Advancing patient care: Machine learning models for predicting grade 3+ toxicities in gynecologic cancer patients treated with HDR brachytherapy
Machine learning models were developed to predict grade 3 or higher toxicities, achieving satisfactory performance. Machine learning presents a novel solution to creating multivariable models for personalized radiation therapy.
Authors
- Vishwa Shah (ORCID: https://orcid.org/0000-0002-0433-2737)
- Adrianna Masters (ORCID: https://orcid.org/0000-0001-7261-2477)
- Andrés Portocarrero-Bonifaz (ORCID: https://orcid.org/0000-0002-5928-3020)
- Salman Syed (ORCID: https://orcid.org/0000-0003-4478-7813)
- Scott R. Silva (ORCID: https://orcid.org/0000-0002-6028-3685)
- Maxwell Kassel
- Jeremy Gaskins (ORCID: https://orcid.org/0000-0002-6284-3404)
- Keith Sowards
- Grant McKenzie
- Jochen Schneider (ORCID: https://orcid.org/0000-0002-0300-0159)
- Bryce Forry
- Thulasi Babitha Avula (ORCID: https://orcid.org/0009-0006-6897-9712)
Institutions
- Loma Linda University Medical Center (US)
- University of Louisville (US)
- Loma Linda University (US)
- WinnMed (US)
- Mayo Clinic in Florida (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-05-14
- DOI
- https://doi.org/10.1371/journal.pone.0312208
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72