Machine learning algorithms for individualized prediction of prognosis in breast cancer liver metastases and the prognostic impact of primary tumor surgery: a multicenter study
We developed and externally validated robust RF-based models for predicting OS and BCSS in BCLM. Our results indicate that PTS is associated with longer survival and lower breast cancer-specific mortality in carefully selected patients, supporting consideration within individualized, multidisciplinary decision-making.
Authors
- Qunchen Zhang (ORCID: https://orcid.org/0000-0001-8976-4071)
- Yan Dong (ORCID: https://orcid.org/0000-0003-0915-6223)
- Mingtao Shao
- Caihong Zhong (ORCID: https://orcid.org/0000-0003-2271-4138)
- Jundong Wu (ORCID: https://orcid.org/0000-0003-0607-8960)
- Ronggang Li (ORCID: https://orcid.org/0000-0001-7126-0182)
- Yutong Fang
- Qi Zhong (ORCID: https://orcid.org/0000-0003-2274-0838)
- Zhibing Yan
- Yong Li (ORCID: https://orcid.org/0000-0003-2808-2944)
- Weiwen Li (ORCID: https://orcid.org/0000-0002-3326-3056)
- Bo Xu (ORCID: https://orcid.org/0000-0001-7683-2514)
- Chunmei Chen (ORCID: https://orcid.org/0009-0000-2912-0856)
Institutions
- Shantou University (CN)
- Xinhui People's Hospital (CN)
- Cancer Hospital of Shantou University Medical College (CN)
- Guangzhou First People's Hospital (CN)
- Jiangmen Central Hospital (CN)
- First Affiliated Hospital of Jinan University (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- Frontiers in Endocrinology
- Published
- 2025-10-13
- DOI
- https://doi.org/10.3389/fendo.2025.1656191
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36