A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.

Journal: Neurosurgical focus
Published Date: Jul 1, 2025

Abstract

OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to construct a BSCM surgery outcome prediction model based on clinical characteristics and T2-weighted MRI-based radiomics.

Authors

  • Xuchen Dong
    1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
  • Haohuai Gui
    6School of Information Science and Technology, Fudan University, Shanghai.
  • Kai Quan
    1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
  • Zongze Li
    1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
  • Ying Xiao
    Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, USA.
  • Jiaxi Zhou
    6School of Information Science and Technology, Fudan University, Shanghai.
  • Yuchuan Zhao
    6School of Information Science and Technology, Fudan University, Shanghai.
  • Dongdong Wang
    Department of Radiology, Huashan Hospital Affiliated to Fudan University, 12 Wulumuqi Rd. Middle, Shanghai 200040, China.
  • Mingjian Liu
    1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
  • Haojing Duan
    9Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai.
  • Shaoxuan Yang
    1Department of Neurosurgery, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai.
  • Xiaolei Lin
    10Huashan Institute of Medicine, Huashan Hospital, Shanghai.
  • Jun Dong
    Suzhou Institute of Nanotech and Nanobionics, Chinese Academy of Sciences, Suzhou 215123, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Yu Ma
    Department of Electronic Engineering, Fudan University, Shanghai, China.
  • Wei Zhu
    The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou University of Chinese Medicine Guangzhou 510120 China zhuwei9201@163.com.

Keywords

External Resources

Popular Topics
Recent Journals