A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly
This ML model offers a more nuanced and potentially more accurate approach to identify patients more likely to develop recurrent or persistent acromegaly following primary ETS and require additional treatment. Following external validation, this ML model could improve personalized treatment planning and follow-up strategies and enhance patient care and resource allocation in clinical practice.
Authors
- Fredric B. Meyer (ORCID: https://orcid.org/0000-0002-4310-7962)
- Dana Erickson
- Sukwoo Hong (ORCID: https://orcid.org/0000-0002-3664-9245)
- Justine Herndon (ORCID: https://orcid.org/0000-0001-5665-0617)
- Ramin A. Morshed (ORCID: https://orcid.org/0000-0001-8464-6041)
- Abdul Karim Ghaith (ORCID: https://orcid.org/0000-0002-9600-609X)
- Irina Bancos (ORCID: https://orcid.org/0000-0001-9332-2524)
- Miguel Sáez-Alegre (ORCID: https://orcid.org/0000-0002-3903-2726)
- Jamie J. Van Gompel (ORCID: https://orcid.org/0000-0001-8087-7870)
- Sandhya Rupa Palit (ORCID: https://orcid.org/0009-0001-4639-2148)
- Yuki Shinya (ORCID: https://orcid.org/0000-0003-0097-5804)
- Carlos Pinheiro‐Neto (ORCID: https://orcid.org/0000-0003-3921-4658)
- John L. D. Atkinson
Institutions
- Mayo Clinic (US)
- University of Tokyo Hospital (JP)
Publication Details
- Journal
- Neurosurgical FOCUS
- Published
- 2025-07-01
- DOI
- https://doi.org/10.3171/2025.4.focus2597
- Primary Topic
- Pituitary Gland Disorders and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00