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Mammographic breast density and breast cancer risk in the Saudi population: a case-control study using visual and
Areej S Aloufi1,2, Abdulrahman N AlNaeem3, Abeer S Almousa3
1Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
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.
