MRI-based radiomics model for the preoperative prediction of classification in children with venous malformations.

Journal: Clinical radiology
Published Date: May 22, 2025

Abstract

AIM: This study aimed to explore the efficacy of MRI-based radiomics models, employing various machine learning techniques, in the preoperative prediction of the digital subtraction angiography (DSA) classification of venous malformations (VMs).

Authors

  • B Jiao
    Department of Hemangioma and Interventional Vascular Surgery, Children's Hospital Affiliated to Shandong University. No. 23976, Jingshi Road, Jinan 250022, Shandong, China. Electronic address: jiaobx0525@163.com.
  • L Wang
    Institute of Organ Transplantation, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Key Laboratory of Ministry of Health, Key Laboratory of Ministry of Education, Wuhan, China.
  • X Zhang
    Agricultural and Rural Bureau of Hanjiang District, Yangzhou 225100, China.
  • Y Niu
    Department of Hemangioma and Interventional Vascular Surgery, Children's Hospital Affiliated to Shandong University. No. 23976, Jingshi Road, Jinan 250022, Shandong, China. Electronic address: niuyanli2007@126.com.
  • J Li
    Department of Pulmonary and Critical Care Medicine, Beijing Anzhen Hospital, Capital Medical University, Beijing 100029, China.
  • Z Liu
    School of Materials and Energy, Guangdong University of Technology, Guangzhou 510006, China.
  • D Song
    Department of Hemangioma and Interventional Vascular Surgery, Children's Hospital Affiliated to Shandong University. No. 23976, Jingshi Road, Jinan 250022, Shandong, China. Electronic address: songdan9966@163.com.
  • L Guo
    Department of Burns and Plastic Surgery, Xiangya Hospital, Central South University, Changsha 410008, China.

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