{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:22:26Z","timestamp":1792790546830,"version":"4.5.0"},"reference-count":38,"publisher":"Wiley","issue":"15","license":[{"start":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T00:00:00Z","timestamp":1754265600000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100014418","name":"Harbin Medical University Cancer Hospital","doi-asserted-by":"publisher","award":["PDTS2024B\u201001"],"award-info":[{"award-number":["PDTS2024B\u201001"]}],"id":[{"id":"10.13039\/100014418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Purpose<\/jats:title>\n                    <jats:p>Prognostic stratification in non\u2010small cell lung cancer (NSCLC) presents considerable challenges due to tumor heterogeneity. Emerging evidence has proposed that adipose tissue may play a prognostic role in oncological outcomes. This study investigates the integration of deep learning (DL)\u2013derived computed tomography (CT) imaging biomarkers with mediastinal adiposity metrics to develop a multimodal prognostic model for postoperative survival prediction in NSCLC patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cohort of 702 surgically resected NSCLC patients was analyzed. Tumor radiomic features were extracted using a DenseNet121 convolutional neural network architecture, while mediastinal fat area (MFA) was quantified through semiautomated segmentation using ImageJ software. A multimodal survival prediction model was developed through feature\u2010level fusion of DL\u2010extracted tumor characteristics and MFA measurements. Model performance was evaluated using Harrell's concordance index (C\u2010index) and receiver operating characteristic (ROC) analysis. Risk stratification was performed using an optimal threshold derived from training data, with subsequent Kaplan\u2013Meier survival curve comparison between high\u2010 and low\u2010risk cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The DL\u2010based tumor model achieved C\u2010indices of 0.787 (95% CI: 0.742\u20130.832) for disease\u2010free survival (DFS) and 0.810 (95% CI: 0.768\u20130.852) for overall survival (OS) in internal validation. Integration of MFA with DL\u2010derived tumor features yielded a multimodal model demonstrating enhanced predictive performance, with C\u2010indices of 0.823 (OS) and 0.803 (DFS). Kaplan\u2013Meier analysis revealed significant survival divergence between risk\u2010stratified groups (log\u2010rank\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multimodal fusion of DL\u2010extracted tumor radiomics and mediastinal adiposity metrics represents a significant advancement in postoperative survival prediction for NSCLC patients, demonstrating superior prognostic capability compared to unimodal approaches.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71077","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T08:55:00Z","timestamp":1754297700000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multimodal Deep Learning Integrating Tumor Radiomics and Mediastinal Adiposity Improves Survival Prediction in Non\u2010Small Cell Lung Cancer: A Prognostic Modeling Study"],"prefix":"10.1002","volume":"14","author":[{"given":"Ye","family":"Niu","sequence":"first","affiliation":[{"name":"Department of Internal Medicine Harbin Medical University Cancer Hospital, Harbin Medical University  Harbin Heilongjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han\u2010bing","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine Harbin Medical University Cancer Hospital, Harbin Medical University  Harbin Heilongjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao\u2010bo","family":"Jia","sequence":"additional","affiliation":[{"name":"The School of Computer Science and Engineering University of Electronic Science and Technology of China  Chengdu Sichuan 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