[1] Yamagami W, Aoki D. Annual report of the Committee on Gynecologic Oncology, the Japan Society of Obstetrics and Gynecology. J Obstet Gynaecol Res 2015;41(2):167-77.
[2] Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69(1):7-34.
[3] Salvesen HB, Haldorsen IS, Trovik J. Markers for individualised therapy in endometrial carcinoma. The Lancet Oncology 2012;13(8):e353-61.
[4] Colombo N, Creutzberg C, Amant F, Bosse T, Gonzalez-Martin A, Ledermann J, et al. ESMOESGO-ESTRO Consensus Conference on Endometrial Cancer: Diagnosis, Treatment and Follow-up. Int J Gynecol Cancer 2016;26(1):2-30.
[5] Binder PS, Mutch DG. Update on prognostic markers for endometrial cancer. Women's health (London, England) 2014;10(3):277-88.
[6] Murali R, Delair DF, Bean SM, Abu-Rustum NR, Soslow RA. Evolving Roles of Histologic Evaluation and Molecular/Genomic Profiling in the Management of Endometrial Cancer. J Natl Compr Canc Netw 2018;16(2):201-9.
[7] AlHilli MM, Mariani A, Bakkum-Gamez JN, Dowdy SC, Weaver AL, Peethambaram PP, et al. Risk-scoring models for individualized prediction of overall survival in low-grade and highgrade endometrial cancer. Gynecol Oncol 2014;133(3):485-93.
[8] Werner HM, Trovik J, Marcickiewicz J, Tingulstad S, Staff AC, Amant F, et al. Revision of FIGO surgical staging in 2009 for endometrial cancer validates to improve risk stratification. Gynecol Oncol 2012;125(1):103-8.
[9] Prat J. Prognostic parameters of endometrial carcinoma. Human pathology 2004;35(6):649-62.
[10] Zhang GY, Wu LY, Li B, Huang MN, Zhang R, Li XG. Retrospective analysis of prognostic variables and clinical outcomes in surgically staged intermediate risk endometrial carcinoma. Eur J Obstet Gynecol Reprod Biol 2013;169(2):309-16.
[11] Bosse T, Peters EE, Creutzberg CL, Jurgenliemk-Schulz IM, Jobsen JJ, Mens JW, et al. Substantial lymph-vascular space invasion (LVSI) is a significant risk factor for recurrence in endometrial cancer--A pooled analysis of PORTEC 1 and 2 trials. Eur J Cancer 2015;51(13):1742-50.
[12] Han KH, Kim HS, Lee M, Chung HH, Song YS. Prognostic factors for tumor recurrence in endometrioid endometrial cancer stages IA and IB. Medicine (Baltimore) 2017;96(21):e6976.
[13] Traen K, Holund B, Mogensen O. Accuracy of preoperative tumor grade and intraoperative gross examination of myometrial invasion in patients with endometrial cancer. Acta obstetricia et gynecologica Scandinavica 2007;86(6):739-41.
[14] Helpman L, Kupets R, Covens A, Saad RS, Khalifa MA, Ismiil N, et al. Assessment of endometrial sampling as a predictor of final surgical pathology in endometrial cancer. Br J Cancer 2014;110(3):609-15.
[15] Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 2016;278(2):563-77.
[16] Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton JD, Snyder A, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol 2017;72(1):3-10.
[17] Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2018;47(3):604-20.
[18] Torheim T, Malinen E, Kvaal K, Lyng H, Indahl UG, Andersen EK, et al. Classification of dynamic contrast enhanced MR images of cervical cancers using texture analysis and support vector machines. IEEE Trans Med Imaging 2014;33(8):1648-56.
[19] Shen WC, Chen SW, Liang JA, Hsieh TC, Yen KY, Kao CH. [18]Fluorodeoxyglucose Positron Emission Tomography for the Textural Features of Cervical Cancer Associated with Lymph Node Metastasis and Histological Type. Eur J Nucl Med Mol Imaging 2017;44(10):1721-31.
[20] Lucia F, Visvikis D, Desseroit MC, Miranda O, Malhaire JP, Robin P, et al. Prediction of outcome using pretreatment (18)F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 2018;45(5):768- 86.
[21] Meier A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa R, Soslow RA, et al. Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer. Abdom Radiol (NY) 2019;44(6):2040-7.
[22] Ueno Y, Forghani B, Forghani R, Dohan A, Zeng XZ, Chamming's F, et al. Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis. Radiology 2017;284(3):748-57.
[23] Yamada I, Miyasaka N, Kobayashi D, Wakana K, Oshima N, Wakabayashi A, et al. Endometrial Carcinoma: Texture Analysis of Apparent Diffusion Coefficient Maps and Its Correlation with Histopathologic Findings and Prognosis. Radiology: Imaging Cancer 2019;1(2).
[24] Stanzione A, Cuocolo R, Del Grosso R, Nardiello A, Romeo V, Travaglino A, et al. Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study. Academic radiology 2020.
[25] Han Y, Xu H, Ming Y, Liu Q, Huang C, Xu J, et al. Predicting myometrial invasion in endometrial cancer based on whole-uterine magnetic resonance radiomics. J Cancer Res Ther 2020;16(7):1648-55.
[26] Yan BC, Li Y, Ma FH, Feng F, Sun MH, Lin GW, 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):1872-82.
[27] Fasmer KE, Hodneland E, Dybvik JA, Wagner-Larsen K, Trovik J, Salvesen O, et al. WholeVolume Tumor MRI Radiomics for Prognostic Modeling in Endometrial Cancer. J Magn Reson Imaging 2021;53(3):928-37.
[28] Chen J, Gu H, Fan W, Wang Y, Chen S, Chen X, et al. MRI-Based Radiomic Model for Preoperative Risk stratification in Stage I Endometrial Cancer. J Cancer 2021;12(3):726-34.
[29] Yan BC, Li Y, Ma FH, Zhang GF, Feng F, Sun MH, et al. Radiologists with MRI-based radiomics aids to predict the pelvic lymph node metastasis in endometrial cancer: a multicenter study. Eur Radiol 2021;31(1):411-22.
[30] Chalkidou A, O'Doherty MJ, Marsden PK. False Discovery Rates in PET and CT Studies with Texture Features: A Systematic Review. PloS one 2015;10(5):e0124165.
[31] Mongan J, Moy L, Kahn Jr CE. Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A guide for authors and reviewers. Radiological Society of North America; 2020.
[32] Beddy P, O'Neill AC, Yamamoto AK, Addley HC, Reinhold C, Sala E. FIGO staging system for endometrial cancer: added benefits of MR imaging. Radiographics : a review publication of the Radiological Society of North America, Inc 2012;32(1):241-54.
[33] Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: A Freeware for Radiomic Feature Calculation in Multimodality Imaging to Accelerate Advances in the Characterization of Tumor Heterogeneity. Cancer research 2018;78(16):4786-9.
[34] Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw 2010;36(11):1-13.
[35] Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016:785-94.
[36] Nishio M, Nishizawa M, Sugiyama O, Kojima R, Yakami M, Kuroda T, et al. Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization. PloS one 2018;13(4):e0195875.
[37] Ellmann S, Wenkel E, Dietzel M, Bielowski C, Vesal S, Maier A, et al. Implementation of machine learning into clinical breast MRI: Potential for objective and accurate decision-making in suspicious breast masses. PloS one 2020;15(1):e0228446.
[38] Kawagishi M, Kubo T, Sakamoto R, Yakami M, Fujimoto K, Aoyama G, et al. Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and expert's knowledge. PloS one 2018;13(11).
[39] Koskas M, Fournier M, Vanderstraeten A, Walker F, Timmerman D, Vergote I, et al. Evaluation of models to predict lymph node metastasis in endometrial cancer: A multicentre study. Eur J Cancer 2016;61:52-60.
[40] Pavic M, Bogowicz M, Würms X, Glatz S, Finazzi T, Riesterer O, et al. Influence of interobserver delineation variability on radiomics stability in different tumor sites. Acta oncologica (Stockholm, Sweden) 2018;57(8):1070-4.