Association between Breast Density and Subsequent Breast Cancer Following Treatment for Ductal Carcinoma <i>In situ</i>
Bibliographic record
Abstract
BACKGROUND: Risk of invasive cancer following treatment for ductal carcinoma in situ (DCIS) is associated with both treatment- and tumor-related factors. However, it is unknown whether stromal factors such as breast density may also influence subsequent invasive breast events. We investigated whether breast density is an independent predictor of subsequent breast events among women treated for DCIS. POPULATION: A prospective cohort study of 3,274 women ages 30 to 93 in the Breast Cancer Surveillance Consortium treated with lumpectomy for DCIS between 1993 and 2005. All subjects had an American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) breast density measure recorded prior to diagnosis. METHODS: Ipsilateral and contralateral breast cancer following lumpectomy for DCIS were ascertained through state tumor registries, regional Surveillance Epidemiology and End Results program or pathology databases. A Cox proportional hazard model was used to compare adjusted risk of breast cancer among women with high (BI-RADS 3 or 4) versus low (BI-RADS 1 or 2) breast density. RESULTS: During a median follow-up period of 39 months (0-132 months), 133 women developed invasive breast cancer. After adjusting for age and radiation treatment, high breast density was associated with increased hazard for contralateral (hazard ratio, 3.1; 95% confidence interval, 1.6-6.1) but not ipsilateral (hazard ratio, 1.0; 95% confidence interval, 0.6-1.6) invasive breast events. CONCLUSION: High breast density is associated with contralateral, but not ipsilateral, invasive breast cancer following lumpectomy for DCIS. Thus, women with DCIS and high breast density may especially benefit from antiestrogenic therapy to reduce the risk of contralateral invasive disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".