ArticleMedComm2026
Development and Validation of a Pathomics Model for Prognosis Prediction in Neoadjuvant Therapy-Treated Breast Cancer: A Retrospective, Multicenter Study.
Article in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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15 authors.
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Abstract
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-treated breast cancer patients by integrating clinicopathological data and hematoxylin and eosin (H&E)-stained surgical specimen whole-slide 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-free survival (DFS) in the two validation cohorts (VCs), yielding C-indices of 0.933 (95% CI: 0.878-0.977) and 0.915 (95% CI: 0.850-0.960) for OS in VC 1, and 0.947 (95% CI: 0.895-0.983) and 0.937 (95% CI: 0.905-0.965) for DFS in VC 2, respectively. The CIOPM enables accurate stratification of patients with NAT-treated breast cancer into high- and low-risk 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.
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