. Robustness of PET Radiomics Features: Impact of Co-Registration with MRI. Appl. Sci. 2021, 11, 10170 (doi)
LIFEx-texture: Mendes, B.; Domingues, I.; Silva, A.; Santos, J. Prostate Cancer Aggressiveness Prediction Using CT Images. Life 2021, 11, 1164 (doi)
LIFEx-viewer: Lee, JH., Kim, S., Lee, H.S. et al. Different prognostic impact of glucose uptake in visceral adipose tissue according to sex in patients with colorectal cancer. Sci Rep 11, 21556 (2021) (doi)
LIFEx-texture: Kim, J.; Jeong, S.Y.; Kim, B.-C.; Byun, B.-H.; Lim, I.; Kong, C.-B.; Song, W.S.; Lim, S.M.; Woo, S.-K. Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images. Diagnostics 2021, 11, 1976 (doi)
LIFEx-texture: Kim, J.; Jeong, S.Y.; Kim, B.-C.; Byun, B.-H.; Lim, I.; Kong, C.-B.; Song, W.S.; Lim, S.M.; Woo, S.-K. Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Using Convolutional Neural Network of Tumor Center 18F-FDG PET Images. Diagnostics 2021, 11, 1976 (doi)
LIFEx-texture: Xue B, Jiang J, Chen L, Wu S, Zheng X, Zheng X and Tang K (2021) Development and Validation of a Radiomics Model Based on ¹⁸F-FDG PET of Primary Gastric Cancer for Predicting Peritoneal Metastasis. Front. Oncol. 11:740111 (doi)
LIFEx-texture: Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim. Artificial Intelligence-Based Detection, Classification and Prediction/Prognosis in PET Imaging: Towards Radiophenomics. PET Clinics, Volume 17, Issue 1, 2021 (doi)
LIFEx-texture: Zhao Y, Chen R, Zhang T, Chen C, Muhelisa M, Huang J, Xu Y and Ma X (2021) MRI-Based Machine Learning in Differentiation Between Benign and Malignant Breast Lesions. Front. Oncol. 11:552634 (doi)
LIFEx-MTV: Hande Melike Bülbül, Ogün Bülbül, Sülen Sarıoğlu, Özhan Özdoğan, Ersoy Doğan, Nuri Karabay. Relationships Between DCE-MRI, DWI, and 18 F-FDG PET/CT Parameters with Tumor Grade and Stage in Patients with Head and Neck Squamous Cell Carcinoma. Mol Imaging Radionucl Ther 2021;30:177-186 (doi)
LIFEx-texture: Lee JW, Kim SY, Han SW, Lee JE, Hong SH, Lee SM, Jo IY. Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. Journal of Personalized Medicine. 2021; 11(10):1029 (doi)
LIFEx-PT-COMP: Zaragori T, Doyen M, Rech F, Blonski M, Taillandier L, Imbert L and Verger A (2021) Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient? Front. Oncol. 11:735257. doi: 10.3389/fonc.2021.735257(doi)
LIFEx-texture: Maëlle Dade, Marine Giry, Giulia Berzero, Marion Benazra, Gilles Huberfeld, Delphine Leclercq, Vincent Navarro, Jean-Yves Delattre, Dimitri Psimaras, Agusti Alentorn. Quantitative brain imaging analysis of neurological syndromes associated with anti-GAD antibodies. NeuroImage: Clinical, Volume 32, 2021, 102826, ISSN 2213-1582 (doi)
LIFEx-texture: valuation of 68Ga-PSMA PET/CT with Volumetric Parameters for Staging of Prostate Cancer Patients: Erratum, Nuclear Medicine Communications: October 2021 - Volume 42 - Issue 10 - p 1186 (doi)
LIFEx-texture: Flaus, A., Habouzit, V., De Leiris, N. et al. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep 11, 18795 (2021) (doi)
LIFEx-texture: Shozo Yamashita, Koichi Okuda, Tetsu Nakaichi, Haruki Yamamoto and Kunihiko Yokoyama.Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET. Journal of Nuclear Medicine Technology March 2021, 49 (1) 58-64 (doi)
LIFEx-texture: Mengmeng Yan, Weidong Wang. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Science Progress
2021, Vol. 104(1) 1-10 (doi)
LIFEx-texture: Zhang, L., Zhao, H., Jiang, H. et al. 18F-FDG texture analysis predicts the pathological Fuhrman nuclear grade of clear cell renal cell carcinoma. Abdom Radiol (2021) (doi)
LIFEx-texture: Xu, Hanyue; Zou, Xiuhe; Zhao, Yunuo; Zhang, Tao; Tang, Youyin; Zheng, Aiping; Zhou, Xianghong; Ma, Xuelei. Differentiation of Intrahepatic Cholangiocarcinoma and Hepatic Lymphoma Based on Radiomics and Machine Learning in Contrast-Enhanced Computer Tomography. Technol Cancer Res Treat ; 20: 15330338211039125, 2021 (doi)
LIFEx-viewer: Wallis, D., Soussan, M., Lacroix, M. et al. An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging (2021). (doi)
LIFEx-texture: Zhong-Wei Chen, Kun Tang, You-Fan Zhao, Yang-Zong Chen, Liang-Jie Tang, Gang Li, Ou-Yang Huang, Xiao-Dong Wang, Giovanni Targher, Christopher D. Byrne, Xiang-Wu Zheng and Ming-Hua Zheng. Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study. Int. J. Med. Sci. 2021, Vol 18(16): 3624-3630 (doi)
LIFEx-texture: Yang, N., Liu, F., Li, C. et al. Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images. Sci Rep 11, 17885 (2021) (doi)
LIFEx-texture: Chong, G.O.; Park, S.-H.; Jeong, S.Y.; Kim, S.J.; Park, N.J.-Y.; Lee, Y.H.; Lee, S.-W.; Hong, D.G.; Park, J.Y.; Han, H.S. Prediction Model for Tumor Budding Status Using the Radiomic Features of F-18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Cervical Cancer. Diagnostics 2021, 11, 1517 (doi)
LIFEx-texture: Yuki Arita and Soichiro Yoshida and Thomas C. Kwee and Hirotaka Akita and Shigeo and Yuki Iwaita and Kiyoko Mukai and Shunya Matsumoto and Ryo and Ryota and Ryuichi Mizuno and Yasuhisa Fujii and Mototsugu Oya and Masahiro Jinzaki. Diagnostic Value of Texture Analysis of Apparent Diffusion Coefficient Maps for Differentiating Fat-Poor Angiomyolipoma from Non-Clear-Cell Renal Cell Carcinoma. European Journal of Radiology. p109895, 0720-048X; 2021 (doi)
LIFEx-texture: Beaumont, H., Iannessi, A., Cucchi, J.M. et al. Intra-scan inter-tissue variability can help harmonize radiomics features in CT. Eur Radiol (2021) (doi)
LIFEx-texture: Ren, H., Mori, N., Mugikura, S. et al. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2-weighted imaging. Abdom Radiol (2021) (doi)
LIFEx-texture: Tang, Y., Zhang, T., Zhou, X. et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Onc 19, 45 (2021) (doi)
LIFEx-texture: Lafata, K.J., Wang, Y., Konkel, B. et al. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (2021) (doi)
LIFEx-texture: Su C-W, Lee J-C, Chang Y-F, et al. Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer. Tumori Journal. August 2021 (doi)
LIFEx-texture: Javier González-Viguera, Gabriel Reynés-Llompart, Alicia Lozano. Outcomes and computed tomography radiomic features extraction in soft tissue sarcomas treated with neoadjuvant radiation therapy. Reports of practical oncology and radiotherapy. 2021-08-13 (doi)
LIFEx-texture: Karmazanovsky, G., Gruzdev, I., Tikhonova, V. et al. Computed tomography-based radiomics approach in pancreatic tumors characterization. Radiol med (2021) (doi)
LIFEx-viewer: Chen C, Cheng Y, Xu J, Zhang T, Shu X, Huang W, Hua Y, Zhang Y, Teng Y, Zhang L, Xu J. Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method. Journal of Personalized Medicine. 2021; 11(8):786 (doi)
LIFEx-texture: Bracci, S., Dolciami, M., Trobiani, C. et al. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. Radiol med (2021) (doi)
LIFEx-texture: Tang, Y., Zhang, T., Zhou, X. et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Onc 19, 45 (2021) (doi)
LIFEx-texture: Hainan Ren, Naoko Mori, Shunji Mugikura, Hiroaki Shimizu, Sakiko Kageyama, Masatoshi Saito, Kei Takase. Prediction of placenta accreta spectrum using texture analysis on coronal and sagittal T2‑weighted imaging. Abdominal Radiology. 20 July 2021 (doi)
LIFEx-texture: Orkun Sarioglu, Fatma C Sarioglu, Ahmet E Capar, Demet Fb Sokmez, Berna D Mete, Umit Belet. Clot-based radiomics features predict first pass effect in acute ischemic stroke. Interv Neuroradiol. 2021 May 18;15910199211019176 (doi)
LIFEx-texture: Baba, A., Kessoku, H., Akutsu, T. et al. Pre-treatment MRI predictor of high-grade malignant parotid gland cancer. Oral Radiol (2021)(doi)
LIFEx-texture: Chen B, Chen C, Wang J, Teng Y, Ma X and Xu J (2021) Differentiation of Low-Grade Astrocytoma From Anaplastic Astrocytoma Using Radiomics-Based Machine Learning Techniques. Front. Oncol. 11:521313. (doi)
LIFEx-texture: Girot, C., Volk, A., Walczak, C. et al. New method for quantification of intratumoral heterogeneity: a feasibility study on Ktrans maps from preclinical DCE-MRI. Magn Reson Mater Phy (2021) (doi)
LIFEx-texture: Elkilany, A., Fehrenbach, U., Auer, T.A. et al. A radiomics-based model to classify the etiology of liver cirrhosis using gadoxetic acid-enhanced MRI. Sci Rep 11, 10778 (2021) (doi)
LIFEx-texture: Timothée Zaragori, Julien Oster, Veronique Roch, Gabriela Hossu, Mohammad Bilal Chawki, Rachel Grignon, Celso Pouget, Guillaume Gauchotte, Fabien Rech, Marie Blonski, Luc Taillandier, Laëtitia Imbert and Antoine Verger. 18F-FDOPA PET for the non-invasive prediction of glioma molecular parameters: a radiomics study. Journal of Nuclear Medicine May 2021, jnumed.120.261545 (doi)
LIFEx-MTV: François Allioux, Damaj Gandhi, Jean-Pierre Vilque, Cathy Nganoa, AnneClaire Gac, Nicolas Aide & Charline Lasnon (2021): End-of-treatment 18F-FDG PET/CT in diffuse large B cell lymphoma patients: ∆SUV outperforms Deauville score, Leukemia & Lymphoma (doi)
LIFEx-viewer: Francesco Bianconi, Mario Luca Fravolini, Sofia Pizzoli, Isabella Palumbo, Matteo Minestrini, Maria Rondini, Susanna Nuvoli, Angela Spanu, Barbara Palumbo. Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT. Quant Imaging Med Surg 2021;11(7):3286-3305 (doi)
LIFEx-texture: Mahmoud M.A., Shihab M., Saad SS., Elhussiny F., Houseni M. Imaging differentiation of malignant hepatic tumors: radiomics and metabolic features of 18F-FDG PET/CT. REJR 2021; 11(2):165-170 (doi)
LIFEx-texture: Friconnet, G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol 4, 105–115 (2021)(doi)
LIFEx-texture: Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol (2021) (doi)
Zeydanli, T., Kilic, H.K. Performance of quantitative CT texture analysis in differentiation of gastric tumors. Jpn J Radiol 2021 (doi)
LIFEx-texture: P Vincenta, ME. Maeder, B Hunt, B Linn, T MangelsDick, T Hasan, KK. Wan and BW. Pogue. CT Radiomic Features of Photodynamic Priming in Clinical Pancreatic Adenocarcinoma Treatment. 2021 Phys. Med. Biol (doi)
LIFEx-texture: Lee JW, Park S-H, Ahn H, Lee SM, Jang SJ. Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT. Cancers. 2021; 13(14):3563 (doi)
LIFEx-texture: Tu SJ, Tran VT, Teo JM, Chong WC, Tseng JR. Utility of radiomic zones for risk classification and clinical outcome predictions using supervised machine learning during simultaneous 11 C-choline PET/MRI acquisition in prostate cancer patients. Med Phys. 2021 Jul 2 (doi)
LIFEx-texture: Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848 (doi)
LIFEx-texture: Kimura, K., Yoshida, S., Tsuchiya, J. et al. Usefulness of texture features of apparent diffusion coefficient maps in predicting chemoradiotherapy response in muscle-invasive bladder cancer. Eur Radiol (2021)(doi)
LIFEx-texture: Mitamuraa K, Norikanea T, Yamamotoa Y＊, Nishishitaa AI, Kobataa T, Fujimotoa K, Takamia Y, Kudomib N, Hoshikawac H , and Nishiyamaa Y. Texture Indices of 18F-FDG PET/CT for Differentiating Squamous Cell Carcinoma and Non-Hodgkin’s Lymphoma of the Oropharynx. Acta Med. Okayama, 2021 Vol. 75, No. 3, pp. 351-356 (doi)
LIFEx-texture: Costa, G.; Cavinato, L.; Masci, C.; Fiz, F.; Sollini, M.; Politi, L.S.; Chiti, A.; Balzarini, L.; Aghemo, A.; di Tommaso, L.; et al. Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases. Cancers 2021, 13, 3077 (doi)
LIFEx-texture: The Clinical Impact of the Late Imaging with 18F-Fluorodeoxyglucose PET Texture Analysis in Invasive Lobular Breast Cancer. FO FALAY, H SEYMEN - Turk J Oncol 2021;36(3):273–84 (doi)
LIFEx-texture: Fiz, F.; Costa, G.; Gennaro, N.; la Bella, L.; Boichuk, A.; Sollini, M.; Politi, L.S.; Balzarini, L.; Torzilli, G.; Chiti, A.; et al. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the “Radiological” Tumour Microenvironment. Diagnostics 2021, 11, 1162 (doi)
LIFEx-texture: Y Chen, H Li, J Feng, S Suo, Q Feng, J Shen. A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease - Journal of Inflammation Research, june 2021 (doi)
LIFEx-texture: Orlhac, F.; Buvat, I. Comment on Ibrahim et al. The Effects of In-Plane Spatial Resolution on CT-Based Radiomic Features’ Stability with and without ComBat Harmonization. Cancers 2021, 13, 1848. Cancers 2021, 13, 3037 (doi)
LIFEx-texture: Ai Y, Zhang J, Jin J, Zhang J, Zhu H and Jin X (2021) Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors. Front. Oncol. 11:610742. (doi)
LIFEx-texture: Gill A.B., Rundo L, Wan JCM, Lau D, Zawaideh JP, Woitek R, Zaccagna F, Beer L, Gale D, Sala E, Couturier DL, Corrie PG, Rosenfeld N, Gallagher FA. Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma. 2020 Cancers Nov 24;12(12):3493 (doi).
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LIFEx-texture: Masci, G.M., Iafrate, F., Ciccarelli, F. et al. Tocilizumab effects in COVID-19 pneumonia: role of CT texture analysis in quantitative assessment of response to therapy. Radiol med (2021) (doi)
LIFEx-texture: Aboelyazid Elkilany, Uli Fehrenbach, Timo Alexander Auer, Tobias Müller, Wenzel Schöning, Bernd Hamm1 & Dominik Geisel. A radiomics‑based model to classify the etiology of liver cirrhosis using gadoxetic acid‑enhanced MRIScientific Reports | (2021) 11:10778 (doi)
LIFEx-viewer: Bordonneet al. High-quality brain perfusion SPECT images may be achieved with a high-speed recording using 360° CZT camera. EJNMMI Physics (2020) 7:65 (doi)
LIFEx-texture: Kim, M., Gu, W., Nakajima, T. et al. Texture analysis of [18F]-fluorodeoxyglucose-positron emission tomography/computed tomography for predicting the treatment response of postoperative recurrent or metastatic oral squamous cell carcinoma treated with cetuximab. Ann Nucl Med (2021) (doi)
LIFEx-texture: Iodine Map Radiomics in Breast Cancer: Prediction of Metastatic Status by Lukas Lenga,Simon Bernatz,Simon S. Martin,Christian Booz,Christine Solbach,Rotraud Mulert-Ernst,Thomas J. Vogl andDoris Leithner.Cancers 2021, 13(10), 2431 (doi)
LIFEx-texture: MA Mazzei, L Di Giacomo, GBagnacci, V Nardone, & al. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer—a multicenter study of GIRCG (Italian Research Group for Gastric Cancer) Quant Imaging Med Surg2021;11(6):2376-238 (doi)
LIFEx-texture: Friconnet, G. Exploring the correlation between semantic descriptors and texture analysis features in brain MRI. Chin J Acad Radiol (2021) (doi)
LIFEx-texture: Tomita, H., Yamashiro, T., Heianna, J. et al. Nodal-based radiomics analysis for identifying cervical lymph node metastasis at levels I and II in patients with oral squamous cell carcinoma using contrast-enhanced computed tomography. Eur Radiol (2021) (doi)
LIFEx-texture: Daria Ripani, Carmelo Caldarella, Tommaso Za, Elena Rossi, Valerio De Stefano, Alessandro Giordano. Progression to symptomatic multiple myeloma predicted by texture analysis-derived parameters in patients without focal disease at 18F-FDG PET/CT. Clinical Lymphoma Myeloma and Leukemia 2021 (doi)
LIFEx-texture: Mazzei, M., Giacomo, L., Bagnacci, G., Nardone, V., Gentili, F., Lucii, G., Tini, P., Marrelli, D., Morgagni, P., Mura, G., Baiocchi, G., Pittiani, F., Volterrani, L., Roviello, F. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer - a multicenter study of GIRCG (Italian Research Group for Gastric Cancer ; Quant Imaging Med Surg 2021;11(6):2376-2387 (doi)
LIFEx-texture: Markich, R., Palussière, J., Catena, V. et al. Radiomics complements clinical, radiological, and technical features to assess local control of colorectal cancer lung metastases treated with radiofrequency ablation. Eur Radiol (2021) (doi)
LIFEx-texture: Hotta, M., Minamimoto, R., Gohda, Y. et al. Prognostic value of 18F-FDG PET/CT with texture analysis in patients with rectal cancer treated by surgery. Ann Nucl Med (2021) (doi)
LIFEx-texture: Crombé, A., Buy, X., Han, F., Toupin, S. and Kind, M. (2021), Assessment of Repeatability, Reproducibility, and Performances of T2 Mapping‐Based Radiomics Features: A Comparative Study. J Magn Reson Imaging (doi)
LIFEx-MTV: Have Volume-based Parameters of Positron Emission Tomography/Computed Tomography Prognostic Relevance for Patients With Potentially Platinum-responsive Recurrent Ovarian Cancer? A Single Center Italian Study. A Gadduci, E Simonetti, F Guidoccio, G Manca, A Giorgetti, T Depalo, S Cosio, M Miccoli and D Volterrani. Anticancer Research 41: 1937-1944 (2021) (doi)
LIFEx-texture: Paolo Florent Felisaz, Giulia Colelli, Elena Ballante, Francesca Solazzo, Matteo Paoletti, Giancarlo Germani, Francesco Santini, Xeni Deligianni, Niels Bergsland, Mauro Monforte, Giorgio Tasca, Enzo Ricci, Stefano Bastianello, Silvia Figini, Anna Pichiecchio ; Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy ; European Journal of Radiology 134 (2021) 109460 (doi)
LIFEx-texture: Nakajo, M., Jinguji, M., Tani, A. et al. Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment [18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer. Mol Imaging Biol (2021) (doi)
LIFEx-texture: Claudia E. Weber, Matthias Wittayer, Matthias Kraemer, Andreas Dabringhaus, Michael Platten, Achim Gass, Philipp Eisele ; Quantitative MRI texture analysis in chronic active multiple sclerosis lesions, Magnetic Resonance Imaging, Volume 79, 2021, Pages 97-102 (doi)
LIFEx-texture: Xue, B., Wu, S., Zhang, M. et al. A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis. Abdom Radiol (2021) (doi)
LIFEx-texture: Krajnc, D.; Papp, L.; Nakuz, T.S.; Magometschnigg, H.F.; Grahovac, M.; pielvogel, C.P.; Ecsedi, B.; Bago-Horvath, Z.; Haug, A.; Karanikas, G.; et al. Breast Tumor Characterization Using [18F]FDG-PET/CT Imaging Combined with Data Preprocessing and Radiomics. Cancers 2021, 13, 1249 (doi)
LIFEx-texture: Thuillier, P.; Liberini, V.; Rampado, O.; Gallio, E.; De Santi, B.; Ceci, F.; Metovic, J.; Papotti, M.; Volante, M.; Molinari, F.; et al. Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms. Biomedicines 2021, 9, 281 (doi)
LIFEx-texture: Sollini, M., Kirienko, M., Cavinato, L. et al. Methodological framework for radiomics applications in Hodgkin’s lymphoma. European J Hybrid Imaging 4, 9 (2020) (doi)
LIFEx-texture: Amandine Crombé, Xavier Buy, Fei Han, Solenn Toupin and Michèle Kind. ORIGINAL RESEARCHAssessment of Repeatability,Reproducibility, and Performances of T2Mapping-Based Radiomics Features:A Comparative Study. J. MAGN. RESON. IMAGING 2021 (doi)
LIFEx-texture: Liberini, V., De Santi, B., Rampado, O. et al. Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor. EJNMMI Phys 8, 21 (2021) (doi)
LIFEx-texture: E. Forde, M. Leech, C. Robert, E. Herron, L. Marignol. Influence of inter-observer delineation variability on radiomic features of the parotid gland. Physica Medica 82 (2021) 240-248 (doi)
LIFEx-texture: Junjie Hang, Kequn Xu, Ruohan Yin, Yueting Shao, Muhan Liu, Haifeng Shi, Xiaoyong Wang3 and Lixia Wu. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases. Journal of Cancer 2021; 12(8): 2351-2358. doi: 10.7150/jca.49569 (doi)
LIFEx-texture: Zhang Tao, Zhang YueHua, Liu Xinglong, Xu Hanyue, Chen Chaoyue, Zhou Xuan, Liu Yichun, Ma Xuelei. Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades. Front. Oncol., 11 February 2021 (10)-3227 (doi)
LIFEx-texture: Liberini, V., De Santi, B., Rampado, O. et al. Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor. EJNMMI Phys 8, 21 (2021) (doi)
LIFEx-texture: Hayato Tomita, Tsuneo Yamashiro, Gyo Iida, Maho Tsubakimoto, Hidefumi Mimura and Sadayuki Murayama. Unenhanced CT texture analysis with machine learning for differentiating between nasopharyngeal cancer and nasopharyngeal malignant lymphoma. Nagoya J. Med. Sci. 83. 135–149, 2021 (doi)
LIFEx-texture-MTV: Hirata, Kenji and Tamaki, Nagara. Quantitative FDG PET Assessment for Oncology Therapy. Cancers 2021, 13(4) 869 (doi)
LIFEx-texture: Cepeda, S., García-García, S., Arrese, I. et al. Relationship between the overall survival in glioblastomas and the radiomic features of intraoperative ultrasound: a feasibility study. J Ultrasound (2021) (doi)
LIFEx-texture: Larobina, M., Megna, R. & Solla, R. Comparison of three freeware software packages for 18F-FDG PET texture feature calculation. Jpn J Radiol (2021) (doi)
LIFEx-texture: Yoon, H., Ha, S., Kwon, S.J. et al. Prognostic value of tumor metabolic imaging phenotype by FDG PET radiomics in HNSCC. Ann Nucl Med (2021) (doi)
LIFEx-texture: Virginia Liberini , Osvaldo Rampado, Elena Gallio, Bruno De Santi, Francesco Ceci, Beatrice Dionisi, Dionisi, Beatrice, Philippe Thuillier, Libero Ciuffreda, Alessandro Piovesan, Federica Fioroni, Annibale Versari, Filippo Molinari, Désirée Deandreis ; 68Ga-DOTATOC PET/CT-Based Radiomic Analysis and PRRT Outcome: A Preliminary Evaluation Based on an Exploratory Radiomic Analysis on Two Patients. Front. Med., 26 January 2021 (doi)
LIFEx-texture: Sarioglu, O., Sarioglu, F.C., Capar, A.E. et al. The role of CT texture analysis in predicting the clinical outcomes of acute ischemic stroke patients undergoing mechanical thrombectomy. Eur Radiol (2021) (doi)
LIFEx-texture: Youyin Tang, Tao Zhang, Yunuo Zhao, Zheyu Chen and Xuelei Ma. Development and validation of a comprehensive radiomics nomogram for prognostic prediction of primary hepatic sarcomatoid carcinoma after surgical resection. International Journal of Medical Sciences 2021; 18(7): 1711-1720 (doi)
LIFEx-texture: Jeonghyun Kang, Jae-Hoon Lee, Hye Sun Lee, Eun-Suk Cho, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Chihyun Park, Yunku Yeu, Jean R. Clemenceau, Sunho Park, Hongming Xu, Changjin Hong and Tae Hyun Hwang. Radiomics Features of 18F-Fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer. Cancers 2021, 13, 392 (doi)
LIFEx-texture: Zhou, Y., Ma, Xl., Zhang, T. et al. Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach. Eur J Nucl Med Mol Imaging (2021) (doi)
LIFEx-texture: Beaumont, H., Iannessi, A., Bertrand, AS. et al. Harmonization of radiomic feature distributions: impact on classification of hepatic tissue in CT imaging. Eur Radiol (2021) (doi)
LIFEx-texture: Alongi, P., Stefano, A., Comelli, A. et al. Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients. Eur Radiol (2021) (doi)
LIFEx-texture: A Radiomics-Based Imaging Tool to Monitor Tumor-Lymphocyte Infiltration and Outcome in Cancer Patients Treated by Anti-PD-1/PD-L1. United States Patent Application 20210003555. Ferte Charles (Bethesda, MD, US), Limkin Elaine Johanna (Cachan, FR), Sun Roger (Paris, FR), Deutsch Eric (Paris, FR) (doi)
LIFEx-texture: Wu, YJ., Liu, YC., Liao, CY. et al. A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules. Sci Rep 11, 66 (2021) (doi)
LIFEx-MTV: Hotta, M., Minamimoto, R., Toyohara, J. et al. Efficacy of cell proliferation imaging with 4DST PET/CT for predicting the prognosis of patients with esophageal cancer: a comparison study with FDG PET/CT. Eur J Nucl Med Mol Imaging (2021) (doi)
LIFEx-texture: Yoo, S., Kang, S., Yoon, J. et al. Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy. Sci Rep 11, 296 (2021) (doi)
LIFEx-MTV: Hotta, M., Minamimoto, R., Toyohara, J. et al. Efficacy of cell proliferation imaging with 4DST PET/CT for predicting the prognosis of patients with esophageal cancer: a comparison study with FDG PET/CT. Eur J Nucl Med Mol Imaging (2021) (doi)
LIFEx-texture: Nicolas Aide, Nicolas Elie, Cécile Blanc-Fournier, Christelle Levy, Thibault Salomon and Charline Lasnon ; Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers ; Front. Oncol., 12 January 2021 (doi)
LIFEx-MTV: Prigent, K., Lasnon, C., Ezine, E. et al. Assessing immune organs on 18F-FDG PET/CT imaging for therapy monitoring of immune checkpoint inhibitors: inter-observer variability, prognostic value and evolution during the treatment course of melanoma patients. Eur J Nucl Med Mol Imaging (2021) (doi)
LIFEx-texture: Li, Y., Zhang, Y., Fang, Q. et al. Radiomics analysis of [18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma. Eur J Nucl Med Mol Imaging (2021) (doi)
LIFEx-texture: Sheen, H., Kim, J.S., Lee, J.K. et al. A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (2021) (doi)
LIFEx-texture: Sheen, H., Kim, J.S., Lee, J.K. et al A radiomics nomogram for predicting transcatheter arterial chemoembolization refractoriness of hepatocellular carcinoma without extrahepatic metastasis or macrovascular invasion. Abdom Radiol (2021). (doi)

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LIFEx-texture: Peter McAnena, Brian Moloney, Robert Browne, Niamh O’Halloran, Leon Walsh, Sinead Walsh, Declan Sheppard, Karl Sweeney, Michael Kerin, Aoife Lowery. A Radiomic Model To Classify Response To Neoadjuvant Chemotherapy in Breast Cancer. Research Square 2021 (doi)

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LIFEx-texture: Xue, Xiu-Qing; Yu, Wen-Ji; Shao, Xiao-Liang; Li, Xiao-Feng; Niu, Rong; Zhang, Fei-Fei; Shi, Yun-Mei; Wang, Yue-Tao Radiomics model based on preoperative 18F-fluorodeoxyglucose PET predicts N2-3b lymph node metastasis in gastric cancer patients, Nuclear Medicine Communications: December 23, 2021 - (doi)
LIFEx-texture: Nakajo, M., Jinguji, M., Tani, A. et al. Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients. Abdom Radiol (2021) (doi)
LIFEx-texture: Navid Hasani, Sriram S. Paravastu, Faraz Farhadi, Fereshteh Yousefirizi, Michael A. Morris, Arman Rahmim, Mark Roschewski, Ronald M. Summers, Babak Saboury. Artificial Intelligence in Lymphoma PET Imaging: A Scoping Review (Current Trends and Future Directions). PET Clinics. Vol 17, Issue 1, P145-174, Jan 01, 2022 (doi)
LIFEx-texture: Kevin Ma, Stephanie A, Harmon, Ivan S, Klyuzhin, Arman Rahmim, DABSNM Baris Turkbey. Clinical Application of Artificial Intelligence in Positron Emission Tomography: Imaging of Prostate Cancer. PET Clinics Vol 17, Issue 1, P137-143, Jan 01, 2022 (doi)
LIFEx-texture : Isil Basara Akin, Hakan Abdullah Ozgul, Canan Altay, Merih Guray Durak, Suleyman Ozkan Akso, Ali Ibrahim Sevinc, Mustafa Secil, Hakan Gulmez, Pinar Balci. Machine Learning-Based Ultrasound Texture Analysis in Differentiation of Benign Phyllodes Tumors from Borderline-Malignant Phyllodes Tumors. Ultraschall Me 2021 (doi)
LIFEx-texture: Dhirajlal Rajgor A., Patel S, McCulloch D, Obara B, Bacardit J, McQueen A, Aboagye E, Ali T, O’Hara J and Winston Hamilton D. The application of radiomics in laryngeal cancer. The British Institute of Radiology. 29 Sep 2021 (doi)
LIFEx-viewer: Thuilliera P, Liberinia V, Grimaldia S, Rampado O, Gallio E, De Santi B, Arvat E, Piovesan A, Filippi R, Molinari F, Deandreis A. Valeur pronostique des paramètres volumétriques corps entier extraits de la TEP/TDM au 68Ga-DOTATOC dans les tumeurs neuroendocrines bien différenciées. JO - Annales d'Endocrinologie Volume 82, issue 5, October 2021, Page 274 (doi)
LIFEx-texture: Thuillier, Philippe; Bourhis, David; Schick, Ulrike; Alavi, Zarrin; Guezennec, Catherine; Robin, Philippe; Kerlan, Véronique; Salaun, Pierre-Yve; Abgral, Ronan. Diagnostic value of positron-emission tomography textural indices for malignancy of 18F-fluorodeoxyglucose-avid adrenal lesions. Q J Nucl Med Mol Imaging ; 65(1): 79-87, 2021 Mar (doi)
LIFEx-texture: Hyun, Seung Hyup; Ahn, Mi Sun; Koh, Young Wha; Lee, Su Jin. A Machine-Learning Approach Using PET-Based Radiomics to Predict the Histological Subtypes of Lung Cancer. Clinical Nuclear Medicine: December 2019 - Volume 44 - Issue 12 - p 956-960 (doi)
LIFEx-texture: Sha ZHU, Hui XU, Chuyu SHEN, Yingjie WANG, Wenting XU, Shihao DUAN, Hanxiao CHEN, Xuejin OU, Linyan CHEN, Xuelei MA. Differential diagnostic ability of 18F-FDG PET/CT radiomics features between renal cell carcinoma and renal lymphoma. The Quarterly Journal of Nuclear Medicine and Molecular Imaging 2021 March;65(1):72-8 (doi)
LIFEx-texture: Tutino, Francesca; Puccini, Giulia; Linguanti, Flavia; Puccini, Benedetta; Rigacci, Luigi; Kovalchuk, Sofya; Sciagrà, Roberto; Berti, Valentina. Baseline metabolic tumor volume calculation using different SUV thresholding methods in Hodgkin lymphoma patients: interobserver agreement and reproducibility across software platforms. Nucl Med Commun ; 42(3): 284-291, 2021 Mar 01 (doi)

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LIFEx-texture: Vernuccio, F., Cannella, R., Bartolotta, T.V. et al. Advances in liver US, CT, and MRI: moving toward the future. Eur Radiol Exp 5, 52 (2021) (doi)
LIFEx-MTV: Annovazzi, A., Ferraresi, V., Rea, S. et al. Prognostic value of total metabolic tumour volume and therapy-response assessment by [¹⁸F]FDG PET/CT in patients with metastatic melanoma treated with BRAF/MEK inhibitors. Eur Radiol (2021) (doi)
LIFEx-texture: Kotaro Ito DDS, PhD , Hirotaka Muraoka DDS, PhD , Naohisa Hirahara DDS, PhD , Eri Sawada DDS, PhD , Satoshi Tokunaga DDS, PhD , Takashi Kaneda DDS, PhD , Quantitative assessment of the parotid gland using computed tomography texture analysis to detect parotid sialadenitis, Oral Surg Oral Med Oral Pathol Oral Radiol (2021), (doi)
LIFEx-texture: Mahmoud M.A., Shihab M., Saad SS., Elhussiny F., Houseni M. Effect of standardized uptake value discretization on radiomics features of liver tumors using 18FDG-PET/CT scan. REJR 2021; 11(3):132-137. DOI: 10.21569/2222-7415-2021-11-3-132-137 (doi)
LIFEx-texture: Zhang, X., Chen, L., Jiang, H. et al. A novel analytic approach for outcome prediction in diffuse large B-cell lymphoma by [¹⁸F]FDG PET/CT. Eur J Nucl Med Mol Imaging (2021). (doi)
LIFEx-texture: Mengmeng Yan and Weidong Wang. A radiomics model of predicting tumor volume change of patients with stage III non-small cell lung cancer after radiotherapy. Science Progress 2021, Vol. 104(1) 1–10 (doi)
LIFEx-texture: Wallis, D., Soussan, M., Lacroix, M. et al. An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging (2021). (doi)
LIFEx-texture: Piaopaio Ying, Wenyi Jin, Xiaoli Wu and Weiyang Cai. Association between CT-Quantified Body Composition and Recurrence, Survival in Nonmetastasis Colorectal Cancer Patients Underwent Regular Chemotherapy after Surgery" recently published in BioMed Research International. Artificial Intelligence for Medical Image Analysis.Volume 2021, Article ID 6657566 (doi)
LIFEx-texture: Samy Ammari, Stephanie Pitre Champagnat, Laurent Dercle, sylvain reuze, sebastien Diffetocq, tite mokoyoko, salma moalla, sara lakiss, joya hadchiti, emilie chouzenoux, corinne balleyguier, nathalie lassau, francois bidault. Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging. Frontiers in Oncology, Frontiers, 2021 (doi)
LIFEx-texture: Xiaozhen Y, Chunwang Y, Yinghua Z, Zhenchang W. Magnetic resonance radiomics signatures for predicting poorly differentiated hepatocellularcarcinomaA SQUIRE-compliant study; Medicine (2021) 100:19 (doi)
LIFEx-texture: Roberto Cannella, Riccardo Sartoris, Jules Grégory, Lorenzo Garzelli, Valérie Vilgrain, Maxime Ronot and Marco Dioguardi Burgio. Quantitative magnetic resonance imaging for focal liver lesions: bridging the gap between research and clinical practice. The British Journal of Radiology. Vol. 94, No. 1122 (doi)
LIFEx-texture: Effect of Chemotherapy on Liver Metabolism as Measured by PET/CT scan. Shaimaa A. Ahmed, AIDA Salama, Mohamed Mohamed Houseni, Asmaa A A Elsheshiny. Egypt. J. Biophys. Biomed. Engng. Vol. 21, No.1,pp.75-85 (2020) (doi)
LIFEx-texture: Xuehan Hu, Xun Sun, Fan Hu, Fang Liu, Weiwei Ruan, Tingfan Wu, Rui An, Xiaoli Lan. Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson’s disease and multiple system atrophy (doi)
LIFEx-texture: Yuhan Yang, Xuelei Ma, Yixi Wang, Xinyan Ding. Prognosis prediction of extremity and trunk wall soft-tissue sarcomas treated with surgical resection with radiomic analysis based on random survival forest. Updates in surgery. 2021 May 18 (doi)

LIFEx-texture: Gruzdev, I S; Zamyatina, K A; Tikhonova, V S; Kondratyev, E V; Glotov, A V; Karmazanovsky, G G; Revishvili, A Sh. Reproducibility of CT texture features of pancreatic neuroendocrine neoplasms. Eur J Radiol ; 133: 109371, 2020 Dec (doi)
LIFEx-texture: Liu Zefan, Zhu Guannan, Jiang Xian, Zhao Yunuo, Zeng Hao, Jing Jing, Ma Xuelei. Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning. Frontiers in Oncology. November 2020, Vol 10, Article 604288 (doi)
LIFEx-texture: Philipp Lohmann, Anna-Katharina Meißner, Martin Kocher, Elena K Bauer, Jan-Michael Werner, Gereon R Fink, Nadim J Shah, Karl-Josef Langen, Norbert Galldiks. Feature-based PET/MRI radiomics in patients with brain tumors. Neuro-Oncology Advances, Volume 2, Issue Supplement_4, December 2020, Pages iv15–iv21 (doi)
LIFEx-MTV: 18F-FDG-PET dissemination features in diffuse large B cell lymphoma are predictive of outcome ; Anne-Ségolène Cottereau, Christophe Nioche, Anne-Sophie Dirand, Jérôme Clerc, Franck Morschhauser, Olivier Casasnovas, Michel Meignan, Irène Buvat ; Journal of Nuclear Medicine, published on June 14, 2019 (doi)
LIFEx-texture: Integrating Liquid Biopsy and Radiomics to Monitor Clonal Heterogeneity of EGFR-Positive Non-Small Cell Lung Cancer ; Cucchiara Federico, Del Re Marzia, Valleggi Simona, Romei Chiara, Petrini Iacopo, Lucchesi Maurizio, Crucitta Stefania, Rofi Eleonora, De Liperi Annalisa, Chella Antonio, Russo Antonio, Danesi Romano ; Front. Oncol. 10:593831(doi)
LIFEx-texture: Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers ; Santiago Cepeda, Ignacio Arrese, Sergio Garcia-Garcia, Maria Velasco-Casares, Trinidad Escudero-Caro, Tomas Zamora, Rosario Sarabia ; World Neurosurgery ; Available online 28 November 2020 (doi)
LIFEx-texture: Development and validation of a CT-texture analysis nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Ren, C., Li, M., Zhang, Y. et al. Cancer Imaging 20, 86 (2020) (doi)
LIFEx-texture: CT texture analysis of mediastinal lymphadenopathy: Combining with US-based elastographic parameter and discrimination between sarcoidosis and lymph node metastasis from small cell lung cancer ; Eriko KodaTsuneo Yamashiro, Rintaro Onoe, Hiroshi Handa, Shinya Azagami, Shoichiro Matsushita, Hayato Tomita, Takeo Inoue, Masamichi Mineshita ; PlosOne Dec 2, 2020 (doi)
LIFEx-texture: Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma. Gill, A.B.; Rundo, L.; Wan, J.C.M.; Lau, D.; Zawaideh, J.P.; Woitek, R.; Zaccagna, F.; Beer, L.; Gale, D.; Sala, E.; Couturier, D.-L.; Corrie, P.G.; Rosenfeld, N.; Gallagher, F.A. Cancers 2020, 12, 3493 (doi)
LIFEx-texture:Texture indices of 4′-[methyl-11C]-thiothymidine uptake predict p16 status in patients with newly diagnosed oropharyngeal squamous cell carcinoma: comparison with 18F-FDG uptake. Ihara-Nishishita, A., Norikane, T., Mitamura, K. et al. European J Hybrid Imaging 4, 20 (2020) (doi)
LIFEx-texture: Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer. Toyama, Y., Hotta, M., Motoi, F. et al. Sci Rep 10, 17024 (2020) (doi)
LIFEx-Texture: Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells ; Sun R, Sundahl N, Hecht M, et al ; Journal for ImmunoTherapy of cancer 2020;8:e001429 (pdf)
LIFEx-Texture: Saint Martin, MJ., Orlhac, F., Akl, P. et al. A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study. Magn Reson Mater Phy (2020) (doi)
LIFEx-Texture: Intensity harmonization techniques influence radiomics features and radiomics‑based predictions in sarcoma patients ; Crombé, A., Kind, M., Fadli, D. et al. ; Sci Rep 10, 15496 (2020) (doi)
LIFEx-Viewer: High-quality brain perfusion SPECT images may be achieved with a high-speed recording using 360° CZT camera. Bordonne, M., Chawki, M.B., Marie, P. et al. EJNMMI Phys 7, 65 (2020) (doi)
LIFEx-Texture: Optimizing the Peritumoral Region Size in Radiomics Analysis for Sentinel Lymph Node Status Prediction in Breast Cancer ; Lan Lei, Junqi Sun, Prateek Prasanna, Chunling Liu, Chuan Huang ; Academic Radiology ; online 5 November 2020 (doi)
LIFEx-Texture: Fully automatic segmentation of diffuse large B cell lymphoma lesions on 3D FDG-PET/CT for total metabolic tumour volume prediction using a convolutional neural network. Blanc-Durand, P., Jégou, S., Kanoun, S. et al. Eur J Nucl Med Mol Imaging (2020) (doi)
LIFEx-Texture: Contrast-Enhanced CT-based Textural Parameters as Potential Prognostic Factors of Survival for Colorectal Cancer Patients Receiving Targeted Therapy. Zhao, Y., Yang, J., Luo, M. et al. Mol Imaging Biol 2020 (doi)
LIFEx-Texture: Methodological Study to Investigate the Potential of Ultrasound-Based Elastography and Texture as Biomarkers to Monitor Liver Tumors ; Salma Moalla, Charly Girot, Stéphanie Franchi-Abella, Samy Ammari, Corinne Balleyguier, Nathalie Lassau and Stéphanie Pitre-Champagnat ; Diagnostics 2020, 10, 811 (doi)
LIFEx-Texture: Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer. Toyama, Y., Hotta, M., Motoi, F. et al. Sci Rep 10, 17024 (2020) (doi)
LIFEx-Texture: Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma. J.Zhong, R.Frood, P.Brown, H.Nelstrop, R.Prestwich, G.McDermott, S.Currie, S.Vaidyanathan, A.F.Scarsbrook ; Clinical Radiology, Oct 2020 (doi)
LIFEx-Texture: Repeatability of 18F-FDG PET Radiomic Features in Cervical Cancer ; Crandall JP, Fraum TJ, Lee M, Jiang L, Grigsby PW, Wahl RL. J Nucl Med October 2, 2020 jnumed.120.247999 (doi)
LIFEx-Texture: Pancreas adenocarcinoma CT texture analysis: comparison of 3D and 2D tumor segmentation techniques. Kulkarni, A., Carrion-Martinez, I., Dhindsa, K. et al. ; Abdom Radiol (2020) (doi)
LIFEx-Texture: Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients. Crombé, A., Kind, M., Fadli, D. et al. Sci Rep 10, 15496 (2020) (doi)
LIFEx-Texture: How can we combat multicenter variability in MR radiomics? Validation of a correction procedure. Orlhac F, Lecler A, Savatovski J, Goya-Outi J, Nioche C, Charbonneau F, Ayache N, Frouin F, Duron L, Buvat I. Eur Radiol (2020) (doi)
LIFEx-Texture: Value of volumetric and textural analysis in predicting the treatment response in patients with locally advanced rectal cancer. Karahan Şen, N.P., Aksu, A. & Kaya, G.Ç. Ann Nucl Med (2020) (doi)
LIFEx-Viewer: Early Prediction of Tumor Response to Neoadjuvant Chemotherapy and Clinical Outcome in Breast Cancer Using a Novel FDG-PET Parameter for Cancer Stem Cell Metabolism ; Chanwoo Kim, Sang-Ah Han, Kyu Yeoun Won, Il Ki Hong and Deog Yoon Kim ; J. Pers. Med. 2020, 10, 132; doi:10.3390/jpm10030132 (doi)
LIFEx-Viewer: Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma. Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS. Theranostics. 2020 Aug 29;10(23):10838-10848. doi: 10.7150/thno.50283. PMID: 32929383; PMCID: PMC7482798 (doi)
LIFEx-Texture: Value of Shape and Texture Features from 18F-FDG PET/CT to Discriminate between Benign and Malignant Solitary Pulmonary Nodules: An Experimental Evaluation ; Palumbo B, Bianconi F, Palumbo I, Fravolini ML, Minestrini M, Nuvoli S, Stazza ML, Rondini M, Spanu A ; Diagnostics 2020, 10, 696 (doi)
LIFEx-Texture: Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma ; Park C, Na KJ, Choi H, Ock CY, Ha S, Kim M, Park S, Keam B, Kim TM, Paeng JC, Park IK, Kang CH, Kim DW, Cheon GJ, Kang KW, Kim YT, Heo DS. ; Theranostics 2020; 10(23):10838-10848 (doi)
LIFEx-Texture: A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer; Xian Jiang, Xiuhe Zou, Jing Sun, Aiping Zheng, Chao Su,, Contrast Media ; Molecular Imaging, vol. 2020, Article ID 5418364, 10 pages, 2020 (doi)
LIFEx-Texture: Association between immunotherapy biomarkers and glucose metabolism from F-18 FDG PET ; Kim BS, Kang J, Jun S, Im H, Pak K, Kim GH, Heo BJ, Kim YH ; European Review for Medical and Pharmacological Sciences ; 2020; 24: 8288-8295 (europeanreview)
LIFEx-Viewer: Reciprocal change in Glucose metabolism of Cancer and Immune Cells mediated by different Glucose Transporters predicts Immunotherapy response ; Kwon Joong Na, Hongyoon Choi, Ho Rim Oh, Yoon Ho Kim, Sae Bom Lee, Yoo Jin Jung, Jaemoon Koh, Samina Park, Hyun Joo Lee, Yoon Kyung Jeon, Doo Hyun Chung, Jin Chul Paeng, In Kyu Park, Chang Hyun Kang, Gi Jeong Cheon, Keon Wook Kang, Dong Soo Lee, and Young Tae Kim ; Theranostics. 2020; 10(21): 9579–9590 (doi)
LIFEx-Texture : A Nomogram Based on Radiomics with Mammography Texture Analysis for the Prognostic Prediction in Patients with Triple-Negative Breast Cancer ; Xian Jiang, Xiuhe Zou, Jing Sun, Aiping Zheng, Chao Su ; Contrast Media ; Molecular Imaging, vol. 2020, Article ID 5418364, 10 pages, 2020 (doi)
LIFEx-Texture: Evaluating Focal 18F-FDG Uptake in Thyroid Gland with Radiomics. Aksu, A., Karahan Şen, N.P., Acar, E. et al. Nucl Med Mol Imaging 2020 (doi)
LIFEx-Texture: Improving the quantitative classification of Erlenmeyer flask deformities. Adusumilli, G., Kaggie, J.D., D’Amore, S. et al. Skeletal Radiol 2020 (doi)
LIFEX-Texture: Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia. Zeng Q-Q, Zheng KI, Chen J, et al. MedComm. 2020;1–9 (doi)
LIFEx-Texture: Radiomics in diffusion data: a test–retest, inter- and intra-reader DWI phantom study ; C. Dreher, T.A. Kuder, F. König, A. Mlynarska-Bujny, C. Tenconi, D. Paech, H.-P. Schlemmer, M.E. Ladd, S. Bickelhaupt ; Clinical Radiology July 25, 2020 (doi)
LIFEX-Texture: Distinguishing Lymphomatous and Cancerous Lymph Nodes in 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography by Radiomics ; B Zheng, J Wu, Z Zhao, X Ou, P Cao, X Ma ; Contrast Media & Molecular Imaging, 2020, Article ID 3959236 (doi)
LIFEx-MTV:A Case of Metastatic Hereditary Leiomyomatosis and Renal Cell Cancer Syndrome-Associated Renal Cell Carcinoma Treated with a Sequence of Axitinib and Nivolumab Following Cytoreductive Nephrectomy ; Ichiro Yonese, Masaya Ito, Kosuke Takemura, Takao Kamai, Fumitaka Koga ; Journal of Kidney Cancer and VHL 2020; 7(2): 6-10 9 (doi)
LIFEx-MTV: Comparison of different automatic methods for the delineation of the total metabolic tumor volume in I–II stage Hodgkin Lymphoma. Martín-Saladich, Q., Reynés-Llompart, G., Sabaté-Llobera, A. et al. Sci Rep 10, 12590 (2020) (doi)
LIFEx-Texture: Radiomics in diffusion data: a test–retest, inter-and intra-reader DWI phantom study ; C.Dreher, T.A.Kuder, F.König, A.Mlynarska-Bujny, C.Tenconi, D.Paech, H. P. Schlemmer, M.E.Ladd, S. Bickelhaupt ; Clinical Radiology ; Available online 25 July 2020 (doi)
LIFEx-Texture: Discrimination between pituitary adenoma and craniopharyngioma using MRI-based image features and texture features ; Yang Zhang, Chaoyue Chen, Zerong Tian & Jianguo Xu ; Jpn J Radiol (2020) (doi)
LEFEx-Texture: MRI-based texture analysis to differentiate the most common parotid tumours; O.Sarioglu, F.C.Sarioglu, A.I. Akdogan, U.Kucuk, I.B.Arslan, I.Cukurova, Y.Pekcevik ; Clinical Radiology ; Available online 20 July 2020 (doi)
LIFEx-Texture: Reinventing Radiation Therapy with Machine Learning and Imaging Bio-markers (Radiomics): state-of-the-art, challenges and perspectives ; Laurent Dercle, Theophraste Henry, Alexandre Carré, Nikos Paragios, Eric Deutsch, Charlotte Robert ; Methods ; Available online 19 July 2020 (doi)
LIFEx-Texture: Prediction of survival outcome based on clinical features and pretreatment 18FDG-PET/CT for HNSCC patients ; Sayantani Ghosh, Shaurav Maulik, Sanjoy Chatterjee, Indranil Mallick, Nishant Chakravorty, JayantaMukherjee ; Computer Methods and Programs in Biomedicine ; Available online 18 July 2020, 105669 (doi)
LIFEx-Texture: Radiomics-based prediction of survival in patients with head and neck squamous cell carcinoma based on pre- and post-treatment 18F-PET/CT ; Zheran Liu, Yuan Cao, Wei Diao, Yue Cheng, Zhiyun Jia, Xingchen Peng ; AGING 2020, Vol. 12, Advance (pdf)
LIFEx-Texture: Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules ; Xiaonan Shao, Rong Niu, Xiaoliang Shao, Zhenxing Jiang and Yuetao Wang ; Shao et al. EJNMMI Research (2020) 10:80 (doi)
LIFEx-Texture: Radiomics: A New Biomedical Workflow to Create a Predictive Model. Comelli A. et al. (2020) In: Papież B., Namburete A., Yaqub M., Noble J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham (doi)
LIFEx-Texture: Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application ; JC Peeken, B Wiestler, SE Combs - Molecular Imaging in Oncology, 2020 (doi)
LIFEx-Texture : Peeken J.C., Wiestler B., Combs S.E. (2020) Image-Guided Radiooncology: The Potential of Radiomics in Clinical Application. In: Schober O., Kiessling F., Debus J. (eds) Molecular Imaging in Oncology. Recent Results in Cancer Research, vol 216. Springer, Cham (doi)
LIFEx-Texture: Value of 18F-FDG PET/CT radiomic features to distinguish solitary lung adenocarcinoma from tuberculosis. Yujing Hu & Xinming Zhao & Jianyuan Zhang & Jingya Han & Meng Dai ; Eur J Nucl Med Mol Imaging (2020) (doi)
LIFEx-Texture, LIFEx-MTV: 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival. Polverari, G.; Ceci, F.; Bertaglia, V.; Reale, M.L.; Rampado, O.; Gallio, E.; Passera, R.; Liberini, V.; Scapoli, P.; Arena, V.; Racca, M.; Veltri, A.; Novello, S.; Deandreis, D. Cancers 2020, 12, 1163 (doi)
LIFEx-Texture: MRI-Based Texture Features as Potential Prognostic Biomarkers in Anaplastic Astrocytoma Patients Undergoing Surgical Treatment ; Yang Zhang, Chaoyue Chen, Yangfan Cheng Danni Cheng Fumin Zhao and Jianguo Xu ; Contrast Media & Molecular Imaging ; Volume 2020, Article ID 2126768 (doi)
LIFEx-Texture: Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions ; Giovanni Caruana, Lucas M. Pessini, Roberto Cannella, Giuseppe Salvaggio, Andréa de Barros, Annalaura Salerno, Cristina Auger & Alex Rovira ; Eur Radiol (2020) (doi)
LIFEx-Texture: Predicting MGMT Promoter Methylation of Glioblastoma from Dynamic Susceptibility Contrast Perfusion: A Radiomic Approach. Girolamo Crisi Silvano Filice. Journal of Neuroimaging, May 2020 (doi)
LIFEx-Texture: Current status and quality of radiomics studies in lymphoma: a systematic review. Wang, H., Zhou, Y., Li, L. et al. Eur Radiol (2020) (doi)
LIFEx-Texture: Liver Tumor Burden in Pancreatic Neuroendocrine Tumors: CT Features and Texture Analysis in the Prediction of Tumor Grade and 18F-FDG Uptake ; Alessandro Beleù, Giulio Rizzo, Riccardo De Robertis, Alessandro Drudi, Gregorio Aluffi, Chiara Longo, Alessandro Sarno, Sara Cingarlini, Paola Capelli, Luca Landoni, Aldo Scarpa, Claudio Bassi and Mirko D’Onofrio ; Cancers 2020, 12, 1486 (doi)
LIFEx-Texture: Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base. Zhang Y, Shang L, Chen C, Ma X, Ou X, Wang J, Xia F and Xu J (2020) Front. Oncol. 10:752 (doi)
LIFEx-Texture: Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels. Liyuan Fan Qiang Cao Xiuping Ding Dongni Gao Qiwei Yang Baosheng Li ; Cancer Medicine, 27 may 2020 (doi)
LIFEx-Texture: Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma. Wang, H., Zhao, S., Li, L. et al. Eur Radiol (2020) (doi)
LIFEx-Texture: Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas. Kirienko, M., Ninatti, G., Cozzi, L. et al. Radiol med (2020) (doi)
LIFEx-Texture: MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma. Sarioglu, F.C., Sarioglu, O., Guleryuz, H. et al. Eur Radiol (2020) (doi)
LIFEx (Texture+MTV): 18F-FDG Pet Parameters and Radiomics Features Analysis in Advanced Nsclc Treated with Immunotherapy as Predictors of Therapy Response and Survival ; Giulia Polverari, Francesco Ceci, Valentina Bertaglia, Maria Lucia Reale, Osvaldo Rampado, Elena Gallio, Roberto Passera, Virginia Liberini, Paola Scapoli, Vincenzo Arena, Manuela Racca, Andrea Veltri, Silvia Novello and Désirée Deandreis. Published: 5 May 2020; Cancers 2020, 12, 1163 (doi)
LIFEx-Texture: Methodological framework for radiomics applications in Hodgkin Lymphoma. Martina Sollini, Margarita Kirienko, Lara Cavinato, Francesca Ricci, Matteo Biroli, Francesca Ieva, Letizia Calderoni, Elena Tabacchi, Cristina Nanni, Pier Luigi Zinzani, Stefano Fanti, Anna Guidetti, Alessandra Alessi, Paolo Corradini, Ettore Seregni, Carmelo Carlo Stella, Arturo Chiti ; Nuclear Medicine & Medical Imaging ; Hematology ; May 2020 (doi)
LIFEx-Texture: Differentiation of renal cell carcinoma subtypes through MRI-based radiomics analysis. Wang, W., Cao, K., Jin, S. et al. Eur Radiol (2020) (doi)
LIFEx-Texture: A Non-invasive Method to Diagnose Lung Adenocarcinoma. Yan M and Wang W (2020) Front. Oncol. 10:602 (doi)
LIFEx-Texture: Magnetic resonance imaging features of tumor and lymph node to predict clinical outcome in node-positive cervical cancer: a retrospective analysis. Park, S., Hahm, M.H., Bae, B.K. et al. Radiat Oncol 15, 86 (2020) (doi)
LIFEx-Texture: Texture Analysis of Ultrasound Images to Differentiate Simple Fibroadenomas From Complex Fibroadenomas and Benign Phyllodes Tumors ; I Basara Akin, H Ozgul, K Simsek, C Altay, M Secil, P Balci; Journal of Ultrasound in Medicine 2020 (doi)
LIFEx-Texture: Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma. Chen, L., Wang, H., Zeng, H. et al. Cancer Imaging 20, 28 (2020) (doi)
LIFEx-Texture: Delta-radiomics increases multicentre reproducibility: a phantom study. Nardone, V., Reginelli, A., Guida, C. et al. Med Oncol 37, 38 (2020)(doi)
LIFEx-Texture: Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis. Yuri S.Velichko, Amirhossein Mozafarykhamseh, Tugce Agirlar Trabzonlu, Zhuoli Zhang, Alfred W. Rademaker, Vahid Yaghmai (doi)
LIFEx-Texture: Treatment-related changes in neuroendocrine tumors as assessed by textural features derived from 68Ga-DOTATOC PET/MRI with simultaneous acquisition of apparent diffusion coefficient. Weber, M., Kessler, L., Schaarschmidt, B. et al. BMC Cancer 20, 326 (2020) (doi)
LIFEx-Texture: Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy. Aide, N., Fruchart, C., Nganoa, C. et al. ; Eur Radiol (2020) (doi)
LIFEx-Texture: A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound ; Nobuyuki Kagiyama, Sirish Shrestha, Jung Sun Cho, Muhammad Khalil, Yashbir Singh, Abhiram Challa, Grace Casaclang-Verzosa, Partho P. Sengupta ; EBioMedicine 54 (2020) 102726 (doi)
LIFEx-Texture: Immunotherapy in Metastatic Colorectal Cancer: Could the Latest Developments Hold the Key to Improving Patient Survival? Damilakis, E.; Mavroudis, D.; Sfakianaki, M.; Souglakos, J. ; Cancers 2020, 12, 889 (mdpi)
LIFEx-Texture: Radiomics of cholangiocarcinoma on pretreatment CT can identify patients who would best respond to radioembolisation. Mosconi, C., Cucchetti, A., Bruno, A. et al. Eur Radiol (2020) (doi)
LIFEx-Texture: High-Dimensional Statistical Learning and Its Application to Oncological Diagnosis by Radiomics ; Bouveyron C. (2020) ; In: Nordlinger B., Villani C., Rus D. (eds) Healthcare and Artificial Intelligence. Springer, Cham (doi)
LIFEx-Texture: Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine? ; Nicolas Giraud, Paul Sargos, Nicolas Leduc, Olivier Saut, Te Vuong and Veronique Vendrely ; Appl. Sci. 2020, 10, 1988; (doi)
LIFEx-Texture: Magnetic resonance imaging assessment of chemotherapy-related adipocytic maturation in myxoid/round cell liposarcomas: specificity and prognostic value ; Amandine Crombe, Maxime Sitbon, berhard Stoeckle, Antoine Italiano, Xavier Buy, François Le Loarer, Michèle Kind ; the British Institute of Radiology; February 27, 2020 (birpublications)
LIFEx-Texture: Performance of Multiparametric Functional Imaging and Texture Analysis in Predicting Synchronous Metastatic Disease in Pancreatic Ductal Adenocarcinoma Patients by Hybrid PET/MR: Initial Experience ; Gao Jing, Huang Xinyun, Meng Hongping, Zhang Miao, Zhang Xiaozhe, Lin Xiaozhu, Li Biao ; Front. Oncol., 25 February 2020 (frontiers)
LIFEx-Texture: Integrated radiomic model for predicting the prognosis of esophageal squamous cell carcinoma patients undergoing neoadjuvant chemoradiation ; Tien-Chi Hou, Wen-Chien Huang, Hung-Chi Tai, Yu-Jen Chen ; Ther Radiol Oncol 2019;3:28 (tro)
LIFEx-Texture: Radiomic Analysis of Craniopharyngioma and Meningioma in the Sellar/Parasellar Area with MR Images Features and Texture Features: A Feasible Study ; Zerong Tian, Chaoyue Chen, Yang Zhang, Yimeng Fan, Ridong Feng and Jianguo Xu ; Contrast Media & Molecular Imaging ; Volume 2020, Article ID 4837156 (doi)
LIFEx-Texture: Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images. Eur Radiol (2020). Jin, X., Ai, Y., Zhang, J. et al. (doi)
LIFEx-Texture: Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer. Blanc-Durand, P., Campedel, L., Mule, S. et al. Eur Radiol (2020). (doi)
LIFEx-Texture: Radiogenomics predicts the expression of microRNA-1246 in the serum of esophageal cancer patients ; Hoshino, I., Yokota, H., Ishige, F. et al. Sci Rep 10, 2532 (2020) (nature)
LIFEx-Texture: Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer. Lacroix Maxime, Frouin Frederique, Dirand Anne-Sophie, Nioche Christophe, Orlhac Fanny, Bernaudin Jean-François, Brillet Pierre-Yves, Buvat Irène ; Front. Oncol. 10:43. doi:10.3389/fonc.2020.00043 (frontiers)
LIFEx-Texture: Hypovascular pancreas head adenocarcinoma: CT texture analysis for assessment of resection margin status and high-risk features ; Kulkarni, A., Carrion-Martinez, I., Jiang, N.N. et al. Eur Radiol (2020) (doi)
LIFEx-Texture: Can radiomics improve the prediction of metastatic relapse of myxoid/round cell liposarcomas? Amandine Crombé, François Le Loarer, Maxime Sitbon, Antoine Italiano, Eberhard Stoeckle, Xavier Buy, Michèle Kind ; January 2020 ; European Radiology (springer)
LIFEx-Texture: Development and validation of a nomogram based on CT images and 3D texture analysis for preoperative prediction of the malignant potential in gastrointestinal stromal tumors. Ren, C., Wang, S. & Zhang, S ; Cancer Imaging 20, 5 (2020) doi:10.1186/s40644-019-0284-7 (cancerimagingjournal)
LIFEx-Texture: Projection-space implementation of deep learning-guided low-dose brain PET imaging improves performance over implementation in image-space ; Amirhossein Sanaat, Hossein Arabi, Ismini Mainta, Valentina Garibotto and Habib Zaidi ; Journal of Nuclear Medicine, published on January 10, 2020 (jnm)
LIFEx-Texture: Predictive Role of Temporal Changes in Intratumoral Metabolic Heterogeneity During Palliative Chemotherapy in Patients with Advanced Pancreatic Cancer: A Prospective Cohort Study. Yoo SH1, Kang SY2, Cheon GJ2, Oh DY3,4, Bang YJ1,4. J Nucl Med. 2020 Jan;61(1):33-39. (pubmed)
LIFEx-Texture: Ability of Radiomics in Differentiation of Anaplastic Oligodendroglioma From Atypical Low-Grade Oligodendroglioma Using Machine-Learning Approach ; Zhang Yang, Chen Chaoyue, Cheng Yangfan, Teng Yuen, Guo Wen, Xu Hui, Ou Xuejin, Wang Jian, Li Hui, Ma Xuelei, Xu Jianguo ; Frontiers in Oncology ; 2019, vol9 p1371 (frontiers)
LIFEx-MTV: F-FDG PET Dissemination Features in Diffuse Large B-Cell Lymphoma Are Predictive of Outcome ; Anne-Ségolène Cottereau, Christophe Nioche, Anne-Sophie Dirand, Jérome Clerc, Franck Morschhauser, Olivier Casasnovas, Michel Meignan and Irène Buvat ; J Nucl Med January 1, 2020 vol. 61 no. 1 40-45 (jnm)

LIFEx-texture: Tian, Zerong; Chen, Chaoyue; Fan, Yimeng; Ou, Xuejin; Wang, Jian; Ma, Xuelei; Xu, Jianguo. Glioblastoma and Anaplastic Astrocytoma: Differentiation Using MRI Texture Analysis. Front Oncol ; 9: 876, 2019 (doi)
LIFEx-texture: A downsampling strategy to assess the predictive value of radiomic features. Dirand, AS., Frouin, F. & Buvat, I. ; Sci Rep 9, 17869 (2019) (doi)
LIFEx-Texture: An initial experience of machine learning based on multi-sequence texture parameters in magnetic resonance imaging to differentiate glioblastoma from brain metastases. Machiko Tateishi, Takeshi Nakaura, Mika Kitajima, Hiroyuki Uetani, Masataka Nakagawa, Taihei Inoue, Jun-ichiro Kuroda, Akitake Mukasa, Yasuyuki Yamashita ; Journal of the Neurological Sciences ; Volume 410, 15 March 2020 (doi)
LIFEx-Texture: Multiparametric quantitative and texture 18F-FDG PET/CT analysis for primary malignant tumour grade differentiation ; Mykola Novikov ; Eur Radiol Exp 3, 48 (2019) (doi)
LIFEx-Texture: Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab ; V Nardone, P Tini, P Pastina, C Botta, A Reginelli, Oncology Letters ; Dec 2019 (spandidos)
LIFEx-Texture: Differential diagnosis of pancreatic serous cystadenoma and mucinous cystadenoma: utility of textural features in combination with morphological characteristics ; J Yang, X Guo, H Zhang, W Zhang, J Song, H Xu, X Ma - BMC Cancer, 2019 (bmccancer)
LIFEx-Texture-MTV: Association of metabolic and genetic heterogeneity in head and neck squamous cell carcinoma with prognostic implications: integration of FDG PET and genomic analysis ;Jinyeong Choi, Jeong-An Gim, Chiwoo Oh, Seunggyun Ha, Howard Lee, Hongyoon Choi & Hyung-Jun Im ; EJNMMI Research volume 9, Article number: 97 (2019) (ejnmmi)
LIFEx-Texture: The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study ; Chaoyue Chen, Xinyi Guo, Jian Wang, Wen Guo, Xuelei Ma and Jianguo Xu ; December 2019 ; Frontiers in Oncology (frontiers)
LIFEx-Texture: Radiomics based on 18F-FDG PET/CT could differentiate breast carcinoma from breast lymphoma using machine-learning approach: A preliminary study ; Xuejin Ou, Jing Zhang, Jian Wang, Fuwen Pang, Yongsheng Wang, Xiawei Wei, Xuelei Ma ; Cancer Medicine. 2019;00:1–11 (onlinelibrary)
LIFEx-Texure: Metastasis risk prediction model in osteosarcoma using metabolic imaging phenotypes: A multivariable radiomics model ; Heesoon Sheen, Wook Kim, Byung Hyun Byun, Chang-Bae Kong, Won Seok Song, Wan Hyeong Cho, Ilhan Lim, Sang Moo Lim, Sang-Keun WooID1 ; PLoS ONE 14(11): e0225242 (doi)
LIFEx-Texture: Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool ; Marie Manon Krebs Krarup, Lotte Nygard, Ivan Richter Vogelius, Flemming Littrup Andersen, Gary Cook, Vicky Goh, Barbara Malene Fischer ; Volume 144, March 2020, Pages 72-78 (doi)
LIFEx-Texture: Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer ; Jianyuan Zhang, Xinming Zhao, Yan Zhao, Jingmian Zhang, Zhaoqi Zhang, Jianfang Wang, Yingchen Wang, Meng Dai, Jingya Han ; European Journal of Nuclear Medicine and Molecular Imaging ; November 2019 ; pp 1–10 (springer)
LIFEx-Texture: A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer ; Davide Franceschini, Luca Cozzi, Fiorenza De RosePierina Navarria, Antonella Fogliata, Ciro Franzese, Donato Pezzulla, Stefano TomatisGiacomo Reggiori, Marta Scorsetti ;Strahlentherapie und Onkologie ; November 2019 ; (springer)
LIFEx-Texture: Contrast-Enhanced MRI Texture Parameters as Potential Prognostic Factors for Primary Central Nervous System Lymphoma Patients Receiving High-Dose Methotrexate-Based Chemotherapy ; Chaoyue Chen, Hongyu Zhuo, Xiawei Wei, Xuelei Ma ; Contrast Media & Molecular Imaging 2019(2):1-7 ; November 2019 (hindawi)
LIFEx-Texture: Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status ; Burak Kocak, Emine Sebnem Durmaz, Ece Ates, Ipek Sel, Saime Turgut Gunes, Ozlem Korkmaz Kaya, Amalya Zeynalova, Ozgur Kilickesmez ; November 2019 ; European Radiology (springer)
LIFEx-Texture: Radiomics-Based Machine Learning Technology Enables Better Differentiation Between Glioblastoma and Anaplastic Oligodendroglioma ; Yimeng Fan Chaoyue Chen, Fumin Zhao, Zerong Tian3, Jian Wang, Xuelei Ma and Jianguo Xu ; November 2019 Frontiers in Oncology 9:1164 (frontiers)
LIFEx-Texture: 11C-methionine-PET for diferentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifer ; Masatoshi Hotta, Ryogo Minamimoto & Kenta Miwa ; December 2019; Scientific Reports 9(1) (doi)
LIFEx-Texture: The Diagnostic Value of MRI-Based Texture Analysis in Discrimination of Tumors Located in Posterior Fossa: A Preliminary Study ; Yang Zhang, Chaoyue Chen, Zerong Tian, Ridong Feng, Yangfan Cheng, Jianguo Xu ; October 2019 Frontiers in Neuroscience 13:1113 (frontiers)
LIFEx-Texture: Radiomics in stratification of pancreatic cystic lesions: Machine learning in action ; Vipin Dalal, Joseph Carmicheal, Amaninder Dhaliwal, Maneesh Jain, Sukhwinder Kaur, Surinder K.Batra ; Cancer Letters ; October 2019 (doi)
LIFEx-Texture: Machine Learning-based MRI Texture Analysis Enables Differentiation between Glioblastoma and Anaplastic Oligodendroglioma ; Yimeng Fan, Xuelei Ma, Chaoyue Chen, Zerong Tian, Jian Wang and Jianguo Xu ; Front. Oncol. 2019.01164 (doi)
LIFEx-Texture: A multidimensional nomogram combining overall stage, dose volume histogram parameters and radiomics to predict progression-free survival in patients with locoregionally advanced nasopharyngeal carcinoma ;
Kaixuan Yanga, Jiangfang Tiana, Bin Zhang, Mei Lia, Wenji Xie, Yating Zou, Qiaoyue Tan, Lihui Liu, Jinbing Zhu, Arthur Shou, Guangjun Li ; Oral Oncology ; Volume 98, November 2019, Pages 85-91 ; (doi)
LIFEx-Texture: Shape and Texture Analysis of Radiomic Data for Computer-Assisted Diagnosis and Prognostication: An Overview ; Francesco Bianconi, Mario Luca Fravolini, Isabella Palumbo, Barbara Palumbo ; Proceedings of the International Conference on Design Tools and Methods in Industrial Engineering, ADM 2019, September 9-10, 2019, Modena, Italy pp 3-14 (springer)
LIFEx-Texture: MRI derived radiomics: Methodology and clinical applications in the field of pelvic oncology ; Ulrike Schick, François Lucia, Gurvan Dissaux, Dimitris Visvikis, Bogdan Badic, Ingrid Masson, Olivier Pradier, Vincent Bourbonne and Mathieu Hatt ; the British Institute of Radiology ; 2019, 12 september (doi)
LIFEx-Texture: Radiomics with artificial intelligence: a practical guide for beginners ; Burak Koçak, Emine Sebnem Durmaz, Ece Ates, Özgür Kiliçkesmez ; Diagn Interv Radiol ; 4 september 2019 (doi)
LIFEx-texture: Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT ; PJ Brown, J Zhong, R Frood, S Currie, A Gilbert, AL Appelt, D Sebag-Montefiore, A Scarsbrook ; 04 September 2019 ; EJNMMI pp 1-10 (doi)
LIFEx-Texture: Conventional MRI radiomics in patients with suspected early- or pseudo-progression ; Alexandre Bani-Sadr, Omer Faruk Eker, Lise-Prune Berner, Roxana Ameli, Marc Hermier, Marc Barritault, David Meyronet, Jacques Guyotat, Emmanuel Jouanneau, Jerome Honnorat, François Ducray, Yves Berthezene ; Neuro-Oncology Advances ; 01 September 2019 (doi)
LIFEx-Texture: CT assessment of tumor heterogeneity and the potential for the prediction of human papillomavirus status in oropharyngeal squamous cell carcinoma ; Mungai F, Verrone GB, Pietragalla M, Berti V, Addeo G, Desideri I, Bonasera L, Miele V. Radiol Med. 2019 Mar 25. (pubmed)
LIFEx-Texture: Glioblastoma Multiforme and Anaplastic Astrocytoma: Differentiation using MRI Texture Analysis ; J Xu, X Ma, Z Tian, C Chen, Y Fan, X Ou, J Wang - Frontiers in Oncology, 2019 ; (doi)
LIFEx-Texture: Contrast-Enhanced CT Texture Analysis: a New Set of Predictive Factors for Small Cell Lung Cancer ; Chaoyue Chen, Xuejin Ou, Hui Li, Yanjie Zhao, Fengnian Zhao, Shengliang Zhou, Xuelei Ma ; Molecular Imaging and Biology ; August 2019 ; pp 1-7 (springer)
LIFEx-MTV: Time to prepare for risk adaptation in lymphoma by standardising measurement of metabolic tumour burden. Sally F Barrington, Michel Meignan ; Apr 2019 ; Journal of Nuclear Medicine ; (jnm)
LIFEx-Texture: Prognostic Value of Functional Parameters of 18F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma ; M Wang, H Xu, L Xiao, W Song, S Zhu, X Ma ; Contrast Media & Molecular Imaging, Volume 2019, Article ID 2641627 (doi, cm&mi)
LIFEx-Texture: Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study. Emine Acar, Asim Leblebici, Berat Ender Ellidokuz, Yasemin Basbinar and Gamze Çapa Kaya. British Institute of Radiology. Published Online: July 10, 2019 (doi)
LIFEx-Texture: AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Isabella Castiglioni, Francesca GallivanonePaolo Soda, Michele AvanzoJoseph StancanelloMarco AielloMatteo InterlenghiMarco Salvatore. European Journal of Nuclear Medicine and Molecular Imaging. First Online: 11 July 2019 ; (springer)
LIFEx-Texture: CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach ; N Taguchi, S Oda, Y Yokota, S Yamamura, M Imuta ;European Journal of Radiology ; Volume 118, September 2019, Pages 38-43 (sciencedirect)
LIFEx-Texture: Radiomics in nuclear medicine: robustness, reproducibility, standardization, and howto avoid data analysis traps and replication crisis ; Alex Zwanenburg ; European Journal of Nuclear Medicine and Molecular Imaging ; 25 June 2019 (doi)
LIFEx-Texture: Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics ; Luca Cozzi, Ciro Franzese, Antonella Fogliata, Davide Franceschini, Pierina Navarria, Stefano Tomatis, Marta Scorsetti ; Strahlentherapie und Onkologie, pp 1-14 (doi)
LIFEx-Texture: Discrimination of pancreatic serous cystadenomas from mucinous cystadenomas with CT textural features: based on machine learning ; Jing Yang, Xinli Guo, Xuejin Ou, Weiwei Zhang, Xuelei Ma ; Front. Oncol., 12 June 2019 (doi, link)
LIFEx-Texture: The Future of Medical Imaging ; Luigi Landini ; Current Pharmaceutical Design, 2018, Vol. 24, No. 46 (eurekaselect)
LIFEx-MTV: Time to prepare for risk adaptation in lymphoma by standardising measurement of metabolic tumour burden ; Sally F Barrington and Michel Meignan ; J Nucl Med April 6, 2019 jnumed.119.227249 (abstract)
LIFEx-Texture: Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET/CT in head and neck cancer ; Catherine Guezennec, David Bourhis, Fanny Orlhac, Philippe Robin, Jean-Baptiste Corre, Olivier Delcroix, Yves Gobel, Ulrike Schick, Pierre-Yves Salaun, Ronan Abgral ; PLOSone March 28, 2019 ; (doi, plosone)
LIFEx-Texture: PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy ; Lidija Antunovic, Rita De Sanctis, Luca Cozzi, Margarita Kirienko, Andrea Sagona, Rosalba Torrisi, Corrado Tinterri, Armando Santoro, Arturo Chiti, Renata Zelic, Martina Sollini ; 26 March 2019 ; European Journal of Nuclear Medicine and Molecular Imaging ; (doi)
LIFEx-Texture: Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers ; Paul Giraud, Philippe Giraud, Anne Gasnier, Radouane El Ayachy, Sarah Kreps, Jean-Philippe Foy, Catherine Durdux, Florence Huguet, Anita Burgun and Jean-Emmanuel Bibault ; Front. Oncol., 27 March 2019 ; (doi)
LIFEx-Texture: Texture Analysis on [18F]FDG PET/CT in Non-Small-Cell Lung Cancer: Correlations Between PET Features, CT Features, and Histological Types ; Francesco Bianconi, Isabella Palumbo, Mario Luca Fravolini, Rita Chiari, Matteo Minestrini, Luca Brunese, Barbara Palumbo ; March 2019 ; Molecular Imaging & Biology ; (doi)
LIFEx-Texture: Tumor heterogeneity in oral and oropharyngeal squamous cell carcinoma assessed by texture analysis of CT and conventional MRI: a potential marker of overall survival ; Jiliang Ren, Ying Yuan, Yiqian Shi, Xiaofeng Tao ;Acta Radiologica ; First Published February 28, 2019 (doi)
LIFEx-Texture: Ability of 18F-FDG PET/CT Radiomic Features to Distinguish Breast Carcinoma from Breast Lymphoma - Xuejin Ou, Jian Wang, Ruofan Zhou, Sha Zhu, Fuwen Pang, Yi Zhou, Rong Tian and Xuelei Ma ; Contrast Media & Molecular Imaging ; Volume 2019, Article ID 4507694, Published 25 February 2019, 9 pages (doi)
LIFEx-Texture: Postmortem Changes in Skeletal Muscle Can Be Expressed by Hounsfield Unit Measurements in Postmortem Computed Tomography—A Murine Model Study ; Yamada, Tsuyoshi; Takeuchi, Tamaki; Ito, Morihiro ; Journal of Medical Imaging and Health Informatics, Volume 9, Number 2 February 2019, pp. 261-266(6) (doi)
LIFEx-Texture: Validation of a method to compensate multicenter effects affecting CT radiomics. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Radiology 2019 (doi) (hal)
LIFEx-Texture: Computed tomography based radiomic signature as predictive of survival and local control after stereotactic body radiation therapy in pancreatic carcinoma. Cozzi L, Comito T, Fogliata A, Franzese C, Franceschini D, Bonifacio C, Tozzi A, Di Brina L, Clerici E, Tomatis S, Reggiori G, Lobefalo F, Stravato A, Mancosu P, Zerbi A, Sollini M, Kirienko M, Chiti A, Scorsetti M. PlosOne Jan 2019 (plosone) (doi)
LIFEx-Texture: Radiomics in Oncological PET/CT: a Methodological Overview. Seunggyun Ha, Hongyoon Choi, Jin Chul Paeng, Gi Jeong Cheon. Nuclear Medicine and Molecular Imaging Jan 2019 (springer)

LIFEx-Texture: Implications of reconstruction protocol for histo-biological characterisation of breast cancers using FDG-PET radiomics. Aide N, Salomon T, Blanc-Fournier C, Grellard JM, Levy C, Lasnon C. EJNMMI Research, Dec 2018 (springer)
LIFEx-Texture: Prognostic value of textural indices extracted from pretherapeutic 18-F FDG-PET/CT in head and neck squamous cell carcinoma. Guezennec C, Robin P, Orlhac F, Bourhis D, Delcroix O, Gobel Y, Rousset J, Schick U, Salaün PY, Abgral R. Head & Neck, Dec 2018 (doi)
LIFEx-Texture: The value of MR textural analysis in prostate cancer. Patel N, Henry A, Scarsbrook A. Clinical Radiology ; Available online 17 December 2018
(sciencedirect)(doi)
LIFEx-Texture: Effects of CT FOV displacement and acquisition parameters variation on texture analysis features. Biondi M, Vanzi E, De Otto G, Carbone SF, Nardone V, Banci Buonamici F. Physics in Medicine and Biology, 2018 Nov, 1361-6560 (link)
LIFEx-Texture: Machine-learning integration of CT histogram analysis to evaluate the composition of atherosclerotic plaques: Validation with IB-IVUS. Masudaae T, Nakaura T, Funamad Y, Okimoto T, Satob T, Higakie T, Noda N, Imadaa N, Babae Y, Awai K ; Journal of Cardiovascular Computed Tomography ; Oct 2018 (link)
LIFEx-Texture: Meignan M and Cottereau AS. FDG-PET in PMBCL: which heterogeneity? Blood 2018 132:117-118 (link)
LIFEx-MTV: Chantepie S, Hovhannisyan N, Guillouet S, Pelage JP, Ibazizene M, Bodet-Milin C, Carlier T, Gac AC, Réboursière E, Vilque JP, Kraeber-Bodéré F, Manrique A, Damaj G, Leporrier M, Barré L. 18F-Fludarabine PET for Lymphoma Imaging: First-in-Humans Study on DLBCL and CLL Patients. J Nucl Med. 2018 Sep;59(9):1380-1385 (link)
LIFEx-Texture: Nakagawa M, Nakaura T, Namimoto T, Kitajima M, Uetani H, Tateishi M, Oda S, Utsunomiya D, Makino K, Nakamura H, Mukasa A, Hirai T, Yamashita Y. Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma. European Journal of Radiology. 2018 Sep (link)
LIFEx-Texture: Vendrami CL, Velichko YS, Miller FH, Chatterjee A, Villavicencio CP, Yaghmai V, McCarthy RJ. Differentiation of Papillary Renal Cell Carcinoma Subtypes on MRI: Qualitative and Texture Analysis. AJR Am J Roentgenol. 2018 Sep 21:1-12 (link)
LIFEx-Texture: Lohmann P, Lerche C, Bauer EK, Steger J, Stoffels G, Blau T, Dunkl V, Kocher M, Viswanathan S, Filss CP, Stegmayr C, Ruge MI, Neumaier B, Shah NJ, Fink GR, Langen KJ & Galldiks N. Predicting IDH genotype in gliomas using FET PET radiomics. Scientific Reports 8, Article number: 13328 (2018) (link)
LIFEx-Texture: Liu C, Ding J, Spuhler K, Gao Y, Serrano Sosa M, Moriary M, Hussain S, He X, Liang C, Huang C. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI. J Magn Reson Imaging, 2018 Sep (link)
LIFEx-Texture: R Sun, EJ Limkin, M Vakalopoulou, L Dercle, S Champiat, S Rong Han, L Verlingue, D Brandao, A Lancia, S Ammari, A Hollebecque, JY Scoazec, A Marabelle, C Massard, JC Soria, C Robert, N Paragios, E Deutsch, C Ferté. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study; The Lancet Oncology ; Published:August 14, 2018 (link)
LIFEx-Texture: P Lohmann, M Kocher, G Ceccon, EK Bauer, G Stoffels, S Viswanathan, MI Ruge, B Neumaier, NJ Shah, GR Fink, KJ Langen, N Galldiks. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clinical. 2018, 20:537-542 (link).
LIFEx-Texture: C Nioche, F Orlhac, S Boughdad, S Reuzé, J Goya-Outi, C Robert, C Pellot-Barakat, M Soussan, F Frouin, and I Buvat. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Research 2018; 78(16):4786-4789 (link).
LIFEx-Texture: S Boughdad, C Nioche, F Orlhac, L Jehl, L Champion, I Buvat. Influence of age on radiomic features in 18F-FDG PET in normal breast tissue and in breast cancer tumors. Oncotarget 2018; 9:30855-30868 (link).
LIFEx-Texture: A Parvez, N Tau, D Hussey, M Maganti, U Metser. 18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin’s lymphoma as predictors of treatment outcome and survival. Ann Nucl Med (2018). https://doi.org/10.1007/s12149-018-1260-1 (link)
LIFEx-Texture: T Tsujikawa, H Tsuyoshi, M Kanno, S Yamada, M Kobayashi, N Narita, H Kimura, S Fujieda, Y Yoshida and H Okazawa. Selected PET radiomic features remain the same. Oncotarget. 2018; 9:20734-20746. https://doi.org/10.18632/oncotarget.25070. (link)
LIFEx-MTV: P Blanc-Durand, A Van Der Gucht, N Schaefer, E Itti, J O. Prior. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study. Plos One April 13, 2018 (link)
LIFEx-Texture: M Kirienko M, L Cozzi, A Rossi, E Voulaz, L Antunovic, A Fogliata, A Chiti, M Sollini. Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging. 2018 Apr 6. doi: 10.1007/s00259-018-3987-2. (link)
LIFEx-Texture: V Nardone, P Tini, S Croci, SF Carbone, L Sebaste, T Carfagno, G Battaglia, P Pastina, G Rubino, MA Mazzei, L Pirtoli. 3D bone texture analysis as a potential predictor of radiationinduced insufficiency fractures. Quant Imaging Med Surg 2018;8(1):14-24 (link)
LIFEx-Texture: C Caramella, A Allorant, F Orlhac, F Bidault, B Asselain, S Ammari, P Jaranowski, A Moussier, C Balleyguier, N Lassau, S Pitre-Champagnat. Can we trust the calculation of texture indices of CT images? A phantom study. Med Phys. 2018 Feb 14. doi: 10.1002/mp.12809 (link)
LIFEx-Texture: V Nardone, P Tini, C Nioche, MA Mazzei, T Carfagno, G Battaglia, P Pastina, R Grassi, L Sebaste, L Pirtoli. Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med. 2018 Jan 24. doi: 10.1007/s11547-017-0850-7 (link)
LIFEx-Texture: F Orlhac, S Boughdad, C Philippe, H Stalla-Bourdillon, C Nioche, L Champion, M Soussan, F Frouin, V Frouin, I Buvat. A post-reconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018 doi: 10.2967/jnumed.117.199935. [Epub ahead of print] (link)
LIFEx-Texture: M Kirienko, L Cozzi, L Antunovic, L Lozza, A Fogliata, E Voulaz, A Rossi, A Chiti, M Sollini ; Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging 45:207-217, 2018 (link)
LIFEx-Texture: N Aide, M Talbot, C Fruchart, G Damaj, C Lasnon ; Diagnostic and prognostic value of baseline FDG PET/CT skeletal textural features in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging. 2018;45(5):699-711 (link)
LIFEx-Texture: A Schernberg, S Reuze, F Orlhac, I Buvat, L Dercle, R Sun, E Limkin, A Escande, C Haie-Meder, E Deutsch, C Chargari, C Robert ; A score combining baseline neutrophilia and primary tumor SUVpeak measured from FDG PET is associated with outcome in locally advanced cervical cancer ; Eur J Nucl Med Mol Imaging. 2018;45(2):187-195. doi: 10.1007/s00259-017-3824-z (link)

LIFEx-Texture: L Cozzi, N Dinapoli, A Fogliata, WC Hsu, G Reggiori, F Lobefalo, M Kirienko, M Sollini, D Franceschini, T Comito, C Franzese, Ma Scorsetti and PM Wang ; Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer 2017 17:829 (link)
LIFEx-Texture: F Orlhac, C Nioche, M Soussan, I Buvat ; Understanding changes in tumor textural indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med 2017; 58:387–392: (link)
LIFEx-Texture: S Reuzé, F Orlhac, C Chargari, C Nioche, E Limkin, F Riet, A Escande, C Haie-Meder, L Dercle, S Gouy, I Buvat, E Deutsch, C Robert ; Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners. Oncotarget. 2017; 8(26):43169-43179 (link)
LIFEx-Texture: M Sollini, L Cozzi, G Pepe, L Antunovic, A Lania, L Di Tommaso, P Magnoni, PA Erba,M Kirienko ; [18F]FDG-PET/CT texture analysis in thyroid incidentalomas: preliminary results. European Journal of Hybrid Imaging, December 2017, 1:3 (link)
LIFEx-Texture: V Nardone, P Tini, C Nioche, M Biondi, L Sebaste, MA Mazzei, F Banci Buonamici, L Pirtoli ; Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: A subset analysis. Radiother Oncol. 2017 Feb;122(2):321. doi: 10.1016/j.radonc.2016.09.004 (link)

LIFEx-Texture: F Orlhac, B Thézé, M Soussan, R Boisgard, I Buvat ; Multiscale texture analysis: from 18F-FDG PET images to pathological slides. J Nucl Med 57: 1823-1828, 2016 (link)
LIFEx-Texture: O Diop, EAL Bathily, B Ndong, G Mbaye, RS Senghor, W Sow-Diop, M Soumboundou, LAD Diouf, AR Djiboune, PM Sy, M Diarra, O Ndoye, M Mbodj, S Seck-Gassama ; Etude de la robustesse des statistiques de premier ordre dans la discrimination des ganglions malins et benins dans le cancer du col de l'utérus. Journal des Sciences, I.S.S.N 0851 – 4631 (link)

LIFEx-Texture: F Orlhac, M Soussan, K Chouahnia, E Martinod, I Buvat ; 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non small cell lung cancer. Plos One 10(12):e0145063, 2015 (link)
LIFEx-Texture: I Buvat, F Orlhac, M Soussan ; Tumor texture analysis in PET: where do we stand? J Nucl Med 56: 1642-1644, 2015 (link)

LIFEx-Texture: M Soussan, F Orlhac, M Boubaya, L Zelek, M Ziol, V Eder, I Buvat ; Relationship between tumor heterogeneity measured on FDG-PET/CT and pathological prognostic factors in invasive breast cancer. Plos One 9: e94017, 2014 (link)
LIFEx-Texture: F Orlhac, M Soussan, JA Maisonobe, CA Garcia, B Vanderlinden, I Buvat ; Tumor texture analysis in 18F-FDG-PET: relationships between texture parameters, histogram indices, SUVs, metabolic volumes and total lesion glycolysis. J Nucl Med 55: 414-422, 2014 (link)

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