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Background <p>Cyclin-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.</p> Methods <p>This multicenter retrospective study included 1,008&#xa0;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.</p> Results <p>CDK4/6is 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, <i>P</i> &lt; 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.</p> Conclusion <p>CDK4/6is 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.</p>

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Machine learning and cox model–based prediction of CDK4/6 inhibitor outcomes in HR+/HER2 − metastatic breast cancer: a multicenter real-world study

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Binliang Liu,
  • Tao Wu,
  • Sijuan Ding,
  • Xuan Liu,
  • Xiaochun Zeng,
  • Ziru Liu,
  • Kaili Lu,
  • Jiantao She,
  • Jun Chen,
  • Heng Tian,
  • Qin Tong,
  • Ke Tang,
  • Juan Yu,
  • Jianguo Wang,
  • Li Ding,
  • Yan Li,
  • Liping Peng,
  • Qiang Zhou,
  • Huichong Zhou,
  • Tengfei Chao,
  • Quchang Ouyang,
  • Ning Xie

摘要

Background

Cyclin-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.

Methods

This 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.

Results

CDK4/6is 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.

Conclusion

CDK4/6is 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.