Deep learning-based radiomics and machine learning for prognostic assessment in IDH-wildtype glioblastoma after maximal safe surgical resection: a multicenter study.

Journal: International journal of surgery (London, England)
Published Date: May 20, 2025

Abstract

BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study aimed to construct and validate a radiomics-based machine learning model for predicting overall survival (OS) in IDH-wildtype GBM after maximal safe surgical resection using magnetic resonance imaging.

Authors

  • Jianpeng Liu
    Department of Radiology, Huashan Hospital, Fudan University, Shanghai.
  • Shufan Jiang
    Department of Pathology, School of Basic Medical Sciences, Fudan University, Shanghai.
  • Yanfei Wu
    Department of PET Centre, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.
  • Ruoyao Zou
    Gynaecological Oncology Department, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou.
  • Yifang Bao
    Department of Radiology, Huashan Hospital, Fudan University, Shanghai.
  • Na Wang
    College of Architecture and Civil Engineering, Xi'an University of Science and Technology Xi'an 710054 Shaanxi China wangna811221@xust.edu.cn +86-29-82202335 +86-29-82203378.
  • Jiaqi Tu
    Innovative Center for Flexible Devices (iFLEX), Max Planck - NTU Joint Lab for Artificial Senses, School of Materials Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.
  • Ji Xiong
    Department of Pathology, Huashan Hospital, Fudan University, 12 Wulumuqi Rd. Middle, Shanghai, 200040, China.
  • Ying Liu
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, China.
  • Yuxin Li
    University of Cincinnati, Department of Chemistry, 312 College Drive, 404 Crosley Tower, Cincinnati, Ohio 45221-0172, United States.

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