Treatment of infertility and risk of breast cancer among women with a BRCA pathogenic variant: a matched case-control study
Bibliographic record
Abstract
BACKGROUND: The global trend toward delayed childbearing has led to an increased use of fertility treatment, including in vitro fertilization (IVF) and hormonal medications. Concerns regarding the potential impact of these interventions on breast cancer risk, particularly among high-risk women with a pathogenic variant in the BRCA1 or BRCA2 genes remains an important clinical concern. METHODS: We conducted a matched case-control analysis of women carrying a pathogenic or likely pathogenic variant in BRCA1 or BRCA2 enrolled in a longitudinal, international study. The analysis included 4,145 women with invasive breast cancer (cases) and 4,145 matched controls without breast cancer. Data on infertility and use of fertility treatments was collected by a research questionnaire. Conditional logistic regression was used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between infertility, fertility medications, and IVF, with the risk of breast cancer. Multivariable models were adjusted for parity and oral contraceptive use. RESULTS: Among the 8,290 participants, 12% reported a history of infertility, 5% had used fertility medication, and 1% had undergone IVF. There was no statistically significant association between a history of infertility (OR = 0.96; 95% CI 0.84-1.10), use of any type of fertility medication (OR = 1.10; 95% CI 0.90-1.34), or IVF specifically (OR = 1.15; 95% CI 0.76-1.73) and the risk of BRCA-breast cancer. Findings were similar in the adjusted analyses. CONCLUSIONS: Findings from this large, international study found no evidence for an association between infertility or fertility treatment and the risk of breast cancer among BRCA1 or BRCA2 carriers. Although based on low rates of exposure, these findings provide some reassurance to BRCA carriers considering fertility treatment. Future studies evaluating impact of contemporary protocols are needed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".