Screening, Risk Reduction Strategies, and Clinical Management of Unaffected Carriers of <i>BRCA1</i> or <i>BRCA2</i> Pathogenic Variants
Bibliographic record
Abstract
Breast cancer remains the most common cancer globally, with approximately 5-15% of cases linked to pathogenic variants primarily in BRCA1 or BRCA2. These mutations greatly increase cancer risks, highlighting the critical need for more effective screening and prevention strategies. This review aimed to summarize existing evidence and propose a comprehensive approach to reducing cancer risk in unaffected mutation carriers. A narrative review of published literature was conducted to evaluate risk-reduction strategies, including surveillance, enhanced imaging, risk-reducing surgeries, and chemoprevention. Barriers to the uptake of these strategies and the psychological impact on carriers were also examined. Annual magnetic resonance imaging (MRI) remains the most sensitive screening tool for early breast cancer detection in high-risk individuals. Selective estrogen receptor modulators (SERMs) and aromatase inhibitors have shown potential as chemopreventive agents, but uptake remains limited due to concerns about efficacy and side effects. Risk-reducing surgeries, such as bilateral salpingo-oophorectomy (BSO) and mastectomy, significantly lower the risk of breast and ovarian cancer; however, their uptake is often hindered by emotional, cultural, and financial factors. Family communication of genetic results and support by healthcare professionals are critical to encouraging preventive actions. Effective screening and risk-reduction strategies are available for BRCA1 and BRCA2 carriers, yet barriers to implementation persist. Personalized counseling, enhanced accessibility, and culturally sensitive education are essential to improving the adoption of these preventive measures. Further studies are needed to explore novel chemoprevention options and interventions to address the unmet needs of carriers worldwide.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".