AI-powered prediction model for neoadjuvant chemotherapy efficacy: comprehensive analysis of breast cancer histological images.

Journal: NPJ precision oncology
Published Date: Jul 15, 2025

Abstract

Breast cancer patients exhibit variable responses to neoadjuvant therapy (NAT), necessitating robust predictive biomarkers. We developed an artificial intelligence (AI)-driven integrated predictive model (IPM) combining histopathological, clinical, and immune features to address this challenge. Using whole-slide images from 1035 patients across four centers, we compared tumor epithelium (TE-score), stroma (TS-score), and whole-tumor (TR-score) deep learning biomarkers, identifying TR-score as optimal (AUC = 0.729 vs. 0.686/0.719 for TE/TS-scores). The IPM, incorporating TR-score and clinical variables, demonstrated superior NAT response prediction versus clinicopathological models (validation AUC = 0.780 vs. 0.706, p < 0.001), with 10% higher accuracy. Immune profiling further enhanced performance (AUC = 0.831 vs. 0.822, p = 0.183). These results establish the biological and clinical validity of TR-score for characterizing tumor-stroma interactions, with IPM providing a generalizable framework for precision oncology. The model's stability across multicenter cohorts (AUCs 0.781-0.816) and incremental value of immune data suggest its utility in guiding NAT decision-making and trial stratification.

Authors

  • Fengling Li
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Yani Wei
    Department of Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Wenchuan Zhang
    Chang Jiang Survey, Planning, Design and Research CO., LTD., Wuhan, China.
  • Yuanyuan Zhao
    Department of Medical Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
  • Jing Fu
    Shaoxing Second Hospital, 123 Yanan Road, Shaoxing, Zhejiang 312000, PR China.
  • Xiuli Xiao
    Department of Pathology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
  • Yan Qiu
    Clinical Research Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
  • Yuhao Yi
    Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, China. yuhaoyi@scu.edu.cn.
  • Yongquan Yang
    Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, China.
  • Hong Bu
    Laboratory of Pathology Key Laboratory of Transplant Engineering and Immunology NHC, West China Hospital Sichuan University Chengdu China.

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