{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T12:19:48Z","timestamp":1793449188777,"version":"4.5.0"},"reference-count":39,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:00:00Z","timestamp":1750291200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81971578"],"award-info":[{"award-number":["81971578"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82371921"],"award-info":[{"award-number":["82371921"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Endometrial cancer (EC) is a common gynecologic malignancy; accurate assessment of key prognostic factors is important for treatment planning.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To develop a deep learning (DL) framework based on biparametric MRI for automated segmentation and multitask classification of EC key prognostic factors, including grade, stage, histological subtype, lymphovascular space invasion (LVSI), and deep myometrial invasion (DMI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Subjects<\/jats:title><jats:p>A total of 325 patients with histologically confirmed EC were included: 211 training, 54 validation, and 60 test cases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>T2\u2010weighted imaging (T2WI, FSE\/TSE) and diffusion\u2010weighted imaging (DWI, SS\u2010EPI) sequences at 1.5 and 3\u2009T.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>The DL model comprised tumor segmentation and multitask classification. Manual delineation on T2WI and DWI acted as the reference standard for segmentation. Separate models were trained using T2WI alone, DWI alone and combined T2WI\u2009+\u2009DWI to classify dichotomized key prognostic factors. Performance was assessed in validation and test cohorts. For DMI, the combined model's was compared with visual assessment by four radiologists (with 1, 4, 7, and 20\u2009years' experience), each of whom independently reviewed all cases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Segmentation was evaluated using the dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), Hausdorff distance (HD95), and average surface distance (ASD). Classification performance was assessed using area under the receiver operating characteristic curve (AUC). Model AUCs were compared using DeLong's test. <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05 was considered significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the test cohort, DSCs were 0.80 (T2WI) and 0.78 (DWI) and JSCs were 0.69 for both. HD95 and ASD were 7.02\/1.71\u2009mm (T2WI) versus 10.58\/2.13\u2009mm (DWI). The classification framework achieved AUCs of 0.78\u20130.94 (validation) and 0.74\u20130.94 (test). For DMI, the combined model performed comparably to radiologists (<jats:italic>p<\/jats:italic>\u2009=\u20090.07\u20130.84).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The unified DL framework demonstrates strong EC segmentation and classification performance, with high accuracy across multiple tasks.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3.<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 3.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.70012","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T08:15:23Z","timestamp":1750320923000},"page":"1009-1020","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multitask Deep Learning for Automated Segmentation and Prognostic Stratification of Endometrial Cancer via Biparametric <scp>MRI<\/scp>"],"prefix":"10.1002","volume":"62","author":[{"given":"Ruixin","family":"Yan","sequence":"first","affiliation":[{"name":"Department of Radiology, State Key Laboratory of Vascular Homeostasis and Remodeling Peking University Third Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ximiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence Beijing University of Posts and Telecommunications  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiqi","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Radiology, Ruijin Hospital Shanghai Jiao Tong University School of Medicine  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiology, State Key Laboratory of Vascular Homeostasis and Remodeling Peking University Third Hospital 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