ArticleBreast cancer research and treatment2026
Machine learning and cox model-based prediction of CDK4/6 inhibitor outcomes in HR+/HER2 - metastatic breast cancer: a multicenter real-world study.
Article in Breast cancer research and treatment, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCyclin-dependent kinase 4/6 inhibitors (CDK4/6is) are standard therapy for HR+/HER2 - metastatic breast cancer (MBC), yet treatment outcomes vary substantially and individualized prognostic tools based on real-world data remain limited. This study evaluated real-world effectiveness and developed prognostic models integrating Cox regression and machine learning.
methodsThis multicenter retrospective study included 1,008 HR+/HER2 - MBC patients treated with CDK4/6is across 20 cancer centers in central China. Treatment patterns were analyzed in the overall cohort. PFS and prognostic factors were evaluated in patients receiving first- or second-line CDK4/6is using Kaplan-Meier and Cox regression analyses. These patients were randomly divided into training and validation cohorts (7:3). A Cox model and seven machine learning algorithms (GBM, RSF, Lasso-Cox, CoxBoost, XGBoost, SuperPC, and plsRcox) were developed and compared using time-dependent AUC, calibration, and decision curve analysis.
resultsCDK4/6is were used as first- and second-line therapy in 65.68% and 24.60% of patients. Median PFS was significantly longer in first-line versus second-line treatment (38.0 vs. 18.8 months, P < 0.001). Independent prognostic factors included Luminal B subtype, liver metastasis, and second-line treatment, while HER2 IHC 1 + and longer disease-free interval were favorable predictors. The Cox model showed good discrimination (AUCs: 0.731, 0.719, and 0.704). Among machine learning models, GBM and RSF showed higher discrimination in the training cohort but only moderate performance in the validation cohort.
conclusionCDK4/6is demonstrated substantial real-world effectiveness. Both Cox and machine learning models enabled individualized prognostic prediction. GBM and RSF showed relatively better predictive performance, but external validation is still required before clinical application.
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