Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

OBJECTIVE: This study aims to establish a machine learning prediction model to explore the correlation between contrast-enhanced mammography (CEM) imaging features and molecular subtypes of mass-type breast cancer.

Authors

  • Mengwei Ma
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, China.
  • Weimin Xu
    Institute of Agricultural Products Processing, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, PR China.
  • Jun Yang
    Cardiovascular Endocrinology Laboratory, Hudson Institute of Medical Research, Clayton, Victoria, Australia; Department of Medicine, Monash University, Clayton, Victoria, Australia.
  • Bowen Zheng
    Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Chanjuan Wen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
  • Sina Wang
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Zeyuan Xu
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, People's Republic of China.
  • Genggeng Qin
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Weiguo Chen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: chenweiguo1964@21cn.com.

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