Key result
ML model stratifies ER+/HER2- mBC patients on CDK4/6 inhibitors by an ~7-month progression-free survival difference.
Why the study?
To develop a machine learning model using clinical, genomic, and transcriptomic features to stratify patients based on response to first-line CDK4/6 inhibitor treatment and identify predictors of response.
Can an integrative machine learning model using clinical, genomic, and transcriptomic features predict real-world progression-free survival in patients with ER-positive/HER2-negative metastatic breast cancer treated with first-line CDK4/6 inhibitors?
Can an integrative machine learning model using clinical, genomic, and transcriptomic features predict real-world progression-free survival in patients with ER-positive/HER2-negative metastatic breast cancer treated with first-line CDK4/6 inhibitors?
An integrative machine learning model using multimodal real-world data successfully stratified patients with ER+/HER2- metastatic breast cancer into high- and low-risk groups for progression-free survival on first-line CDK4/6 inhibitors.
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ML stratification separates rwPFS on first-line CDK4/6 inhibitors; hypothesis-generating and requires prospective validation before clinical use.
Sanchez-Vega et al. (2026) studied this question. Integrative ML model stratified ER+/HER2- metastatic breast cancer patients on first-line CDK4/6 inhibitors into high-risk (median rwPFS 11.2m) and low-risk (18m) groups (p<0.001).
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