21-Gene Assay and Breast Cancer Mortality in Ductal Carcinoma In Situ
Bibliographic record
Abstract
BACKGROUND: The inability to identify individuals with ductal carcinoma in situ (DCIS) who are at risk of breast cancer (BC) mortality have hampered efforts to reduce the overtreatment of DCIS. The 21-gene recurrence score (RS) predicts distant metastases for individuals with invasive BC, but its prognostic utility in DCIS is unknown. METHODS: We performed a population-based analysis of 1362 individuals of DCIS aged 75 years or younger at diagnosis treated with breast-conserving therapy. We examined the association between a high RS (defined a priori as >25) and the risk of BC mortality by using a propensity score-adjusted model accounting for the competing risk of death from other causes, testing for interactions. All statistical tests were 2-sided. RESULTS: With 16 years median follow-up, 36 (2.6%) died of BC, and 200 (14.7%) died of other causes. The median value of the RS was 15 (range = 0-84); 29.6% of individuals had a high RS. A high RS was associated with an 11-fold increased risk of BC mortality (hazard ratio = 11.27, 95% confidence interval [CI] = 3.00 to 42.33; P < .001) in women aged 50 years or younger at diagnosis treated with breast-conserving surgery alone, culminating in a 9.4% (95% CI = 2.3% to 22.5%) 20-year risk of BC death. For women with a high RS, treatment with radiotherapy was associated with a 71% (hazard ratio = 0.29, 95% CI = 0.10 to 0.89; P = .03) relative and a 5% absolute reduction in the 20-year cumulative risk of death from BC. CONCLUSION: The 21-gene RS predicts BC mortality in DCIS and combined with age (50 years or younger) at diagnosis can identify individuals for whom radiotherapy reduces the risk of death from BC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".