A Machine Learning Model for Predicting the HER2 Positive Expression of Breast Cancer Based on Clinicopathological and Imaging Features.

Journal: Academic radiology
Published Date: Jan 20, 2025

Abstract

RATIONALE AND OBJECTIVES: To develop a machine learning (ML) model based on clinicopathological and imaging features to predict the Human Epidermal Growth Factor Receptor 2 (HER2) positive expression (HER2-p) of breast cancer (BC), and to compare its performance with that of a logistic regression (LR) model.

Authors

  • Xiaojuan Qin
    College of Clinical Medicine, Ningxia Medical University, Yinchuan 750004, PR China (X.Q., X.Z.). Electronic address: QXJ2141326466@163.com.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Xiaoping Zhou
    College of Clinical Medicine, Ningxia Medical University, 692 Shengli Road, Yinchuan, 750004, People's Republic of China.
  • Yan Yang
    Department of Endocrinology and Metabolism, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
  • Ningmei Zhang
    Department of Pathology, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan, 750004, People's Republic of China.

Keywords

External Resources

Popular Topics
Recent Journals