Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.

Journal: Annals of medicine
Published Date: Aug 3, 2025

Abstract

BACKGROUND: Most models of neoadjuvant chemotherapy (NACT) for breast cancer (BC) suffer from insufficient data and lack interpretability. Additionally, there is a notable absence of reports from China in this field. This study is also the first to integrate the Advanced Lung Cancer Inflammation Index (ALI) into such a model to evaluate its effectiveness.

Authors

  • Quan Yuan
    School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
  • Rongjie Ye
    Department of Orthopaedics, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China.
  • Yao Qian
    Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150000, Heilongjiang, China.
  • Hao Yu
    Shanghai Key Lab of Trustworthy Computing, East China Normal University, Shanghai, China.
  • Yuexin Zhou
    Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
  • Xiaoqiao Cui
    Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
  • Feng Liu
    Department of Vascular and Endovascular Surgery, The First Medical Center of Chinese PLA General Hospital, 100853 Beijing, China.
  • Ming Niu
    Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, 150000, Heilongjiang, China. niuming2024@126.com.

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