Diagnosing growth in low-grade gliomas with and without artificial intelligence-measured longitudinal volume measurements: A retrospective observational study
These findings highlight the clinical potential of AI-assisted volumetric analysis followed by physician oversight for the timely detection of tumor progression in LGG patients.
Authors
- Fabio Raman (ORCID: https://orcid.org/0000-0003-2393-9937)
- Nidhal Bouaynaya (ORCID: https://orcid.org/0000-0002-8833-8414)
- Jinsuh Kim (ORCID: https://orcid.org/0000-0002-4252-0229)
- Fanny Morón (ORCID: https://orcid.org/0000-0003-3745-2177)
- Houman Sotoudeh (ORCID: https://orcid.org/0000-0002-5510-7062)
- Hassan M. Fathallah‐Shaykh (ORCID: https://orcid.org/0000-0002-2690-7685)
- Markus Bredel
- Alex C. Whitley
- Hayat Rahal
Institutions
- Johns Hopkins University (US)
- University of Miami (US)
- Emory University (US)
- Baylor College of Medicine (US)
- Johns Hopkins Medicine (US)
- Emory University Hospital (US)
- University of Alabama at Birmingham (US)
- Alabama Department of Rehabilitation Services (US)
- Kazakh Eye Research Institute (KZ)
- Central Alabama Veterans Health Care System (US)
- Rowan University (US)
- The University of Texas Southwestern Medical Center (US)
Publication Details
- Journal
- Neuro-Oncology Advances
- Published
- 2026-01-01
- DOI
- https://doi.org/10.1093/noajnl/vdaf271
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00