Host, reproductive, and lifestyle factors in relation to quantitative histologic metrics of the normal breast
Bibliographic record
Abstract
Abstract Background Emerging data indicate that variations in quantitative epithelial and stromal tissue composition and their relative abundance in benign breast biopsies independently impact risk of future invasive breast cancer. To gain further insights into breast cancer etiopathogenesis, we investigated associations between epidemiological factors and quantitative tissue composition metrics of the normal breast. Methods The study participants were 4108 healthy women ages 18–75 years who voluntarily donated breast tissue to the US-based Susan G. Komen Tissue Bank (KTB; 2008–2019). Using high-accuracy machine learning algorithms, we quantified the percentage of epithelial, stromal, adipose, and fibroglandular tissue, as well as the proportion of fibroglandular tissue that is epithelium relative to stroma (i.e., epithelium-to-stroma proportion, ESP) on digitized hematoxylin and eosin (H&E)-stained normal breast biopsy specimens. Data on epidemiological factors were obtained from participants using a detailed questionnaire administered at the time of tissue donation. Associations between epidemiological factors and square root transformed tissue metrics were investigated using multivariable linear regression models. Results With increasing age, the amount of stromal, epithelial, and fibroglandular tissue declined and adipose tissue increased, while that of ESP demonstrated a bimodal pattern. Several epidemiological factors were associated with individual tissue composition metrics, impacting ESP as a result. Compared with premenopausal women, postmenopausal women had lower ESP [β (95% Confidence Interval (CI)) = −0.28 (− 0.43, − 0.13); P < 0.001] with ESP peaks at 30–40 years and 60–70 years among pre- and postmenopausal women, respectively. Pregnancy [β (95%CI) vs nulligravid = 0.19 (0.08, 0.30); P < 0.001] and increasing number of live births (P-trend < 0.001) were positively associated with ESP, while breastfeeding was inversely associated with ESP [β (95%CI) vs no breastfeeding = −0.15 (− 0.29, − 0.01); P = 0.036]. A positive family history of breast cancer (FHBC) [β (95%CI) vs no FHBC = 0.14 (0.02–0.26); P = 0.02], being overweight or obese [β (95%CI) vs normal weight = 0.18 (0.06–0.30); P = 0.004 and 0.32 (0.21–0.44); P < 0.001, respectively], and Black race [β (95%CI) vs White = 0.12 (− 0.005, 0.25); P = 0.06] were positively associated with ESP. Conclusion Our findings revealed that cumulative exposure to etiological factors over the lifespan impacts normal breast tissue composition metrics, individually or jointly, to alter their dynamic equilibrium, with potential implications for breast cancer susceptibility and tumor etiologic heterogeneity.
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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 | 0.004 |
| 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 | 0.001 | 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".