Publications 2022 - Journal papers (167)

  • 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)
  • LIFEx-viewer: Rahimpour, M., Saint Martin, MJ., Frouin, F. et al. Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI. Eur Radiol (2022) (doi)
  • 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