新辅助化疗（NAC）是局部晚期乳腺癌的标准治疗选择。然而，并非所有患者都会从NAC中受益，有些人甚至在治疗后获得更糟糕的结果。因此，治疗受益的预测因素对指导临床决策至关重要。在这里，我们通过基于深度学习（DL）的方法研究了乳腺癌间质组织学的预测潜力，提出了肿瘤相关间质分数（TS分数）用于预测NAC的病理完全缓解（pCR），并使用多中心数据集进行了验证。 TS分数被证明是pCR的独立预测因素，它不仅表现优于基线变量和肿瘤间质浸润淋巴细胞（sTILs），而且显着改善了基线变量模型的预测性能。此外，我们发现与淋巴细胞不同，间质中的胶原蛋白和成纤维细胞可能与对NAC的反应不佳相关。 TS分数有潜力更好地分层乳腺癌患者在NAC的设置中。© 2022. 作者（们）。

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potential of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multicenter dataset. The TS-score was demonstrated to be an independent predictor of pCR, and it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Furthermore, we discovered that unlike lymphocytes, collagen and fibroblasts in the stroma were likely associated with a poor response to NAC. The TS-score has the potential to better stratify breast cancer patients in NAC settings.© 2022. The Author(s).