Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

Journal: Journal of translational medicine
Published Date: Aug 16, 2021

Abstract

BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance.

Authors

  • Fengling Li
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Yongquan Yang
    Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Yani Wei
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Ping He
    Shanghai Hospital Development Center, Shanghai 200040, China. Electronic address: heping@shdc.org.cn.
  • Jie Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Zhongxi Zheng
    Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China. digitalpathology@scu.edu.cn.
  • Hong Bu
    Laboratory of Pathology Key Laboratory of Transplant Engineering and Immunology NHC, West China Hospital Sichuan University Chengdu China.

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