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  • 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).
  • LIFEx-texture: Karahan Şen, N.P., Aksu, A. & Çapa Kaya, G. A different overview of staging PET/CT images in patients with esophageal cancer: the role of textural analysis with machine learning methods. Ann Nucl Med (2021) (doi)
  • 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)