AI-driven multi-modal framework for prognostic modeling in glioblastoma: Enhancing clinical decision support.

Journal: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date: Aug 7, 2025

Abstract

OBJECTIVE: Glioblastoma (GBM) is the most aggressive malignant brain tumor, associated with poor prognosis and limited therapeutic options. Accurate prognostic modeling is essential for guiding personalized treatment strategies. However, existing models often rely on single-modality data, limiting their ability to capture the complex molecular and histopathological heterogeneity of GBM. This study proposes an AI-driven, multi-modal framework for clinical decision support, encompassing both early triage and prognostic evaluation stages. A Vision Transformer (ViT) is first employed to classify tumor grades (WHO grades 2-4) using radiological images. Subsequently, an attention-based deep learning model integrates histopathological and transcriptomic data to improve risk stratification and inform treatment planning.

Authors

  • Zihan Zhao
    Department of Urology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Institute of Urology, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
  • Nguyen Quoc Khanh Le
    In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan; AIBioMed Research Group, Taipei Medical University, Taipei 110, Taiwan; Translational Imaging Research Center, Taipei Medical University Hospital, Taipei 110, Taiwan. Electronic address: khanhlee@tmu.edu.tw.
  • Matthew Chin Heng Chua
    Institute of Systems Science, National University of Singapore, 29 Heng Mui Keng Terrace, Singapore 119620, Singapore.

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