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January 29, 2024Neuro-OncologyOpen Access

Glioblastoma and radiotherapy: A multicenter AI study for Survival Predictions from MRI (GRASP study)

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Authors

ACAlysha ChelliahDWDavid WoodLCLiane S. Canas

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Overview

Randomized trial predicts glioblastoma survival using AI on MRI imaging, indicating patient stratification for treatment.

Key Points

  • This research aimed to predict survival of glioblastoma patients at 8 months post-radiotherapy using MRI data.
  • Retrospective and prospective data collected from 206 glioblastoma patients across 11 UK centers.
  • Neural networks were developed using both imaging (MRI) and nonimaging (demographics/treatment) features to predict survival.
  • Models were validated internally on 19 retrospective cases and externally on 29 prospective cases.
  • The imaging model had a performance of AUC 0.93, outperforming the nonimaging model with AUC 0.79 (P = .038).
  • Combining imaging and nonimaging models yielded an AUC of 0.91 (P > .05).
  • Pretraining the imaging model with data from 10,000s of MRIs improved performance significantly (P = .003).

Cite This Study

Chelliah et al. (2024) studied this question.

synapsesocial.com/papers/6a33c899cce949c3f40e7572https://doi.org/10.1093/neuonc/noae017
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multiscale Multiparametric <scp>MRI</scp> Deep Learning for Short‐Term Survival Assessment in Glioblastoma2026
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  3. 3A Systematic Review and Meta-Analysis of Survival Prediction in GlioblastomaPatients Using Advanced MRI Techniques2026
  4. 4Prediction of early recurrence of adult‐type diffuse gliomas following radiotherapy using multi‐modal magnetic resonance images2024 · 6 citations
  5. 5Glioblastoma survival prediction through MRI and clinical data integration with transfer learning2025 · 3 citations