Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy
The monitoring system effectively detected out-of-distribution outliers and clinical practice changes, providing a reliable framework for early detection of monthly performance degradation.
Authors
- Jean‐Claude M. Rwigema (ORCID: https://orcid.org/0000-0003-2180-1280)
- Nathan Y. Yu (ORCID: https://orcid.org/0000-0003-4059-650X)
- Quan Chen (ORCID: https://orcid.org/0000-0001-5570-2462)
- Sarah E. James (ORCID: https://orcid.org/0000-0002-2334-790X)
- Libing Zhu (ORCID: https://orcid.org/0000-0001-9780-0095)
- Jason Holmes (ORCID: https://orcid.org/0000-0001-8943-9471)
- Yi Rong (ORCID: https://orcid.org/0000-0002-2620-1893)
- Shang Lu
- Carlos Vargas (ORCID: https://orcid.org/0000-0002-1614-3562)
Institutions
- WinnMed (US)
- Mayo Clinic in Florida (US)
- California State University, Sacramento (US)
Publication Details
- Journal
- Physics and Imaging in Radiation Oncology
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1016/j.phro.2025.100886
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Cancer Institute