用病理切片自动预测乳腺癌复发风险，准确率达76.2%。
Breast Cancer Recurrence Risk Prediction Based on Multiple Instance Learning
- 基于多实例学习框架分析常规病理切片，提取深度特征
- 在210例患者数据上实现0.836的平均AUC和76.2%准确率
- 可为临床提供快速低成本的复发风险分层工具
预测乳腺癌复发风险是重要的临床挑战。本研究探索了计算病理学在常规苏木精-伊红染色全切片图像（WSI）上应用深度学习进行患者分层的潜力。在自建的210例患者数据集上，对比了三种多实例学习（MIL）框架——CLAM-SB、ABMIL 和 ConvNeXt-MIL-XGBoost，目标是预测5年复发风险，分为低、中、高三个等级，标签由21基因复发评分确定。使用UNI和CONCH预训练模型提取特征。在五折交叉验证中，改进版CLAM-SB模型表现最佳，平均曲线下面积（AUC）达0.836，分类准确率为76.2%。结果表明，基于标准病理切片的深度学习可用于自动化、与基因组相关联的风险分层，为快速、低成本的临床决策支持提供可行路径。
原文摘要 · Abstract (English)
Predicting breast cancer recurrence risk is a critical clinical challenge. This study investigates the potential of computational pathology to stratify patients using deep learning on routine Hematoxylin and Eosin (H&E) stained whole-slide images (WSIs). We developed and compared three Multiple Instance Learning (MIL) frameworks -- CLAM-SB, ABMIL, and ConvNeXt-MIL-XGBoost -- on an in-house dataset of 210 patient cases. The models were trained to predict 5-year recurrence risk, categorized into three tiers (low, medium, high), with ground truth labels established by the 21-gene Recurrence Score. Features were extracted using the UNI and CONCH pre-trained models. In a 5-fold cross-validation, the modified CLAM-SB model demonstrated the strongest performance, achieving a mean Area Under the Curve (AUC) of 0.836 and a classification accuracy of 76.2%. Our findings demonstrate the feasibility of using deep learning on standard histology slides for automated, genomics-correlated risk stratification, highlighting a promising pathway toward rapid and cost-effective clinical decision support.
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