Obesity and Breast Cancer: Interaction or Interference with the Response to Therapy?
Bibliographic record
Abstract
Background: Aromatase inhibitors (AI) are widely used for treating hormone-sensitive breast cancer (BC). Obesity, however, due to aromatase-mediated androgen conversion into estradiol in the peripheral adipose tissue, might impair AI inhibitory capacity. We aimed at identifying a cut-off of body mass index (BMI) with significant prognostic impact, in a cohort of stage I-II BC patients on systemic adjuvant therapy with AI. Methods: we retrospectively evaluated routinely collected baseline parameters. The optimal BMI cut-off affecting disease-free survival (DFS) in AI-treated BC patients was identified through maximally selected rank statistics; non-linear association between BMI and DFS in the AI cohort was assessed by hazard-ratio-smoothed curve analysis using BMI as continuous variable. The impact of the BMI cut-off on survival outcomes was estimated through Kaplan−Meier plots, with log-rank test and hazard ratio estimation comparing patient subgroups. Results: A total of 319 BC patients under adjuvant endocrine therapy and/or adjuvant chemotherapy were included. Curve-fitting analysis showed that for a BMI cut-off >29 in AI-treated BC patients (n = 172), DFS was increasingly deteriorating and that the impact of BMI on 2-year DFS identified a cut-off specific only for the cohort of postmenopausal BC patients under adjuvant therapy with AI. Conclusion: in radically resected hormone-sensitive BC patients undergoing neoadjuvant or adjuvant chemotherapy and treated with AI, obesity represents a risk factor for recurrence, with a significantly reduced 2-year DFS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".