Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study
An explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.
Authors
- Botao Sun (ORCID: https://orcid.org/0000-0002-5866-8797)
- Zhuo Chen (ORCID: https://orcid.org/0009-0006-9778-1916)
- Jiashan Ding
- Yu Zhang (ORCID: https://orcid.org/0000-0002-9457-3162)
- Xinying Li (ORCID: https://orcid.org/0000-0001-8462-8919)
- Hui Ou‐Yang (ORCID: https://orcid.org/0000-0002-8676-8138)
Institutions
- Central South University (CN)
- National Clinical Research (US)
- Xiangya Hospital Central South University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1097/js9.0000000000002288
- Citations
- 9
- Primary Topic
- Ovarian cancer diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 15.28
Funders
- National Natural Science Foundation of China