Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

Journal: Journal of vascular surgery. Venous and lymphatic disorders
PMID: 39694463

Abstract

OBJECTIVE: According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method of measuring volume is time consuming, laborious, and has certain errors in clinical applications. Therefore, this study aims to explore whether machine learning radiomics features based on noncontrast magnetic resonance imaging (MRI) can predict the severity of primary lower limb lymphedema.

Authors

  • Jie Ren
    Digital Clinical Measures, Translational Medicine, Merck & Co., Inc., Rahway, NJ, United States.
  • Xingpeng Li
    Department of Radiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Mengke Liu
    Department of Radiology, Affiliated Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, Shandong, China.
  • Tingting Cui
    Department of Virology, Parasitology and Immunology, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, B-9820, Merelbeke, Belgium. Electronic address: tingting.cui@ugent.be.
  • Jia Guo
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Rongjie Zhou
    Department of MRI, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Kun Hao
    School of Computer and Information Engineering, Tianjin Chengjian University, Tianjin 300384, China.
  • Rengui Wang
    Department of Radiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
  • Yunlong Yue
    Department of MR, Beijing Shijitan Hospital, Capital Medical University/Peking University, Ninth Clinical Medical College, Beijing 100038, China.

Keywords

External Resources

Popular Topics
Recent Journals