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Updated: Jun 27, 2026

Glioblastoma Relapse Post-Resection Model for Therapeutic Hydrogel Investigations
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Predicting Recurrence Risk of Glioblastoma Based on Preoperative-Postoperative Longitudinal MRI: A Multicenter Study.

Chengwei Chen1, Fan Guo2, Dong Huang1,3,4

  • 1School of Biomedical Engineering, Air Force Medical University, No. 169 Changle West Road, Xi'an 710032, China.

Bioengineering (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

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This study introduces MambaDiff-Net, a deep learning model using longitudinal MRI scans to predict glioblastoma recurrence risk after surgery. It accurately identifies high-risk patients, enabling personalized treatment strategies.

Area of Science:

  • Neuro-oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glioblastoma recurrence is common, and current imaging methods struggle to track tumor changes over time.
  • Predicting recurrence risk is crucial for effective patient management and treatment planning.

Purpose of the Study:

  • To develop and validate a deep learning model, MambaDiff-Net, for predicting glioblastoma recurrence risk using longitudinal MRI data.
  • To capture dynamic changes in tumor imaging before and after surgery to improve recurrence prediction.

Main Methods:

  • A dual-stream encoder architecture (MambaDiff-Net) was used to analyze preoperative and postoperative T2WI MRI scans.
  • A feature discrepancy module modeled longitudinal imaging changes to calculate recurrence risk probabilities.
Keywords:
brain tumorglioblastomamagnetic resonance imagingpreoperative-postoperative longitudinal analysisrecurrence risk prediction

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  • The model was trained and validated on 139 glioblastoma patient datasets, with recurrence within 6 months as the endpoint.

    • Main Results:

    • MambaDiff-Net achieved high performance with AUCs of 0.887 (internal) and 0.762 (external validation).
    • The model significantly outperformed single-time-point imaging approaches in predicting recurrence.
    • Kaplan-Meier analysis and decision curve analysis confirmed the model's effective risk stratification and clinical utility.

    Conclusions:

    • Longitudinal MRI analysis with deep learning, specifically MambaDiff-Net, can accurately predict postoperative glioblastoma recurrence risk.
    • The model's ability to track imaging dynamics supports individualized treatment decisions for glioblastoma patients.
    • This approach enhances clinical decision-making by providing dynamic, personalized recurrence risk assessments.