Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data
We have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. Among these models, the XGBoost model-derived from variables selected through multiple logistic regression-demonstrated the best performance. It exhibited high AUC values and average accuracy during internal validation, making it a recommended tool for clinical use. However, due to variations in time and context, influencing factors may change over time; thus, continuous evolution of the model is necessary. We propose a framework for this ongoing model adaptation.
Authors
- Yu Shen (ORCID: https://orcid.org/0000-0002-3899-7868)
- Liu‐Lin Chen
- Xiaolong Luo (ORCID: https://orcid.org/0000-0002-9939-2766)
- Zhen Meng (ORCID: https://orcid.org/0009-0004-2138-1743)
- Mei Yang (ORCID: https://orcid.org/0000-0002-6273-0370)
- Wanqi Wang (ORCID: https://orcid.org/0000-0001-7424-9165)
- Yufeng Wang (ORCID: https://orcid.org/0000-0002-4576-1984)
- Yuan He (ORCID: https://orcid.org/0000-0002-6461-5341)
- Li Feng (ORCID: https://orcid.org/0000-0003-4123-0892)
- Linhui Li (ORCID: https://orcid.org/0000-0002-0311-3786)
- Lirong Yang
- Zhijin Liu
Institutions
- Nanchang University (CN)
- Kunming Medical University (CN)
- Second People's Hospital of Yunnan Province (CN)
- Third Affiliated Hospital of Nanchang University (CN)
- Jiangxi Pingxiang People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-11-08
- DOI
- https://doi.org/10.3389/fonc.2024.1457294
- Citations
- 8
- Primary Topic
- Ovarian cancer diagnosis and treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.30
Funders
- Kunming Medical University