{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:43:31Z","timestamp":1774021411533,"version":"3.50.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2704405"],"award-info":[{"award-number":["2022YFC2704405"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","ejradiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.ejrad.2026.112745","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T16:12:56Z","timestamp":1771690376000},"page":"112745","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["MRI-based deep learning and radiomics for preoperative prediction of P53abn endometrial cancer: A multicenter study"],"prefix":"10.1016","volume":"198","author":[{"given":"Ke","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoli","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuqing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjian","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyi","family":"Gou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Hong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1199-1351","authenticated-orcid":false,"given":"Jin","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.ejrad.2026.112745_b0005","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1016\/S0140-6736(22)00323-3","article-title":"Endometrial cancer","volume":"399","author":"Crosbie","year":"2022","journal-title":"Lancet"},{"key":"10.1016\/j.ejrad.2026.112745_b0010","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1093\/bjr\/tqae066","article-title":"Determination of p53abn endometrial cancer: a multitask analysis using radiological-clinical nomogram on MRI","volume":"97","author":"Ning","year":"2024","journal-title":"Br. J. Radiol."},{"key":"10.1016\/j.ejrad.2026.112745_b0015","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1002\/path.5373","article-title":"Clinicopathological and molecular characterisation of \u201cmultiple-classifier\u201d endometrial carcinomas","volume":"250","author":"Le\u00f3n-Castillo","year":"2020","journal-title":"J. Pathol."},{"key":"10.1016\/j.ejrad.2026.112745_b0020","doi-asserted-by":"crossref","unstructured":"Cancer Genome Atlas Research Network, C. Kandoth, N. Schultz, A.D. Cherniack, R. Akbani, Y. Liu, H. Shen, A.G. Robertson, I. Pashtan, R. Shen, C.C. Benz, C. Yau, P.W. Laird, L. Ding, W. Zhang, G.B. Mills, R. Kucherlapati, E.R. Mardis, D.A. Levine, Integrated genomic characterization of endometrial carcinoma, Nature 497 (2013) 67\u201373. https:\/\/doi.org\/10.1038\/nature12113.","DOI":"10.1038\/nature12113"},{"key":"10.1016\/j.ejrad.2026.112745_b0025","doi-asserted-by":"crossref","first-page":"4171","DOI":"10.1007\/s00018-017-2575-0","article-title":"Molecularly targeted therapies for p53-mutant cancers","volume":"74","author":"Zhao","year":"2017","journal-title":"Cell. Mol. Life Sci. CMLS"},{"key":"10.1016\/j.ejrad.2026.112745_b0030","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1002\/cncr.30496","article-title":"Confirmation of ProMisE: a simple, genomics-based clinical classifier for endometrial cancer","volume":"123","author":"Talhouk","year":"2017","journal-title":"Cancer"},{"key":"10.1016\/j.ejrad.2026.112745_b0035","doi-asserted-by":"crossref","unstructured":"M. Yano, K. Ito, A. Yabuno, N. Ogane, T. Katoh, M. Miyazawa, M. Miyazawa, K. Hasegawa, H. Narahara, M. Yasuda, Impact of TP53 immunohistochemistry on the histological grading system for endometrial endometrioid carcinoma, Mod. Pathol. Off. J. U. S. Can. Acad. Pathol. Inc 32 (2019) 1023\u20131031. https:\/\/doi.org\/10.1038\/s41379-019-0220-1.","DOI":"10.1038\/s41379-019-0220-1"},{"issue":"Suppl 1","key":"10.1016\/j.ejrad.2026.112745_b0040","doi-asserted-by":"crossref","first-page":"S64","DOI":"10.1097\/PGP.0000000000000518","article-title":"Endometrial Carcinoma diagnosis: use of FIGO grading and genomic subcategories in clinical practice: recommendations of the international society of gynecological pathologists","volume":"38","author":"Soslow","year":"2019","journal-title":"Int. J. Gynecol. Pathol. off. J. Int. Soc. Gynecol. Pathol."},{"key":"10.1016\/j.ejrad.2026.112745_b0045","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1007\/s00404-020-05531-4","article-title":"TCGA molecular subgroups and FIGO grade in endometrial endometrioid carcinoma","volume":"301","author":"Travaglino","year":"2020","journal-title":"Arch. Gynecol. Obstet."},{"key":"10.1016\/j.ejrad.2026.112745_b0050","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.ygyno.2021.07.007","article-title":"Clear cell endometrial carcinomas with mismatch repair deficiency have a favorable prognosis: a systematic review and meta-analysis","volume":"162","author":"Travaglino","year":"2021","journal-title":"Gynecol. Oncol."},{"key":"10.1016\/j.ejrad.2026.112745_b0055","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1136\/ijgc-2020-002230","article-title":"ESGO\/ESTRO\/ESP guidelines for the management of patients with endometrial carcinoma","volume":"31","author":"Concin","year":"2021","journal-title":"Int. J. Gynecol. Cancer off. J. Int. Gynecol. Cancer Soc."},{"key":"10.1016\/j.ejrad.2026.112745_b0060","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1002\/path.5375","article-title":"p53 immunohistochemistry is an accurate surrogate for TP53 mutational analysis in endometrial carcinoma biopsies","volume":"250","author":"Singh","year":"2020","journal-title":"J. Pathol."},{"issue":"Suppl 1","key":"10.1016\/j.ejrad.2026.112745_b0065","doi-asserted-by":"crossref","first-page":"S123","DOI":"10.1097\/PGP.0000000000000488","article-title":"Interpretation of P53 immunohistochemistry in endometrial carcinomas: toward increased reproducibility","volume":"38","author":"K\u00f6bel","year":"2019","journal-title":"Int. J. Gynecol. Pathol. off. J. Int. Soc. Gynecol. Pathol."},{"key":"10.1016\/j.ejrad.2026.112745_b0070","doi-asserted-by":"crossref","first-page":"3869","DOI":"10.1007\/s00261-020-02567-7","article-title":"Multiparametric magnetic resonance imaging of endometrial polypoid lesions","volume":"45","author":"Lee","year":"2020","journal-title":"Abdom. Radiol. n. y."},{"key":"10.1016\/j.ejrad.2026.112745_b0075","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1148\/radiol.212873","article-title":"Development and validation of multiparametric MRI\u2013based radiomics models for preoperative risk stratification of endometrial cancer","volume":"305","author":"Lefebvre","year":"2022","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.112745_b0080","doi-asserted-by":"crossref","unstructured":"E. Maheshwari, S. Nougaret, E.B. Stein, G.M. Rauch, K.-P. Hwang, R.J. Stafford, A.H. Klopp, P.T. Soliman, K.E. Maturen, A.G. Rockall, S.I. Lee, E.A. Sadowski, A.M. Venkatesan, Update on MRI in Evaluation and Treatment of Endometrial Cancer, RadioGraphics 42 (2022) 2112\u20132130. https:\/\/doi.org\/10.1148\/rg.220070.","DOI":"10.1148\/rg.220070"},{"key":"10.1016\/j.ejrad.2026.112745_b0085","doi-asserted-by":"crossref","first-page":"167","DOI":"10.2174\/1573405618666220517101012","article-title":"Histogram analysis of apparent diffusion coefficient on diffusion weighted magnetic resonance imaging in differentiation between low and high grade serous ovarian cancer","volume":"19","author":"Liu","year":"2023","journal-title":"Curr. Med. Imaging"},{"issue":"63","key":"10.1016\/j.ejrad.2026.112745_b0090","first-page":"1126","article-title":"Endometrial cancer: the role of MRI quantitative assessment in preoperative staging and risk stratification","volume":"1987","author":"Keles","year":"2022","journal-title":"Acta Radiol. Stockh. Swed."},{"key":"10.1016\/j.ejrad.2026.112745_b0095","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.clinimag.2021.05.014","article-title":"Predictive value of T2-weighted imaging and dynamic contrast-enhanced MRI for assessing cervical invasion in patients with endometrial cancer: a meta-analysis","volume":"78","author":"Bi","year":"2021","journal-title":"Clin. Imaging"},{"key":"10.1016\/j.ejrad.2026.112745_b0100","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1186\/s12916-023-02898-4","article-title":"Multimodal-based machine learning strategy for accurate and non-invasive prediction of intramedullary glioma grade and mutation status of molecular markers: a retrospective study","volume":"21","author":"Ma","year":"2023","journal-title":"BMC Med."},{"key":"10.1016\/j.ejrad.2026.112745_b0105","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1002\/jmri.29186","article-title":"Association of pathological features and multiparametric MRI-Based radiomics with TP53-mutated prostate cancer","volume":"60","author":"Chen","year":"2024","journal-title":"J. Magn. Reson. Imaging JMRI"},{"key":"10.1016\/j.ejrad.2026.112745_b0110","doi-asserted-by":"crossref","unstructured":"M. Shahedi, J.D. Dormer, A.D. T T, Q.N. Do, Y. Xi, M.A. Lewis, A.J. Madhuranthakam, D.M. Twickler, B. Fei, Segmentation of uterus and placenta in MR images using a fully convolutional neural network, Proc. SPIE-- Int. Soc. Opt. Eng. 11314 (2020) 113141R. https:\/\/doi.org\/10.1117\/12.2549873.","DOI":"10.1117\/12.2549873"},{"key":"10.1016\/j.ejrad.2026.112745_b0115","article-title":"Sample size, power and effect size revisited: simplified and practical approaches in pre-clinical, clinical and laboratory studies","volume":"31","author":"Serdar","year":"2021","journal-title":"Biochem. Medica"},{"key":"10.1016\/j.ejrad.2026.112745_b0120","doi-asserted-by":"crossref","first-page":"195","DOI":"10.4103\/tjem.tjem_182_23","article-title":"Receiver operating characteristic curve analysis in diagnostic accuracy studies: a guide to interpreting the area under the curve value","volume":"23","author":"\u00c7orbac\u0131o\u011flu","year":"2023","journal-title":"Turk. J. Emerg. Med."},{"key":"10.1016\/j.ejrad.2026.112745_b0125","doi-asserted-by":"crossref","unstructured":"L. Vermij, A. L\u00e9on-Castillo, N. Singh, M.E. Powell, R.J. Edmondson, C. Genestie, P. Khaw, J. Pyman, C.M. McLachlin, P. Ghatage, S.M. de Boer, H.W. Nijman, V.T.H.B.M. Smit, E.J. Crosbie, A. Leary, C.L. Creutzberg, N. Horeweg, T. Bosse, TransPORTEC consortium, p53 immunohistochemistry in endometrial cancer: clinical and molecular correlates in the PORTEC-3 trial, Modern Pathology 35 (2022) 1475\u20131483. https:\/\/doi.org\/10.1038\/s41379-022-01102-x.","DOI":"10.1038\/s41379-022-01102-x"},{"key":"10.1016\/j.ejrad.2026.112745_b0130","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.3390\/diagnostics13152504","article-title":"A Coarse-to-fine fusion network for small liver tumor detection and segmentation: a real-world study","volume":"13","author":"Wu","year":"2023","journal-title":"Diagnostics"},{"key":"10.1016\/j.ejrad.2026.112745_b0135","doi-asserted-by":"crossref","DOI":"10.3389\/fnagi.2022.915009","article-title":"Automatic segmentation of white matter hyperintensities in routine clinical brain MRI by 2D VB-Net: a large-scale study","volume":"14","author":"Zhu","year":"2022","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.ejrad.2026.112745_b0140","doi-asserted-by":"crossref","DOI":"10.1093\/pcmedi\/pbad019","article-title":"Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma","volume":"6","author":"Chen","year":"2023","journal-title":"Precis. Clin. Med."},{"key":"10.1016\/j.ejrad.2026.112745_b0145","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.neunet.2018.01.005","article-title":"Deep neural network for traffic sign recognition systems: an analysis of spatial transformers and stochastic optimisation methods","volume":"99","author":"Arcos-Garc\u00eda","year":"2018","journal-title":"Neural Networks"},{"key":"10.1016\/j.ejrad.2026.112745_b0150","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13040-024-00384-y","article-title":"Processing imbalanced medical data at the data level with assisted-reproduction data as an example","volume":"17","author":"Zhu","year":"2024","journal-title":"Biodata Min."},{"key":"10.1016\/j.ejrad.2026.112745_b0155","doi-asserted-by":"crossref","first-page":"2904","DOI":"10.1007\/s00259-021-05220-7","article-title":"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","volume":"48","author":"Zhou","year":"2021","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"10.1016\/j.ejrad.2026.112745_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103438","article-title":"Automatic segmentation of the uterus on MRI using a convolutional neural network","volume":"114","author":"Kurata","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.ejrad.2026.112745_b0165","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s13246-022-01110-w","article-title":"Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network","volume":"45","author":"Polat","year":"2022","journal-title":"Phys. Eng. Sci. Med."},{"key":"10.1016\/j.ejrad.2026.112745_b0170","doi-asserted-by":"crossref","first-page":"14440","DOI":"10.1038\/s41598-021-93792-7","article-title":"Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network","volume":"11","author":"Kurata","year":"2021","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ejrad.2026.112745_b0175","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1002\/jmri.28323","article-title":"TP53 mutation estimation based on MRI radiomics analysis for breast cancer","volume":"57","author":"Sun","year":"2023","journal-title":"J. Magn. Reson. Imaging JMRI"},{"key":"10.1016\/j.ejrad.2026.112745_b0180","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/s12885-025-13424-5","article-title":"Estimation of TP53 mutations for endometrial cancer based on diffusion-weighted imaging deep learning and radiomics features","volume":"25","author":"Shen","year":"2025","journal-title":"BMC Cancer"},{"key":"10.1016\/j.ejrad.2026.112745_b0185","doi-asserted-by":"crossref","first-page":"19257","DOI":"10.3390\/ijms140919257","article-title":"The consequence of oncomorphic TP53 mutations in ovarian cancer","volume":"14","author":"Brachova","year":"2013","journal-title":"Int. J. Mol. Sci."},{"key":"10.1016\/j.ejrad.2026.112745_b0190","doi-asserted-by":"crossref","first-page":"506","DOI":"10.4236\/jct.2014.56058","article-title":"Oncomorphic TP53 mutations in gynecologic cancers lose the normal protein:protein interactions with the microRNA microprocessing complex","volume":"5","author":"Brachova","year":"2014","journal-title":"J. Cancer Ther."},{"key":"10.1016\/j.ejrad.2026.112745_b0195","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1038\/sj.bjc.6602567","article-title":"Expression patterns of angiogenic and lymphangiogenic factors in ductal breast carcinoma in situ","volume":"92","author":"W\u00fclfing","year":"2005","journal-title":"Br. J. Cancer"},{"key":"10.1016\/j.ejrad.2026.112745_b0200","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1186\/s12957-021-02398-w","article-title":"Potential of combination of DCE-MRI and DWI with serum CA125 and CA199 in evaluating effectiveness of neoadjuvant chemotherapy in breast cancer","volume":"19","author":"Zhang","year":"2021","journal-title":"World J. Surg. Oncol."},{"key":"10.1016\/j.ejrad.2026.112745_b0205","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.ejrad.2026.112745_b0210","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1186\/s13244-021-01052-z","article-title":"A primer on deep learning and convolutional neural networks for clinicians","volume":"12","author":"Iglesias","year":"2021","journal-title":"Insights Imaging"}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26000938?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X26000938?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T14:44:57Z","timestamp":1774017897000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X26000938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":42,"alternative-id":["S0720048X26000938"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112745","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"MRI-based deep learning and radiomics for preoperative prediction of P53abn endometrial cancer: A multicenter study","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2026.112745","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"112745"}}