Automated Machine Learning Differentiation of Pituitary Macroadenomas and Parasellar Meningiomas Using Preoperative Magnetic Resonance Imaging
Our customized AutoML model accurately differentiates PAs from PSMs using preoperative MRIs and outperforms traditional ML. It is the first AutoML model specifically trained for parasellar tumor classification. Its highly automated, user-friendly design may facilitate scalable integration into clinical practice.
Authors
- Chase Kahn (ORCID: https://orcid.org/0000-0003-0098-8533)
- Gurston Nyquist (ORCID: https://orcid.org/0009-0000-2905-7789)
- Mindy Rabinowitz
- Marc Rosen (ORCID: https://orcid.org/0000-0003-4476-0426)
- Elliott M. Sina (ORCID: https://orcid.org/0009-0004-8473-8965)
- Eric W. Tam (ORCID: https://orcid.org/0000-0003-0325-6573)
- Srihari Daggumati
- James J. Evans
- Kelsey Limage
- Emma Anisman
- Nickolas Pudik
Institutions
- Thomas Jefferson University (US)
Publication Details
- Journal
- Otolaryngology
- Published
- 2025-10-13
- DOI
- https://doi.org/10.1002/ohn.70034
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00