Breast cancer risk stratification for mammographic screening: A nation-wide screening cohort of 24,431 women in Singapore

Background Breast cancer incidence is increasing in Asia. However, few women in Singapore attend routine mammography screening. We aim to identify women at high risk of breast cancer who will benefit most from regular screening using the Gail model and information from their first screen (recall status and mammographic density). Methods In 24,431 Asian women (50-69 years) who attended screening between 1994 and 1997, 117 developed breast cancer within 5 years of screening. Cox proportional hazard models were used to study the associations between risk classifiers (Gail model 5-year absolute risk, recall status, mammographic density), and breast cancer occurrence. The efficacy of risk stratification was evaluated by considering sensitivity, specificity, and the proportion of cancers identified. Results Adjusting for information from first screen attenuated the hazard ratios (HR) associated with 5-year absolute risk (continuous, unadjusted HR [95% confidence interval]: 2.3 [1.8-3.1], adjusted HR: 1.9 [1.4-2.6]), but improved the discriminatory ability of the model (unadjusted AUC: 0.615 [0.559-0.670], adjusted AUC: 0.703 [0.653-0.753]). The sensitivity and specificity of the adjusted model were 0.709 and 0.622, respectively. Thirty-eight percent of all breast cancers were detected in 12% of the study population considered high risk (top five percentile of the Gail model 5-year absolute risk [absolute risk >= 1.43%], were recalled, and/or mammographic density >= 50%). Conclusion The Gail model is able to stratify women based on their individual breast cancer risk in this population. Including information from the first screen can improve prediction in the 5 years after screening. Risk stratification has the potential to pick up more cancers.

Automated summary

Breast cancer incidence is rising in Asia, but many women in Singapore do not attend routine mammography screening. By utilizing the Gail model and information from the first screen, researchers were able to identify high-risk women and improve the prediction of breast cancer in this population. This risk stratification method has the potential to detect more cancers and benefit women in the long term.