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Radiomics features were extracted from TVUS images, and deep learning features were obtained using ResNet-based networks. These features, with clinical variables, were combined to build a multimodal model. Feature selection in the training cohort used reproducibility filtering (intraclass correlation coefficient [ICC] &gt;0.80), univariate analysis, Pearson correlation (|r|&gt;0.90), and least absolute shrinkage and selection operator (LASSO) regression. Model performance was evaluated by receiver operating characteristic (ROC) curves, area under the curve (AUC), calibration, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0223_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The multimodal model achieved AUCs of 0.941 (95\u202f% CI: 0.897\u20130.985) and 0.922 (95\u202f% CI: 0.852\u20130.992) in training and validation cohorts, outperforming clinical (0.812, 0.791) and radiomics-only models (0.871, 0.843). DeLong tests were significant (p&lt;0.05). DCA showed higher clinical net benefit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0223_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This multimodal model effectively predicts 1-year recurrence after hysteroscopic EP resection, supporting individualized postoperative management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0223","type":"journal-article","created":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T11:56:46Z","timestamp":1782388606000},"page":"459-473","source":"Crossref","is-referenced-by-count":1,"title":["Transvaginal ultrasound-based radiomics and integrated clinical indicators via multimodal deep learning for prediction of endometrial polyp recurrence after hysteroscopic surgery"],"prefix":"10.1515","volume":"71","author":[{"given":"Yun","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Gynecology , Hefei First People\u2019s Hospital (Binhu Branch) , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyang","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Gynecology , Hefei First People\u2019s Hospital (Binhu Branch) , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gynecology , Hefei First People\u2019s Hospital (Binhu Branch) , Hefei , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"key":"2026100814512967176_j_bmt-2026-0223_ref_001","doi-asserted-by":"crossref","unstructured":"Chen, X, Chen, G, Bao, D, Liu, J, Zhang, X. 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