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May 6, 2026Cancer Research CommunicationsOpen Access

MRI Deep Learning for Differentiating Glioblastoma, IDH-Wildtype from Central Nervous System Diffuse Large B-cell Lymphoma

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Authors

MMMana MoassefiPDPaul A. DeckerGCGian Marco Conte

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Overview

Deep learning demonstrates accurate differential diagnosis of glioblastoma in 239 patients, suggesting new imaging strategies.

Key Points

  • This research aims to utilize deep learning on MRI to differentiate between glioblastoma and CNS diffuse large B-cell lymphoma.
  • Used T1 post-contrast and T2-weighted MRI sequences for analysis.
  • Conducted a three-stage temporal study design with 146 matched patients for model development.
  • Tested models on independent cohorts and evaluated performance using AUC and cross validation.
  • Achieved AUC of 0.84 for the ensemble approach on the prospective test cohort.
  • Obtained AUC of 0.83 for the loss approach, indicating robust prediction performance.
  • Stratified AUC analysis showed consistent performance across sex and age.

Cite This Study

Moassefi et al. (2026) studied this question.

synapsesocial.com/papers/69faa2e204f884e66b533653https://doi.org/10.1158/2767-9764.crc-25-0710
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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. 2IMG-38. Differentiating adult diffuse IDH-mutant glioma from molecular glioblastoma IDH-wildtype using deep learning on MRI2025
  3. 3Radiomics-based differentiation between glioblastoma and primary central nervous system lymphoma: CT vs MRI2025
  4. 4IMG-47. How does deep learning/machine learning perform in comparison to radiologists in distinguishing glioblastomas (or grade IV astrocytomas) from primary CNS lymphomas?: a meta-analysis and systematic review2025
  5. 5Automatic segmentation and classification of GBM and SBM using a 3D deep learning model on multiparametric MRI: a multi-center study2025