Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk

Abstract

This commentary is about predicting a woman’s breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density

“…On the other hand, the Cirrocumulus and Cirrus risk gradients both remained similar to what they were when fitted alone [2-6]. We concluded that “conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer” [7].…”

“…For example, when Cirrocumulus and Cirrus were combined to predict breast cancer at on average a younger age (see Table 1), the AUC-equivalent OPERA was 2.28, so the interquartile risk ratio is ∼7-fold. In comparison, the interquartile risk ratio is ∼4-fold for a multigenerational family history risk score in predicting breast cancer before age 50 years, ∼3-fold for the latest polygenic risk score, ∼2-fold for conventional mammographic density, ∼1.5-fold for BRCA1 and BRCA2 mutations, and ∼1.2-fold or less for lifestyle-related risk factors [7,8]. .…”

Novel mammogram‐based measures improve breast cancer risk prediction beyond an established mammographic density measure

“…In terms of interpretation, the residuals are in effect the risk factor (not the raw measure itself), and the log(OPERA) is the difference between cases and controls in the mean of the standardised residuals; see Appendix [6]. On the other hand, the OR per unadjusted standard deviation is problematic when it is estimated after adjusting for covariates (unless it is adjusted in a way that depends on the correlations between covariates [2,7]) and is prone to misinterpretation; see Discussion.…”

Long-Term Evaluation of Women Referred to a Breast Cancer Family History Clinic (Manchester UK 1987–2020)

“…In countries where screening begins at 50 (e.g., the UK), consideration should be given to offering all women a one-off mammogram at age 40, together with risk estimation to determine future screening frequency. Mammographic density could be assessed automatically using artificial intelligence methods [ 64 , 65 ] and SNPs used only to determine precise risk where needed. The trials of risk and density-adapted screening and determination of the value of supplemental imaging techniques, such as whole breast ultrasound, contrast-enhanced mammography and abbreviated MRI, would further refine the management advice offered to women with high MD.…”