A Simplified Novel Algorithm to Predict the 21-Gene Recurrence Score
Bibliographic record
Abstract
Background: The 21-gene recurrence score (Oncotype DX) guides adjuvant chemotherapy decisions in early-stage estrogen receptor (ER)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer. However, its high cost and limited availability motivate the development of simplified predictive models using routinely reported pathology parameters. The aim of this study was to develop and validate a practical, rule-based algorithm that predicts Oncotype DX recurrence score (RS) category using only histologic grade and progesterone receptor (PR) expression percentage. Methods: early breast cancer who underwent Oncotype DX testing. Cases were randomly assigned to a learning (n = 377) and validation (n = 151) set. Univariate analysis and receiver operating characteristics (ROC) curves were used to determine PR% cut-offs within each histologic grade to stratify patients into low-risk (RS ≤ 25) or high-risk (RS > 25) categories. A stepwise algorithm was derived from these parameters and tested in the validation cohort. Results: Histologic grade and PR% were significantly associated with RS. Grade 1 tumors were uniformly low-risk regardless of PR%. In grade 2, PR ≥ 60% achieved 100% sensitivity for low RS; in grade 3, PR < 40% achieved 100% sensitivity for high risk. The algorithm confidently stratified ∼ 65% of cases. In the validation set, the model showed 87.5% sensitivity, 100% specificity, 100% positive predictive value (PPV), 99% negative predictive value (NPV), and 99% overall accuracy. Conclusion: This simplified algorithm accurately predicts Oncotype DX RS category using only histologic grade and PR%. It enables confident risk stratification in most patients without molecular testing, offering a low-cost, practical tool for clinical decision-making, particularly in resource-limited settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".