Machine Learning–Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2− Early Breast Cancer Using Real-World and NATALEE Data
Bibliographic record
Abstract
PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HER2- early breast cancer (EBC). Understanding individual recurrence risk would aid in clinical decision-making. We used machine learning to identify risk factors and develop recurrence risk prediction models. EXPERIMENTAL DESIGN: Predictor variables were identified by gradient boosting and used to train models on a large, diverse real-world dataset of patients with stage I-III HR+/HER2- EBC obtained from the US-based, electronic health record-derived deidentified Flatiron Health Research Database. An elastic net-penalized Cox proportional hazards model was validated internally with real-world data and externally with data from the NATALEE trial of ribociclib in patients with HR+/HER2- EBC. Prediction and outcome concordance for distant recurrence and treatment effect were analyzed with Harrell's concordance index (C-index) and integrated Brier score; model performance over time was determined by dynamic AUC analysis. RESULTS: The model accurately predicted distant recurrence in the real-world cohort [n = 7,842; C-index: 0.85 (95% confidence interval, 0.8461-0.8598); integrated Brier score: 0.05 (95% confidence interval, 0.0443-0.0495)] over time (AUC >0.7 through 10 years); internal validation and sensitivity analyses confirmed model performance. External validation with the NATALEE nonsteroidal aromatase inhibitor alone arm yielded a lower but still discriminative performance (C-index: 0.66). Training on NATALEE data improved concordance (C-index: 0.70); the NATALEE-trained model predicted a 3.2% reduction in distant recurrence at 48 months with ribociclib treatment in the real-world cohort. CONCLUSIONS: A machine learning model was developed that accurately predicted distant recurrence in HR+/HER2- EBC. The identified predictor variables and developed models may aid in risk-based personalized treatment decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".