Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients
Our findings demonstrate that ensemble learning methods, particularly RF, exhibit superior performance in predicting HSPC progression. This study represents a preliminary step toward a predictive tool, highlighting the potential of baseline clinical data for risk stratification. Future prospective studies with larger, multi-center cohorts are warranted to validate and refine this approach for possible clinical integration.
Authors
- Bingyu Zhu (ORCID: https://orcid.org/0000-0002-7286-8955)
- Haiyang Jiang (ORCID: https://orcid.org/0000-0001-9508-0610)
- Lingtao Yan
- Libo Yang (ORCID: https://orcid.org/0009-0001-5160-6155)
- Yu Bai (ORCID: https://orcid.org/0000-0002-0253-4853)
- Yufeng Zheng (ORCID: https://orcid.org/0009-0004-4125-9446)
- ChongJian Zhang
- Yu Wang
- Qilin Wang
- Ruiqian Li
- Jun Li
- Xusong Pang
- Hong Yang
Institutions
- Kunming Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2026-02-12
- DOI
- https://doi.org/10.3389/fonc.2026.1704671
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China