Development and validation of an interpretable machine learning model for diagnosing pathologic complete response in breast cancer.

Journal: Computer methods and programs in biomedicine
Published Date: Apr 23, 2025

Abstract

BACKGROUND: Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critical prognostic marker for patients with breast cancer, potentially allowing surgery omission. However, noninvasive and accurate pCR diagnosis remains a significant challenge due to the limitations of current imaging techniques, particularly in cases where tumors completely disappear post-NACT.

Authors

  • Qi Zhou
  • Fei Peng
    College of Food Science and Engineering, Northwest A&F University, Yangling 712100, China.
  • Zhiyuan Pang
    Department of Breast Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China.
  • Ruichun He
    Department of Radiology, Tangshan People's Hospital, Tangshan, Hebei, China.
  • Haiping Zhang
    Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Key Laboratory of Oilseeds processing, Ministry of Agriculture, Oil Crops and Lipids Process Technology National & Local Joint Engineering Laboratory, Hubei Key Laboratory of Lipid Chemistry and Nutrition, Wuhan 430062, China.
  • Xiaoman Jiang
    Department of Breast Surgery, The Second Hospital of Jilin University, Changchun, Jilin, China.
  • Jian Song
    School of International Studies, Sun Yat-sen University, Guangzhou, China.
  • Jingwu Li
    Cancer Institute, Tangshan People's Hospital, Tangshan 063001, China.

Keywords

External Resources

Popular Topics
Recent Journals