{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:07:35Z","timestamp":1784012855023,"version":"3.55.0"},"reference-count":32,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T00:00:00Z","timestamp":1782950400000},"content-version":"vor","delay-in-days":1,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["624B2100"],"award-info":[{"award-number":["624B2100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MedComm"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Accurate outcome prediction is essential for personalized therapy in patients with breast cancer receiving neoadjuvant therapy (NAT). In this study, we developed and validated a multimodal artificial intelligence model, the ClinicHistomics Integrated Outcome Prediction Model (CIOPM), to predict prognosis in NAT\u2010treated breast cancer patients by integrating clinicopathological data and hematoxylin and eosin (H&amp;E)\u2010stained surgical specimen whole\u2010slide images (WSIs). A total of 847 WSIs from 835 patients across four multicenter cohorts collected between January 2008 and May 2020 were enrolled in model development and external validation. The CIOPM demonstrated robust predictive performance for overall survival (OS) and disease\u2010free survival (DFS) in the two validation cohorts (VCs), yielding C\u2010indices of 0.933 (95% CI: 0.878\u20130.977) and 0.915 (95% CI: 0.850\u20130.960) for OS in VC 1, and 0.947 (95% CI: 0.895\u20130.983) and 0.937 (95% CI: 0.905\u20130.965) for DFS in VC 2, respectively. The CIOPM enables accurate stratification of patients with NAT\u2010treated breast cancer into high\u2010 and low\u2010risk groups, achieving an AUC of 0.957 for both OS and DFS prediction. Moreover, it demonstrated superior performance in ablation studies, subgroup analyses, and clinical risk model comparisons. This model provides reliable risk stratification and personalized treatment decisions.<\/jats:p>","DOI":"10.1002\/mco2.70826","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T06:53:00Z","timestamp":1783061580000},"source":"Crossref","is-referenced-by-count":0,"title":["Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy\u2010Treated Breast Cancer: A Retrospective, Multicenter Study"],"prefix":"10.1002","volume":"7","author":[{"given":"Yani","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Pathology, West China Hospital Sichuan University  Chengdu Sichuan China"},{"name":"Institute of Clinical Pathology, West China Hospital Sichuan University  Chengdu Sichuan China"},{"name":"Department of Pathology, The First Affiliated Hospital Sun Yat\u2010sen University  Guangzhou Guangdong 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