BACKGROUND: Germline BRCA1/2 testing is recommended for all patients with ovarian cancer, as identifying pathogenic variants (PV) informs treatment and enables family cascade testing. However, in resource-limited settings, high testing costs often limit feasibility. An alternative approach is to use predictive models to prioritize patients at risk, optimizing resource allocation. Existing models are largely Western-derived; in Asian populations, models exist for breast cancer-not ovarian cancer.
METHODS: Using data from a multicenter study of 1,126 Asian patients with ovarian cancer (including 147 BRCA PV carriers), we developed models incorporating cancer history, clinicopathologic features, and reproductive factors to estimate the likelihood of carrying PVs. We assessed discrimination, calibration, and accuracy and compared genetic testing costs with a universal testing strategy.
RESULTS: Our final model demonstrated good calibration and strong discriminatory power, with an area under the curve of 0.80 (95% confidence interval, 0.74-0.87). Factors included in the model were age at diagnosis, ethnicity, personal and family cancer history, and clinicopathologic features. The model had 77% accuracy at the optimal threshold for testing compared with 13% accuracy with universal testing, reducing genetic testing costs from ∼USD4,371 to ∼USD1,974 per identified carrier. Notably, while maintaining 100% sensitivity, the model reduced testing by 15%, yielding potential savings of ∼USD68,182 per 800 patients each year.
CONCLUSIONS: Targeted testing using a prediction model potentially offers a more efficient and scalable alternative when universal testing is not feasible, optimizing impact in resource-limited settings.
IMPACT: This work supports more affordable and equitable BRCA testing pathways for Asian patients with ovarian cancer.
* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.