Mammographic density and the risk of breast cancer recurrence after breast‐conserving surgery
Bibliographic record
Abstract
BACKGROUND: Women with invasive breast cancer who are treated with breast-conserving surgery and radiotherapy face a cumulative risk of local disease recurrence of approximately 10% at 10 years. To the authors' knowledge, the role of mammographic density as a risk factor for the development of local recurrence has not been thoroughly evaluated to date. METHODS: Medical records were reviewed for 335 patients who underwent breast-conserving surgery for invasive breast cancer and for whom a pretreatment mammogram was available. Information was recorded concerning mammographic density as well as tumor features, patient characteristics, and adjuvant treatments received. Patients were categorized for mammographic density based on the Wolfe classification as either low (<25% density), intermediate (25-50% density), or high (>50% density). A multivariate survival analysis was conducted using the Cox proportional hazards model with local disease recurrence as the primary endpoint. RESULTS: Patients in the high mammographic density group experienced a much greater risk of local disease recurrence compared with women with the least dense breasts (10-year actuarial risks: 21% vs 5%; hazards ratio [HR], 5.7 [95% confidence interval, 1.6-20; P=.006]). The difference in the rates of disease recurrence at 10 years was pronounced for women who did not receive radiotherapy (40% vs 0% for patients with >50% density and <25% density, respectively; P<.0001). CONCLUSIONS: Mammographic breast density is an important risk factor for local breast cancer recurrence among women not receiving breast irradiation. Mammographic density should be taken into consideration when stratifying patients for clinical trials of partial breast radiotherapy. If confirmed, mammographic density might be used to help determine which patients might benefit from radiotherapy.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".