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November 1, 2025Neuro-OncologyOpen Access

IMG-38. Differentiating adult diffuse IDH-mutant glioma from molecular glioblastoma IDH-wildtype using deep learning on MRI

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Authors

MMMana MoassefiGCGian-Marco ContePDPaul Decker

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Overview

Deep learning identifies MRI features in IDH-mutant glioma and molecular GBM, suggesting a need for refined models.

Key Points

  • This research aims to differentiate between IDH-mutant gliomas and molecular glioblastomas using MRI and deep learning techniques.
  • Retrospective analysis of 97 individuals with molecular GBM matched to 97 IDH-mutant gliomas.
  • Utilized postcontrast T1-weighted and T2-weighted MRI sequences to train a 3D-DenseNet121 model.
  • Validated on an independent prospective cohort of 150 IDH-mutant gliomas and 71 molecular GBM.
  • Achieved an AUC of 0.75 in the prospective validation set for distinguishing glioma types.
  • Sex-stratified AUC for females was 0.72 and for males was 0.76, indicating slight variability.
  • Age-stratified AUC showed 0.71 for individuals under 55 and 0.64 for those older than 55.

Cite This Study

Moassefi et al. (2025) studied this question.

synapsesocial.com/papers/6925435ec0ce034ddc35813bhttps://doi.org/10.1093/neuonc/noaf201.1117
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Also Consider

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

  1. 1IMG-87. Differentiation of IDH-Wildtype Glioblastoma and Primary Central Nervous System Lymphoma Using 3D Deep Learning on MRI2025
  2. 2108 Interpretable Radiomics-Based Prediction of IDH Status in Glioma2026
  3. 3MRI Deep Learning for Differentiating Glioblastoma, IDH-Wildtype from Central Nervous System Diffuse Large B-cell Lymphoma2026
  4. 4Integration of MRI radiomics and germline genetics to predict the IDH mutation status of gliomas2024
  5. 5MRI-Based Radiomics Approach with Deep Learning for Distinguishing IDH-Mutant from IDH Wild-type Grade-4 Astrocytomas2024