Predicting ESWL success for ureteral stones: a radiomics-based machine learning approach.

Journal: BMC medical imaging
Published Date: Jul 4, 2025

Abstract

OBJECTIVES: This study aimed to develop and validate a machine learning (ML) model that integrates radiomics and conventional radiological features to predict the success of single-session extracorporeal shock wave lithotripsy (ESWL) for ureteral stones.

Authors

  • Ran Yang
    Radiology Department, Chongqing University Three Gorges Hospital, Chongqing 404000, People's Republic of China.
  • Dan Zhao
    Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, China.
  • Chunxue Ye
    Department of Radiology, Chongqing Western Hospital, No. 301, Huafu Avenue North, Jiulongpo District, Chongqing, 400050, China.
  • Ming Hu
    Department of Civil and Environmental Engineering and Earth Sciences, College of Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
  • Xiao Qi
    Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ, USA.
  • Zhichao Li
    School of Political Science and Public Administration, East China University of Political Science and Law, Shanghai 201620, China. 2863@ecupl.edu.cn.

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