Performance of risk prediction models in breast cancer screening among women in Cyprus

Tools that can predict breast cancer risk in women will have a significant impact on women and healthcare systems in low- and middle-income countries.

We compared the performances of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model, the International Breast Cancer Intervention Study (IBIS) model, and the Gail model in predicting breast cancer risk among women in Cyprus.

We recruited 655 women from Dr Burhan Nalbantoglu Devlet Hastanesi Hospital in Lefkosa: 318 had breast cancer and 337 did not have breast cancer (hospital-based controls). We collected retrospective data from the hospital's medical records and interviews with the women after informed consent. Data collected included: age, age at diagnosis, age at menarche, menopausal status, presence of benign breast disease, breast cancer in relatives, BRCA-1 and BRCA-2 mutations, history of hormone replacement therapy, and breast density. We calculated the 5-year risk of breast cancer and the risk values were used to plot receiver operating curves.

For the area under the curve (95% confidence interval, CI), sensitivity and specificity for the models were: BOADICEA 0.81 (95% CI: 0.77-0.84), 26.4% and 98.8%; IBIS 0.80 (95% CI: 0.77-0.84), 19.4% and 97.3%; and Gail 0.76 (95% CI: 0.73-0.80), 17.3% and 98.5%.

The breast cancer risk prediction models performed similarly although on closer appraisal, the BOADICEA and IBIS models performed slightly better. These models are simple, appropriate, cost-effective, and non-invasive tools for identifying high-risk women in low- and middle-income countries who could benefit from mammography screening.