Deep Learning MRI Models for the Differential Diagnosis of Tumefactive Demyelination versus<i>IDH</i>Wild-Type Glioblastoma
MRI can provide the basis for applying deep learning models to aid in the differential diagnosis of brain lesions. Further validation is needed to evaluate how well the model generalizes across institutions, patient populations, and technology, and to evaluate optimal thresholds for classification. Next steps also should incorporate additional tumor etiologies such as CNS lymphoma and brain metastases.
Authors
- Gian Marco Conte (ORCID: https://orcid.org/0000-0001-5926-7517)
- Terry C. Burns (ORCID: https://orcid.org/0000-0001-7633-3045)
- Bradley J. Erickson (ORCID: https://orcid.org/0000-0001-7926-6095)
- Jessica Sagen
- Francesca Guido
- Mana Moassefi (ORCID: https://orcid.org/0000-0002-0111-7791)
- Michael W. Ruff (ORCID: https://orcid.org/0000-0001-8513-9615)
- Christina B. McCarthy (ORCID: https://orcid.org/0000-0001-9725-5285)
- Daniel H. Lachance (ORCID: https://orcid.org/0000-0002-5475-9931)
- W. Oliver Tobin
- Yalda Nikanpour (ORCID: https://orcid.org/0009-0005-9430-5722)
- Paul A. Decker (ORCID: https://orcid.org/0000-0002-3756-4227)
- Mahboubeh Fereidan‐Esfahani
- Matthew L. Kosel
- Robert B. Jenkins
- Jeanette E. Eckel-Passow (ORCID: https://orcid.org/0009-0001-5176-6378)
- Heather K. Pump
Institutions
- Mayo Clinic (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- The University of Texas at Austin (US)
Publication Details
- Journal
- American Journal of Neuroradiology
- Published
- 2025-01-09
- DOI
- https://doi.org/10.3174/ajnr.a8645
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00