BRAVE模型可精准辅助乳腺病理诊断，提升临床效率与生存预测能力。
A Breast Vision Pathology Foundation Model for Real-world Clinical Utility
- 基于10万+张乳腺全切片图像训练，适配多场景病理分析
- 在三中心前瞻验证中排除超70%阴性病例，特异性达95%以上
- 支持临床全流程应用，显著提升医生诊断准确率与一致性
病理基础模型虽在回顾性研究中表现优异，但其在真实临床中的实用性尚不明确。本研究提出面向乳腺病理的BRAVE模型，基于来自亚欧北美32个来源的101,638张乳腺全切片图像进行开发与评估。在涵盖术前活检、术中冰冻及术后切除标本的82个队列中，BRAVE在34项任务上进行了系统验证，包括回顾性基准测试、临床挑战场景、流程导向影响模拟、前瞻性观察验证（阈值锁定于回顾队列）以及路径医师-人工智能交叉协作研究。结果显示，BRAVE可实现安全排除低风险病例、辅助复审漏诊阳性及优先分诊高危病例等实用功能。在三个中心的前瞻性验证中，其对阴性活检样本的排除率达76.9%（阴性预测值0.953），对阴性冰冻切片的排除率达70.1%（阴性预测值0.973），对术后分型病例有78.8%被识别为高置信度明确病例（阴性预测值1.000）。阅读者研究显示，AI辅助将平衡准确率从88.5%提升至95.1%（比值比3.14，P<0.001），且提高效率、信心和阅片者间一致性。此外，BRAVE衍生评分独立预测无病生存（校正后风险比4.79，P<0.001）与总生存（校正后风险比8.14，P<0.001）。
原文摘要 · Abstract (English)
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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