Machine learning-based prognostic model for metastatic breast cancer and its interpretability: a multicenter retrospective study

The constructed RSF prognostic model demonstrated excellent predictive performance in MBC survival prediction and achieved good interpretability as confirmed by the SHAP analysis. These findings indicate that the developed model can facilitate prognostic assessment and promote the design of individualized treatments for MBC patients.

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Publication Details

Journal
Gland Surgery
Published
2025-12-01
DOI
https://doi.org/10.21037/gs-2025-362
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
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article

Machine learning-based prognostic model for metastatic breast cancer and its interpretability: a multicenter retrospective study

Sili Jin, Jie Wang, Kai Zhu, Jing-Hua Wu
Gland Surgery
Breast Cancer Treatment Studies
article

Machine learning-based prognostic model for metastatic breast cancer and its interpretability: a multicenter retrospective study

Sili Jin, Jie Wang, Kai Zhu, Jing-Hua Wu
article en

Abstract

The constructed RSF prognostic model demonstrated excellent predictive performance in MBC survival prediction and achieved good interpretability as confirmed by the SHAP analysis. These findings indicate that the developed model can facilitate prognostic assessment and promote the design of individualized treatments for MBC patients.

Gland SurgeryVol. 14(12)
Jiaxing University (CN), First Hospital of Jiaxing (CN)
Good health and well-being
Openalex Percentile: Top 33%
Breast Cancer Treatment Studies
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Machine learning-based prognostic model for metastatic breast cancer and its interpretability: a multicenter retrospective study — Sili Jin, Jie Wang, et al. · Gland Surgery (2025) | TGRS Research Map | TGRS