Multi-parametric MRI-based machine learning model for prediction of pathological grade of renal injury in a rat kidney cold ischemia-reperfusion injury model.

Journal: BMC medical imaging
PMID: 39060984

Abstract

BACKGROUND: Renal cold ischemia-reperfusion injury (CIRI), a pathological process during kidney transplantation, may result in delayed graft function and negatively impact graft survival and function. There is a lack of an accurate and non-invasive tool for evaluating the degree of CIRI. Multi-parametric MRI has been widely used to detect and evaluate kidney injury. The machine learning algorithms introduced the opportunity to combine biomarkers from different MRI metrics into a single classifier.

Authors

  • Lihua Chen
    Department of Radiology, Southwest Hospital, Chongqing, China.
  • Yan Ren
    b Department of Traditional Chinese Medicine, College of Pharmacy , Southwest Minzu University , Chengdu , China.
  • Yizhong Yuan
    Department of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
  • Jipan Xu
    Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, No. 24 Fu Kang Road, Nan Kai District, Tianjin, 300192, China.
  • Baole Wen
    College of Medicine, Nankai University, Tianjin, 300350, China.
  • Shuangshuang Xie
    Department of Breast Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310029, P. R. China.
  • Jinxia Zhu
    Institute of Land and Urban-Rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, China.
  • Wenshuo Li
    School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.
  • Xiaoli Gong
    College of Computer Science, Nankai University, Tianjin, 300350, China.
  • Wen Shen

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