Mammographic breast density and breast cancer risk in the Saudi population: a case–control study using visual and automated methods

Abstract

Objective This study aims to establish risk of breast cancer based on breast density among Saudi women and to compare cancer prediction using different breast density methods. Methods 1140 pseudonymised screening mammograms from Saudi females were retrospectively collected. Breast density was assessed using Breast Imaging Reporting and Data System (BI-RADS) density categories and visual analogue scale (VAS) of 285 cases and 855 controls matched on age and body mass index. In a subset of 160 cases and 480 controls density was estimated by two automated methods, Volpara Density™ and predicted VAS (pVAS). Odds ratios (ORs) between the highest and second categories in BI-RADS and Volpara density grades, and highest vs lowest quartiles in VAS, pVAS and Volpara Density™, were estimated using conditional logistic regression. Results: For BI-RADS, the OR was 6.69 (95% CI 2.79–16.06) in the highest vs second category and OR = 4.78 (95% CI 3.01–7.58) in the highest vs lowest quartile for VAS. In the subset, VAS was the strongest predictor OR = 7.54 (95% CI 3.86–14.74), followed by pVAS using raw images OR = 5.38 (95% CI 2.68–10.77) and Volpara Density ™ OR = 3.55, (95% CI 1.86–6.75) for highest vs lowest quartiles. The matched concordance index for VAS was 0.70 (95% CI 0.65–0.75) demonstrating better discrimination between cases and controls than all other methods. Conclusion Increased mammographic density was strongly associated with risk of breast cancer among Saudi women. Radiologists’ visual assessment of breast density is superior to automated methods. However, pVAS and Volpara Density ™ also significantly predicted breast cancer risk based on breast density. Advances in knowledge Our study established an association between breast density and breast cancer in a Saudi population and compared the performance of automated methods. This provides a stepping-stone towards personalised screening using automated breast density methods.