用于准确预测乳腺癌复发的计算病理学：基于深度学习的工具的开发与验证
Computational Pathology for Accurate Prediction of Breast Cancer Recurrence: Development and Validation of a Deep Learning-based Tool
AI总结：
本研究提出了一种基于深度学习的计算病理学工具 Deep-BCR-Auto，通过常规 H&E 染色全切片图像预测乳腺癌复发风险，在两个独立队列中表现出优于现有弱监督模型的准确率与泛化能力，为临床提供了一种低成本、高可及性的预后评估方案。
摘要：
准确的复发风险分层对于优化乳腺癌患者的治疗方案至关重要。当前的预后工具如 Oncotype DX (ODX) 为 HR+/HER2- 患者提供了有价值的基因组见解，但受限于成本和可及性，特别是在服务不足的人群中。在本研究中，我们提出了 Deep-BCR-Auto，一种基于深度学习的计算病理学方法，可从常规 H&E 染色的全切片图像 (WSIs) 中预测乳腺癌复发风险。我们的方法在两个独立队列上进行了验证：TCGA-BRCA 数据集和来自俄亥俄州立大学 (OSU) 的内部数据集。Deep-BCR-Auto 在将患者分层为低复发和高复发风险类别方面表现出稳健的性能。在 TCGA-BRCA 数据集上，该模型的受试者工作特征曲线下面积 (AUROC) 达到 0.827，显著优于现有的弱监督模型 (p=0.041)。在独立的 OSU 数据集中，Deep-BCR-Auto 保持了强大的泛化能力，AUROC 达到 0.832，准确率为 82.0%，特异性为 85.0%，敏感性为 67.7%。这些发现突显了计算病理学作为复发风险评估的具有成本效益的替代方案的潜力，拓宽了获取个性化治疗策略的途径。本研究强调了将基于深度学习的计算病理学整合到常规病理评估中，用于不同临床环境下的乳腺癌预后的临床实用性。
英文摘要：
Accurate recurrence risk stratification is crucial for optimizing treatment plans for breast cancer patients. Current prognostic tools like Oncotype DX (ODX) offer valuable genomic insights for HR+/HER2- patients but are limited by cost and accessibility, particularly in underserved populations. In this study, we present Deep-BCR-Auto, a deep learning-based computational pathology approach that predicts breast cancer recurrence risk from routine H&E-stained whole slide images (WSIs). Our methodology was validated on two independent cohorts: the TCGA-BRCA dataset and an in-house dataset from The Ohio State University (OSU). Deep-BCR-Auto demonstrated robust performance in stratifying patients into low- and high-recurrence risk categories. On the TCGA-BRCA dataset, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.827, significantly outperforming existing weakly supervised models (p=0.041). In the independent OSU dataset, Deep-BCR-Auto maintained strong generalizability, achieving an AUROC of 0.832, along with 82.0% accuracy, 85.0% specificity, and 67.7% sensitivity. These findings highlight the potential of computational pathology as a cost-effective alternative for recurrence risk assessment, broadening access to personalized treatment strategies. This study underscores the clinical utility of integrating deep learning-based computational pathology into routine pathological assessment for breast cancer prognosis across diverse clinical settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
