A Breast Vision Pathology Foundation Model for Real-world Clinical Utility

Yingxue Xu; Zhengyu Zhang; Xiuming Zhang; Mengwei Xu; Fengtao Zhou; Yihui Wang; Jiabo Ma; Yi Xin

cs.CVMay 6, 2026

A Breast Vision Pathology Foundation Model for Real-world Clinical Utility

Authors: Yingxue Xu, Zhengyu Zhang, Xiuming Zhang, Mengwei Xu, Fengtao Zhou, Yihui Wang, Jiabo Ma, Yi Xin, +27 more

Organizations: Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, China · Department of Pathology, Nanfang Hospital, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China · Department of Pathology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China · State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Department of Pathology, School of Basic Medicine and Xijing Hospital, Fourth Military Medical University, Xi’an, China · School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China · Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China · Department of Radiology, Qingdao University Hospital, Qingdao, China · Department of Ultrasound, Binzhou Medical University Hospital, Yantai, Shandong Province, China · Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China · Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, Guangdong, China · Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Hong Kong SAR, China · AI Thrust, Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, China · Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China · Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China · Faculty of Applied Sciences, Macao Polytechnic University, Macao, China · Department of Pathology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China · Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology, Hong Kong SAR, China · Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China · HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen, China · State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Hong Kong SAR, China

Abstract

Pathology foundation models have shown strong retrospective performance, but whether such systems can support clinically relevant use remains unclear. This challenge is particularly important in breast cancer, where pathological assessment serves as the gold standard for diagnosis and guides treatment planning, surgical decision-making and risk stratification across pre-, intra- and post-operative stages. Here we present \textbf{BRAVE}, a breast-adaptive pathology foundation model developed and evaluated using a total resource of 101,638 breast whole-slide images from 32 sources across Asia, Europe and North America. We assessed BRAVE across 34 tasks in 82 cohorts spanning pre-operative biopsy, intra-operative frozen section and post-operative resection, using an evidence chain comprising retrospective benchmarking, clinically challenging scenarios, workflow-oriented clinical impact simulations, prospective observational validation with the thresholds locked in the retrospective cohorts and crossover pathologist-AI interaction studies. Across these settings, BRAVE supported practical roles in the clinical workflow, including safe exclusion of low-risk cases from routine review, AI-assisted second-review rescue of initially missed positives and prioritization of cases for further assessment. In prospective validation across three centres, BRAVE excluded 76.9% of negative biopsy cases (NPV 0.953) and 70.1% of negative frozen-section cases (NPV 0.973), and triaged 78.8% of post-operative subtyping cases as high-confidence clear-cut cases (NPV 1.000). In reader studies, AI assistance improved balanced accuracy from 88.5% to 95.1% (OR 3.14, P<0.001), with better efficiency, confidence and inter-rater agreement. BRAVE-derived scores also independently predicted disease-free survival (adjusted HR 4.79, P<0.001) and overall survival (adjusted HR 8.14, P<0.001).

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