Comparison of Random Survival Forest Based‐Overall Survival With Deep Learning and Cox Proportional Hazard Models in <scp>HER</scp>‐2‐Positive <scp>HR</scp>‐Negative Breast Cancer
In conclusion, compared to conventional prognostic models, the Random Survival Forest (RSF) model serves as a reliable tool for predicting long-term survival in breast cancer patients, demonstrating consistent performance across diverse datasets. Furthermore, the feature set selected via RSF-Variable Importance (VIMP) (compared with LASSO regression and Cox regression) significantly enhances the performance of prognostic models. Our findings may offer practical guidance for future development of long-term prognostic models tailored to breast cancer subtypes.
Authors
- Haijun Yu (ORCID: https://orcid.org/0000-0002-5346-7830)
- Wen-Qi Cai (ORCID: https://orcid.org/0000-0001-8460-7333)
- Runze Zhang (ORCID: https://orcid.org/0000-0001-9698-0178)
- Chunqian Yang
- Huachao Wu (ORCID: https://orcid.org/0000-0003-1225-6145)
- Yan Qi (ORCID: https://orcid.org/0000-0003-3963-6332)
- Chaoyan Wu
- Linhui Zheng
Institutions
- Zhongnan Hospital of Wuhan University (CN)
Publication Details
- Journal
- Cancer Reports
- Published
- 2025-07-01
- DOI
- https://doi.org/10.1002/cnr2.70262
- Citations
- 2
- Primary Topic
- Breast Cancer Treatment Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Hubei Province
- Wuhan University
- Zhongnan Hospital of Wuhan University