Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.

Journal: European radiology
Published Date: Jun 1, 2024

Abstract

OBJECTIVES: To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs.

Authors

  • Lili Zhou
    School of Computer Science and Cyberspace Science, Xiangtan University, Xiangtan, 411105, China.
  • Qiang Ji
    Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University.
  • Hong Peng
    1 Center for Radio Administration and Technology Development, School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China.
  • Feng Chen
    Department of Integrated Care Management Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Yi Zheng
    Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
  • Zishan Jiao
    Capital Medical University, Beijing, China.
  • Jian Gong
    Estuarine and Coastal Environment Research Center, Chinese Research Academy of Environmental Sciences, Beijing, 100012, P. R. China.
  • Wenbin Li
    Key Laboratory of the plateau of environmental damage control, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou, China.

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