An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study.

Journal: Journal of translational medicine
Published Date:

Abstract

OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.

Authors

  • Yuxi Liu
    Beijing Key Laboratory for Green Catalysis and Separation, Key Laboratory of Beijing on Regional Air Pollution Control, Key Laboratory of Advanced Functional Materials, Education Ministry of China, Laboratory of Catalysis Chemistry and Nanoscience, Department of Environmental Chemical Engineering, School of Environmental and Chemical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
  • Yunfeng Wu
    School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
  • Qing Xia
  • Hao He
    School of Aerospace Engineering , Xiamen University , Xiamen 361005 , P. R. China.
  • Haining Yu
    Department of Ultrasound, Affiliated Hospital of Shandong Second Medical University, Shan Dong, Weifang, P.R. China.
  • Ying Che
    Department of Ultrasound, The First Affiliated Hospital of Dalian Medical University, Liaoning, Dalian, P.R. China. yche1964@163.com.