Estrogen and progesterone receptor levels in nonneoplastic breast epithelium of breast cancer cases versus benign breast biopsy controls
Bibliographic record
Abstract
BACKGROUND: Previous studies and biological mechanisms of carcinogenesis suggest that the steroid receptor content of benign breast epithelium may be related to breast cancer risk. The objective in this study was to compare the levels of estrogen receptor-alpha (ER) and progesterone receptor (PR) in nonneoplastic breast epithelium between breast cancer cases and biopsy controls. METHODS: Between 1995 and 1997 at two sites (Women's College Hospital in Toronto and Kingston General Hospital), 667 women who were scheduled for diagnostic excisional breast biopsies completed a questionnaire providing personal information and agreed to allow analysis of routinely resected tissue. Histological slides with nonneoplastic epithelium were available for 101 cancer cases and 200 biopsy controls in Toronto and for 105 cancer cases and 119 controls in Kingston. Nonneoplastic epithelium was examined with immunohistochemical assays to determine the percent of epithelial cells staining for ER and PR. Unconditional logistic regression was used to calculate odds ratios (OR) stratified by study site. RESULTS: The ER content of nonneoplastic tissue was higher in cases than biopsy controls in unadjusted analyses; after adjustment for age, however, a weak association remained in only one of the study sites. After adjustment for age, the PR content of nonneoplastic tissue was slightly lower in breast cancer cases than controls in one study site. Furthermore, this inverse association was confined to women with PR negative breast cancer in comparison to the controls. No interaction between ER and PR content of nonneoplastic tissue was observed in relation to the odds of having breast cancer. CONCLUSION: The results of this study are consistent with only a slight indication of increased ER levels in nonneoplastic tissue in breast cancer cases relative to controls. This study contributes to the understanding of breast cancer by examining both ER and PR in nonneoplastic tissue. Limitations remain, however, such as the necessity of using as controls women with benign breast changes, difficulties in selecting the appropriate tissue for analysis, and tissue sampling concurrent to diagnosis.
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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 | 0.000 |
| 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".