Area and volumetric density estimation in processed full-field digital mammograms for risk assessment of breast cancer

Abstract

Introduction: Mammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.

Methods: The data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.

Results: All three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p<1 × 10(-6) for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).

Conclusions: Our results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.

Figure 1. Correlation between Volpara and our PD measurements from processed mammograms: (a) Scatter-plots of our PD measurements (processed mammograms) and Volpara (raw mammograms) measurements for the training sample of GE mammograms from 403 women (b) scatter-plots of our PD measurements and Volpara measurements for the test sample of GE mammograms from 1011 women with genotype information.

Figure 2. –log₁₀ QQ plots for p-values assessing the association between the investigated features (Table S1 and APs) and rs10995190.

(a) with adjustment for age, BMI, menopausal status, HRT use, parity and age at first birth. (b) with adjustment as in (a), plus Volpara. (c) with adjustment as in (a), plus CASAM-Vol. (d) with adjustment as in (a), plus CASAM-Area.