Omitting Radiotherapy after Breast-Conserving Surgery in Luminal A Breast Cancer: The LUMINA Study
Bibliographic record
Abstract
The modern generation of trials evaluating the role of adjuvant radiation have turned to genomic profiling as a further risk stratification tool. The LUMINA trial by Whelan and colleagues, published in the New England Journal of Medicine , applied Ki67 testing to identify those with luminal A disease and evaluated locoregional outcomes with breast-conserving surgery and endocrine therapy alone. This article was reviewed at the Canadian Association of General Surgeons' "Evidence-Based Reviews in Surgery" webinar series. Here, we present the Evidence-Based Reviews in Surgery panel's methodologic review and clinical commentary. The LUMINA study demonstrated very low rates of local recurrence in low-risk patients with luminal A biologic subtype treated with breast-conserving surgery and endocrine therapy alone without radiation. Although the LUMINA study was rigorously designed and executed, there are significant pragmatic limitations to the implementation of the proposed approach using their protocol. We advocate that there is no "one-size-fits-all" approach to early estrogen receptor + breast cancer. The choice of treatment strategy should strongly consider patient goals and preferences, with the need for incorporation of quality of life and patient-reported endpoints into future studies evaluating this population to help guide these nuanced decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".