@article {Li2022.06.19.496741, author = {Fengling Li and Yongquan Yang and Yani Wei and Yuanyuan Zhao and Jing Fu and Xiuli Xiao and Zhongxi Zheng and Hong Bu}, title = {Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer}, elocation-id = {2022.06.19.496741}, year = {2022}, doi = {10.1101/2022.06.19.496741}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even get worse outcomes after therapy. Hence, predictors for treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potentials 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 multi-center dataset. The TS-score is demonstrated to to be an independent predictor of pCR as 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. Further, we discovered that unlike lymphocyte, collagen and fibroblasts in stroma were likely associated with poor response to NAC. The TS-score has potentials to be a candidate for better stratification of breast cancer patients in NAC settings.Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2022/06/21/2022.06.19.496741}, eprint = {https://www.biorxiv.org/content/early/2022/06/21/2022.06.19.496741.full.pdf}, journal = {bioRxiv} }