{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:57:23Z","timestamp":1792789043778,"version":"4.5.0"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"publisher","award":["12475352"],"award-info":[{"award-number":["12475352"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"publisher","award":["82273570"],"award-info":[{"award-number":["82273570"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"publisher","award":["82574015"],"award-info":[{"award-number":["82574015"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"key project of Zhejiang Natural Science Foundation","award":["LZ24A050008"],"award-info":[{"award-number":["LZ24A050008"]}]},{"name":"\u201cPioneer and leading goose\u201d R&D Program of Zhejiang","award":["2024C03044"],"award-info":[{"award-number":["2024C03044"]}]},{"name":"\u201cPioneer and leading goose\u201d R&D Program of Zhejiang","award":["*2025C01SA100245"],"award-info":[{"award-number":["*2025C01SA100245"]}]},{"name":"Autonomous Region Regional Collaborative Innovation Special Project","award":["2026E01072"],"award-info":[{"award-number":["2026E01072"]}]},{"name":"Key project of Zhejiang Provincial Health Science and Technology Program","award":["WKJ-ZJ-2437"],"award-info":[{"award-number":["WKJ-ZJ-2437"]}]},{"name":"Major project of Wenzhou Science and Technology Bureau","award":["Y20240118"],"award-info":[{"award-number":["Y20240118"]}]},{"name":"Major project of Wenzhou Science and Technology Bureau","award":["ZY2024016"],"award-info":[{"award-number":["ZY2024016"]}]},{"name":"Zhejiang Engineering Research Center for innovation and application of Intelligent Radiotherapy Technology, the Summit Advancement Discipline of Zhejiang Province"},{"id":[{"id":"https:\/\/ror.org\/00rd5t069","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Zhejiang-Hong Kong Precision Theranostics of Thoracic Tumors Joint Laboratory"},{"name":"Wenzhou key Laboratory of basic science and translational research of radiation oncology"},{"name":"Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, Discipline Cluster of Oncology, Wenzhou Medical University","id":[{"id":"https:\/\/ror.org\/00rd5t069","id-type":"ROR","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/00rd5t069","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Objectives: Cervical cancer (CC) remains a leading cause of cancer-related morbidity and mortality among women worldwide, with postoperative recurrence posing a major challenge to long-term survival. Multimodal data integration, including imaging, textual, and clinical information, holds significant potential for enhancing prognostic prediction. This study aims to develop and validate a multimodal, multi-scale deep learning (DL) model for predicting recurrence in operable CC patients. Methods: This multicenter retrospective study included 445 operable CC patients with preoperative MRI and corresponding radiology reports from three institutions. A Multi-Scale Model (MSM) combining ConvNeXt and dual-path Vision Transformer (ViT) was developed. Textual features extracted from radiology reports using BERT were fused with imaging and clinical data to construct a multimodal network (MSM-TC). Model interpretability was enhanced using SHapley Additive exPlanations (SHAP) and attention visualization. Results: The MSM integrating ConvNeXt and ViT achieved receiver operating characteristic curve (AUC) values of 0.944, 0.837, and 0.681 in the training, internal validation, and external validation cohorts, respectively. Incorporating textual data (MSM-T) further improved performance (AUCs: 0.902, 0.860, and 0.742). The final multimodal model (MSM-TC) integrating imaging, textual, and clinical data demonstrated the highest predictive accuracy with AUCs of 0.930, 0.860, and 0.798 across training, internal validation, and external validation cohorts, respectively. Kaplan\u2013Meier analysis confirmed that the model-derived risk score effectively stratified patients into high- and low-risk groups. Conclusions: Our multimodal, multi-scale DL framework robustly predicts recurrence and survival in operable CC patients, supporting its potential for individualized prognostic assessment and future clinical translation.<\/jats:p>","DOI":"10.3390\/cancers18193075","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:21:48Z","timestamp":1790072508000},"page":"3075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Scale MRI, Radiology Reports, and Blood Biomarker\u2013Guided Multimodal Deep Learning for Predicting Postoperative Recurrence in Cervical Cancer: A Multicenter Study"],"prefix":"10.3390","volume":"18","author":[{"given":"Yiyang","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"},{"name":"School of Basic Medical Science, Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]},{"given":"Yao","family":"Ai","sequence":"additional","affiliation":[{"name":"Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Yangyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]},{"given":"Long","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cixi Biomedical Research Institute, Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"software","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"given":"Yifan","family":"Yang","sequence":"additional","affiliation":[{"name":"Cixi Biomedical Research Institute, Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Wenping","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"}]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"}]},{"given":"Yimou","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, The Second Affiliated Hospital & Yuying Children\u2019s Hospital of Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Jianping","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People\u2019s Hospital, Quzhou 324000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Yan","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4117-5953","authenticated-orcid":false,"given":"Xiance","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Radiation and Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China"},{"name":"Cixi Biomedical Research Institute, Wenzhou Medical University, Wenzhou 325000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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