Triple-negative breast cancer (TNBC) remains a major clinical challenge due to its aggressive behavior and lack of targeted therapies. Accurate early prediction of response to neoadjuvant chemotherapy (NACT) is essential for guiding personalized treatment strategies and improving patient outcomes. In this study, we present an attention-based multiple instance learning (MIL) framework designed to predict pathologic complete response (pCR) directly from pre-treatment hematoxylin and eosin (H&E)-stained biopsy slides. The model was trained on a retrospective in-house cohort of 174 TNBC patients and externally validated on an independent cohort (n = 30). It achieved a mean area under the curve (AUC) of 0.85 during five-fold cross-validation and 0.78 on external testing, demonstrating robust predictive performance and generalizability. To enhance model interpretability, attention maps were spatially co-registered with multiplex immunohistochemistry (mIHC) data stained for PD-L1, CD8+ T cells, and CD163+ macrophages. The attention regions exhibited moderate spatial overlap with immune-enriched areas, with mean Intersection over Union (IoU) scores of 0.47 for PD-L1, 0.45 for CD8+ T cells, and 0.46 for CD163+ macrophages. The presence of these biomarkers in high-attention regions supports their biological relevance to NACT response in TNBC. This not only improves model interpretability but may also inform future efforts to identify clinically actionable histological biomarkers directly from H&E-stained biopsy slides, further supporting the utility of this approach for accurate NACT response prediction and advancing precision oncology in TNBC.
三阴性乳腺癌（TNBC）因其侵袭性强且缺乏靶向治疗手段，仍是临床面临的主要挑战。准确早期预测新辅助化疗（NACT）疗效对于指导个体化治疗策略、改善患者预后至关重要。本研究提出一种基于注意力机制的多示例学习（MIL）框架，旨在直接利用治疗前苏木精-伊红（H&E）染色活检切片预测病理完全缓解（pCR）。该模型基于174例TNBC患者的回顾性内部队列进行训练，并在独立队列（n=30）中进行外部验证。在五折交叉验证中平均曲线下面积（AUC）达0.85，外部测试集达0.78，显示出稳健的预测性能与泛化能力。为增强模型可解释性，研究将注意力图与多重免疫组化（mIHC）检测的PD-L1、CD8+ T细胞及CD163+巨噬细胞表达数据进行空间配准。注意力区域与免疫富集区呈现中度空间重叠，其平均交并比（IoU）分别为PD-L1（0.47）、CD8+ T细胞（0.45）和CD163+巨噬细胞（0.46）。这些生物标志物在高注意力区域的分布证实了其与TNBC新辅助化疗反应的生物学关联性。该研究不仅提升了模型可解释性，也为未来直接从H&E染色切片中识别具有临床指导价值的组织学生物标志物提供了新思路，进一步验证了该方法在精准预测TNBC新辅助化疗疗效、推动精准肿瘤学发展方面的应用潜力。
肿瘤(癌症)患者之家