Validation of a breast cancer assay for radiotherapy omission: an individual participant data meta-analysis
Bibliographic record
Abstract
BACKGROUND: There are currently no molecular tests to identify individual breast cancers where radiotherapy (RT) offers no benefit. Profile for the Omission of Local Adjuvant Radiotherapy (POLAR) is a 16-gene molecular signature developed to identify low-risk cancers where RT will not further reduce recurrence rates. METHODS: An individual participant data meta-analysis was performed in 623 patients of node-negative estrogen receptor-positive and HER2-negative early breast cancer enrolled in 3 RT randomized trials for whom primary tumor material was available for analysis. A Cox proportional hazards model on time to locoregional recurrence was used to test the interaction between POLAR score and RT. RESULTS: A total of 429 (69%) patients' tumors had a high POLAR score, and 194 (31%) had a low score. Patients with high POLAR score had, in the absence of RT, a 10-year cumulative incidence of locoregional recurrence (20%, 95% confidence interval [CI] = 15% to 26%, vs 5%, [CI] 2% to 11%) for those with a low score. Patients with a high POLAR score had a large benefit from RT (hazard ratio [HR] for RT vs no RT = 0.37, 95% CI = 0.23 to 0.60; P < .001). In contrast, there was no evidence of benefit from RT for patients with a low POLAR score (HR = 0.92, 95% CI = 0.42 to 2.02; P = .832). The test for interaction between RT and POLAR was statistically significant (P = .022). CONCLUSIONS: POLAR is not only prognostic for locoregional recurrence but also predictive of benefit from RT in selected patients. Patients aged 50 years and older with estrogen receptor-positive and HER2-negative disease and a low POLAR score could consider omitting adjuvant RT. Further validation in contemporary clinical cohorts is required.
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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 | 0.083 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.052 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".