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Publications - Journal papers

Here is a list of publications related to LIFEx software for metabolic tumor volume measurement (MTV) and texture analysis.

Publications 2020 - Journal papers (45)

  • 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: Lacroix M, Frouin F, Dirand AS, NIOCHE C, Orlhac F, Bernaudin JF, Brillet PY and Buvat I. Correction for Magnetic Field Inhomogeneities and Normalization of Voxel Values Are Needed to Better Reveal the Potential of MR Radiomic Features in Lung Cancer. Front Oncol. 2020;10:43. Published 2020 Jan 31 (frontiers)
  • 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: Differential diagnosis of pancreatic serous cystadenoma and mucinous cystadenoma: utility of textural features in combination with morphological characteristics. Yang, J., Guo, X., Zhang, H. et al. BMC Cancer 19, 1223 2019 (doi)

Publications 2019 - Journal papers (47)

  • 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 (doicm&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 (doilink)
  • 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 ; (doiplosone)
  • 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)

Publications 2018 - Journal papers (26)

  • 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)

Publications 2017 - Journal papers (5)

  • 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)

Publications 2016 - Journal papers (2)

  • 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)

Publications 2015 - Journal papers (2)

  • 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)

Publications 2014 - Journal papers (2)

  • 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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