Logo image
Back
IMG-87. Differentiation of IDH-Wildtype Glioblastoma and Primary Central Nervous System Lymphoma Using 3D Deep Learning on MRI
Abstract   Open access   Peer reviewed

IMG-87. Differentiation of IDH-Wildtype Glioblastoma and Primary Central Nervous System Lymphoma Using 3D Deep Learning on MRI

Mana Moassefi, Gian Marco Conte, Paul Decker, Matthew Kosel, Michael Ruff, Terry Burns, Umar Farooq, Colin Derdeyn, Thomas Habermann, James Cerhan, …
Neuro-oncology (Charlottesville, Va.), Vol.27(Supplement_5), pp.v294-v294
11/11/2025
DOI: 10.1093/neuonc/noaf201.1166
PMCID: PMC12601527
url
https://doi.org/10.1093/neuonc/noaf201.1166View
Published (Version of record) Open Access

Abstract

Glioblastoma, IDH-wildtype (GBM), and primary central nervous system lymphoma (PCNSL) are two aggressive brain tumors with distinct treatment strategies but overlapping imaging features on MRI. Accurate preoperative differentiation remains a clinical challenge, due to their overlapping imaging features, even when advanced MRI techniques are employed. This study explored deep learning approaches leveraging multi-sequence MRI to distinguish GBM from PCNSL. Models were developed utilizing 146 PCNSL cases, which were matched to146 GBM by age, sex and year of diagnosis. A 3D-DenseNet121 convolutional neural network was trained using two analytical strategies: (i) loss-based model selection and (ii) ensemble prediction using five-fold cross-validation. Model performance was evaluated on an independent validation set of 256 GBM and 37 PCNSL from the same institution as well as 36 PCNSL cases from an external institution. All MRIs were preprocessed using the FeTS pipeline. Using the independent validation cohort, area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CI) were computed overall and stratified by demographic characteristics. Using both T1 post-contrast (T1Gd) and T2-weighted images, the loss-based model achieved AUC=0.83 (95% CI: 0.77-0.88) and the ensemble model yielded an AUC=0.82 (95% CI: 0.76-0.89). Model performance was similar across males and females; however, model performance was better for subjects older than 50 years of age versus younger than 50. When using only T1Gd images, the loss-based model dropped to AUC=0.55 (95% CI: 0.48-0.63), whereas the ensemble model had AUC=0.79 (95% CI: 0.72-0.85). Models trained with only T2 images achieved AUCs of 0.67 (95% CI: 0.60-0.73) (loss-based) and 0.75 (95% CI: 0.69-0.82) (ensemble). These results highlight the added value of the multi-sequence input. Our results confirm the feasibility of using automated deep learning models to differentiate GBM from PCNSL. Notably, the model generalized well to external MRI data, emphasizing its potential utility as a diagnostic aid in real-world clinical workflows.

Details

Metrics

8 Record Views
Logo image