MRI-based Radiomics Models for Pretreatment Risk Stratification of Endometrial Cancer.

HomeRadiologyVol. 305, No. 2 PreviousNext Reviews and CommentaryEditorialMRI-based Radiomics Models for Pretreatment Risk Stratification of Endometrial CancerAki Kido , Mizuho NishioAki Kido , Mizuho NishioAuthor AffiliationsFrom the Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan.Address correspondence to A.K. (email: [email protected]).Aki Kido Mizuho NishioPublished Online:Jul 12 2022https://doi.org/10.1148/radiol.221398MoreSectionsFull textPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In References1. Lefebvre TL , Ueno Y , Dohan A , et al. Development and validation of multiparametric MRI-based radiomics models for preoperative risk stratification of endometrial cancer. Radiology 2022; 305(2):375–386 . Abstract, Google Scholar2. Lambin P , Rios-Velazquez E , Leijenaar R , et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012;48(4):441–446. Crossref, Medline, Google Scholar3. Zwanenburg A , Vallières M , Abdalah MA , et al. The Image Biomarker Standardization Initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020;295(2):328–338. Link, Google Scholar4. van Timmeren JE , Cester D , Tanadini-Lang S , Alkadhi H , Baessler B. Radiomics in medical imaging—“how-to” guide and critical reflection. Insights Imaging 2020;11(1):91. Crossref, Medline, Google Scholar5. Lambin P , Leijenaar RTH , Deist TM , et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017;14(12):749–762. Crossref, Medline, Google Scholar6. Collins GS , Reitsma JB , Altman DG , Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 2015;350:g7594. Crossref, Medline, Google Scholar7. Ueno Y , Forghani B , Forghani R , et al. Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification—a preliminary analysis. Radiology 2017;284(3):748–757. Link, Google Scholar8. Yan BC , Li Y , Ma FH , et al. Preoperative assessment for high-risk endometrial cancer by developing an MRI- and clinical-based radiomics nomogram: a multicenter study. J Magn Reson Imaging 2020;52(6):1872–1882. Crossref, Medline, Google Scholar9. Bluemke DA , Moy L , Bredella MA , et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers—from the Radiology Editorial Board. Radiology 2020;294(3):487–489. Link, Google Scholar10. Dewey M , Bosserdt M , Dodd JD , Thun S , Kressel HY. Clinical imaging research: higher evidence, global collaboration, improved reporting, and data sharing are the grand challenges. Radiology 2019;291(3):547–552. Link, Google ScholarArticle HistoryReceived: June 3 2022Revision requested: June 16 2022Revision received: June 16 2022Accepted: June 21 2022Published online: July 12 2022Published in print: Nov 2022 FiguresReferencesRelatedDetailsAccompanying This ArticleDevelopment and Validation of Multiparametric MRI–based Radiomics Models for Preoperative Risk Stratification of Endometrial CancerJul 12 2022RadiologyRecommended Articles Machine Learning for Hepatocellular Carcinoma Segmentation at MRI: Radiology In TrainingRadiology2022Volume: 304Issue: 3pp. 509-515Current Applications and Future Impact of Machine Learning in RadiologyRadiology2018Volume: 288Issue: 2pp. 318-328Update on MRI in Evaluation and Treatment of Endometrial CancerRadioGraphics2022Volume: 42Issue: 7pp. 2112-2130Endometrial Carcinoma: MR Imaging–based Texture Model for Preoperative Risk Stratification—A Preliminary AnalysisRadiology2017Volume: 284Issue: 3pp. 748-757On the Interpretability of Artificial Intelligence in Radiology: Challenges and OpportunitiesRadiology: Artificial Intelligence2020Volume: 2Issue: 3See More RSNA Education Exhibits Artificial Intelligence in Diagnostic Imaging: Current Applications and Future PerspectiveDigital Posters2019A Multidisciplinary Approach for Program Development with Artificial Intelligence in Pancreatic Cancer: How We Fit InDigital Posters2019The 2021 Update On MRI For Endometrial Cancer: What The Radiologist Needs To KnowDigital Posters2021 RSNA Case Collection Dedifferentiated Thyroid CancerRSNA Case Collection2020Renal Oncocytic NeoplasmRSNA Case Collection2022Small cell lung carcinomaRSNA Case Collection2020 Vol. 305, No. 2 Metrics Altmetric Score PDF download