AI融合病理图像与临床数据，提升乳腺癌复发风险预测准确率
Multi-modal AI for comprehensive breast cancer prognostication
- 用视觉变压器模型分析病理切片，结合临床信息做多模态预测
- 在5个队列中预测无病生存期的C指数达0.71，优于现有标准检测
- 对三阴性乳腺癌等难治类型仍有良好表现，适合临床决策参考
乳腺癌治疗选择依赖分子分型和临床特征，但现有工具如基因检测在精准度上仍不足。本文开发了一种基于人工智能的新方法，将数字化H&E染色切片的病理图像与临床数据融合，实现更可靠的复发风险预测。研究采用自监督学习训练的跨癌种视觉变压器基础模型提取病理图像特征，并与临床数据整合为多模态AI测试。该模型基于来自7个国家15个队列共8,161名女性患者的资料进行训练与评估，其中3,502名患者用于独立验证。在5个评估队列中，该测试对主要终点——无病生存期的预测表现优异（C-index: 0.71 [0.68-0.75]，HR: 3.63 [3.02-4.37, p<0.001]）。与标准21基因检测Oncotype DX直接对比（n=858）显示，其C-index为0.67 [0.61-0.74]，高于Oncotype DX的0.61 [0.49-0.73]。多变量分析表明，该AI测试可独立提供预后信息（HR: 3.11 [1.91-5.09, p<0.001]）。在主要分子亚型中均表现稳健，包括目前指南未推荐诊断工具的三阴性乳腺癌（C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]）。结果表明，该AI测试在准确性与适用范围上均优于现有方法。
原文摘要 · Abstract (English)
Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for optimal clinical decision-making. We developed a novel artificial intelligence (AI)-based approach that integrates digital pathology images with clinical data, providing a more robust and effective method for predicting the risk of cancer recurrence in breast cancer patients. Specifically, we utilized a vision transformer pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 female breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five evaluation cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.001]). In a direct comparison (n=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, achieving a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent prognostic information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.001)]). The test demonstrated robust accuracy across major molecular breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test improves upon the accuracy of existing prognostic tests, while being applicable to a wider range of patients.
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