Contribution of Prediagnostic Host Factors to Shaping the Stromal Microenvironment of Breast Cancer among Sub-Saharan African Women
Bibliographic record
Abstract
BACKGROUND: The stromal microenvironment (SME) is integral to breast cancer biology, impacting metastatic proclivity and treatment response. Emerging data indicate that host factors may impact the SME, but the relationship between prediagnostic host factors and SME phenotype remains poorly characterized, particularly among women of African ancestry. METHODS: We conducted a case-only analysis involving 792 patients with breast cancer (17-84 years) from the Ghana Breast Health Study. High-accuracy machine-learning algorithms were applied to standard H&E-stained images to characterize SME phenotypes [including percent tumor-associated connective tissue stroma, Ta-CTS (%); tumor-associated stromal cellular density, Ta-SCD (%)]. Associations between prediagnostic host factors and SME phenotypes were assessed in multivariable linear regression models. RESULTS: Decreasing Ta-CTS and increasing Ta-SCD were associated with aggressive, mostly high-grade tumors (P-value < 0.001). Several prediagnostic host factors were associated with Ta-SCD independently of tumor characteristics. Compared with nulliparous women, parous women had higher levels of Ta-SCD [mean (standard deviation, SD) = 31.3% (7.6%) vs. 28.9% (7.1%); P-value = 0.01]. Similarly, women with a positive family history of breast cancer had higher levels of Ta-SCD than those without family history [mean (SD) = 33.0% (7.5%)] vs. 30.9% (7.6%); P-value = 0.03]. Conversely, increasing body size was associated with decreasing Ta-SCD [mean (SD) = 31.6% (7.4%), 31.4% (7.3%), and 30.1% (8.0%) for slight, average, and large body sizes, respectively; P-value = 0.005]. CONCLUSIONS: Epidemiological risk factors were associated with varying degrees of stromal cellularity in tumors, independently of clinicopathological characteristics. IMPACT: The findings raise the possibility that epidemiological risk factors may partly influence tumor biology via the stromal microenvironment. See related In the Spotlight, p. 459.
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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) | 0.001 | 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".