A deep learning model to predict glioma recurrence using integrated genomic and clinical data
Our results demonstrate the potential of multimodal DL classifiers for predicting early glioma recurrence. By integrating clinical, mutational, and transcriptomic data from patients, LUNAR enables improved risk stratification. Its consistent performance across two independent datasets underscores its robustness.
Authors
- Seema Nagpal (ORCID: https://orcid.org/0000-0002-0289-2319)
- Ece D. Gamsiz Uzun (ORCID: https://orcid.org/0000-0003-4068-7153)
- Jeremy L. Warner (ORCID: https://orcid.org/0000-0002-2851-7242)
- Jessica Patricoski (ORCID: https://orcid.org/0000-0003-4149-4150)
- Ritambhara Singh (ORCID: https://orcid.org/0000-0002-7523-160X)
Institutions
- Palo Alto University (US)
- Brown University (US)
- Rhode Island Hospital (US)
- Stanford Medicine (US)
- Center for Cancer Research (US)
- Stanford University (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-08-19
- DOI
- https://doi.org/10.1038/s43856-025-01083-3
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Brown University