Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.

Journal: BMC cancer
PMID: 40155850

Abstract

BACKGROUND: Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.

Authors

  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Peipei Zhang
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China.
  • Yanhui Feng
    Computer Center, Tongji Hospital, Tongji Medical College, Uazhong University of Science and Technology, Wuhan, China.
  • Wenzhi Lv
    Department of Artificial Intelligence, Julei Technology, Wuhan, China.
  • Xiangde Min
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China.
  • Zhiyong Liu
    State Key Laboratory of Respiratory Disease , Guangzhou Institutes of Biomedicine and Health (GIBH) , Chinese Academy of Sciences (CAS) , Guangzhou-510530 , China . Email: zhang_tianyu@gibh.ac.cn ; ; Tel: (+86)20 3201 5270.
  • Jin Li
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
  • Zhaoyan Feng
    Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, PR China.

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