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Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer

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收录情况： ◇ SCIE

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机构： [1]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China. [2]Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China. [3]Department of Pathology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China. [4]Department of Pathology, Sichuan Provincial People's Hospital, Chengdu, China. [5]Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

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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).

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出版当年[2022]版：

大类 | 2 区医学

小类 | 2 区肿瘤学

最新[2023]版：

大类 | 2 区医学

小类 | 2 区肿瘤学

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出版当年[2022]版：

Q1 ONCOLOGY

最新[2023]版：

Q1 ONCOLOGY

影响因子： 6.6 最新[2023版] 6.6 最新五年平均 5.9 出版当年[2022版] 6.5 出版当年五年平均 7.519 出版前一年[2021版] 6.6 出版后一年[2023版]

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第一作者机构： [1]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China. [2]Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China.

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通讯作者：

通讯机构： [1]Department of Pathology, West China Hospital, Sichuan University, Chengdu, China. [2]Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China.

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