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Customized SAM-Med3D With Multi-View Representation Fusion and Age-Grade Stratified Loss for Glioma Survival Risk

Xinyu Li, Hulin Kuang, Jin Liu

    IEEE Journal of Biomedical and Health Informatics
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    Summary

    SAM-Risk improves glioma survival risk prediction by fusing multi-view medical images and clinical data with a customized foundational model. This approach enhances personalized treatment strategies for brain tumors.

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    Area of Science:

    • * Artificial Intelligence in Medicine
    • * Medical Imaging Analysis
    • * Oncology

    Background:

    • * Accurate survival risk prediction is essential for personalized glioma treatment.
    • * Medical image foundational models offer potential for analyzing complex prognostic features.
    • * Existing methods may not fully leverage multimodal data or clinical knowledge.

    Purpose of the Study:

    • * To develop SAM-Risk, an advanced model for glioma survival risk prediction.
    • * To integrate multimodal MRI, radiomics, and clinical data for enhanced prognostic accuracy.
    • * To incorporate clinical knowledge through an age-grade stratified loss function.

    Main Methods:

    • * Developed a 3D representation generation module to transform 1D features into 3D representations.
    • * Implemented a multi-view representation fusion module to combine MRI and other features.
    • * Utilized a customized SAM-Med3D model, fine-tuned with LoRA, and a feature refinement module.
    • * Introduced an age-grade stratified loss function based on glioma prognosis standards.

    Main Results:

    • * SAM-Risk achieved a C-index of 75.08% on the UCSF-PDGM dataset.
    • * SAM-Risk achieved a C-index of 73.67% on the BraTS2020 dataset.
    • * The model outperformed several existing survival risk prediction methods.

    Conclusions:

    • * SAM-Risk effectively predicts glioma survival risk by integrating multimodal data and clinical knowledge.
    • * The proposed fusion strategy and stratified loss enhance model performance and clinical relevance.
    • * This approach holds promise for improving personalized treatment decisions in glioma patients.