Abstract 2779: Relationship of mammographic density with breast cancer subtypes
Bibliographic record
Abstract
Abstract Background: Mammographic density (MD), a measure of the amount of radiologically dense breast tissue, is one of the strongest breast cancer risk factors. Studies have found that high MD increases risk for both estrogen receptor (ER)-negative and positive breast cancers, but data relating MD to breast cancer subtypes are limited. Methods: We used data from the population-based Polish Breast Cancer Study to explore relationships between MD and breast cancer characteristics. This analysis includes 227 invasive breast cancers, which were available as frozen samples and used for mRNA and cytogenetic profiling, and fixed tissues, which were prepared as tissue microarrays for immunohistochemical analysis. Pre-treatment mammograms of the unaffected breast were retrieved for 184 (81%) cases ages 28-75 years. Craniocaudal views of digitized films were used to assess percent MD with Cumulus, a computer-assisted thresholding method. Analysis of variance (ANOVA) models were used to test the null hypothesis of no mean difference in MD between the breast cancer subtypes. Results: Tumor subtypes were classified by key immunohistochemical markers as luminal A (ER+ and/or progesterone receptor (PR)+, human epidermal growth factor receptor-2 (HER2)-; n=129), luminal B (ER+ and/or PR+, HER2+; n=6), HER2-expressing (ER-, PR-, HER2+; n=13), basal-like (ER-, PR-, HER2-, cytokeratin 5+, and/or HER1+; n=23), and unclassified (negative for all five markers; n=8). Five cases did not have interpretable staining for all five markers and were excluded. MD values were approximately normally distributed. Preliminary ANOVA models revealed no differences in mean percent MD by tumor subtypes (p=0.29) as follows: luminal A, 27.5% (95% confidence interval (CI): 24.9-30.2); luminal B, 27.0% (95% CI: 14.8-39.2); HER2-expressing, 27.2% (95% CI: 18.9-35.5); basal-like, 24.6% (95% CI: 18.4-30.8); and unclassified, 15.7% (95% CI: 5.2-26.3). In analysis of covariance models adjusted for age there was marginal evidence of elevated MD with the HER2-expressing subtype (p=0.04), but we cannot exclude the possibility that this is a chance finding. Conclusions: Our results do not provide strong support for an association between percent MD and breast cancer subtypes. To identify additional clues as to the biology underlying the MD-breast cancer relationship, ongoing analyses will relate MD to mRNA and cytogenetic profiles and data for protein expression via immunohistochemistry available for additional markers as previously reported. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2779.
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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 | 0.001 | 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) | 0.004 | 0.001 |
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".