The quantitative analysis of mammographic densities

TLDR

Quantitative classification of mammographic parenchyma is a strong predictor of breast cancer risk, yet current schemes are coarse and subjective, leading to inter‑observer variability. The study proposes an interactive thresholding method to quantify the proportion of dense tissue in digitized mammograms. Observers used a CRT display to set grey‑level thresholds, delineating breast and dense regions, and the dense proportion was computed from the image histogram, yielding a method that correlates strongly with radiologists’ six‑category ratings. The method demonstrated high reliability (ICC > 0.9) and strong correlation (R > 0.91) with radiologists’ ratings, suggesting it could be useful for routine risk assessment and monitoring breast parenchyma changes in preventive studies.

Abstract

Quantitative classification of mammographic parenchyma based on radiological assessment has been shown to provide one of the strongest estimates of the risk of developing breast cancer. Existing classification schemes, however, are limited by coarse category scales. In addition, subjectivity can lead to sizeable interobserver and intraobserver variations. Here, we propose an interactive thresholding technique applied to digitized film-screen mammograms, which assesses the proportion of the mammographic image representing radiographically dense tissue. Observers viewed images on a CRT display and selected grey-level thresholds from which the breast and regions of dense tissue in the breast were identified. The proportion of radiographic density was then calculated from the image histogram. The technique was evaluated for the mammograms of 30 women and is well correlated (R > 0.91, Spearman coefficient) with a six-category subjective classification of radiographic density by radiologists. The technique was found to be very reliable with an intraclass correlation coefficient between observers typically R > 0.9. This technique may have a role in routine mammographic analysis for the purpose of assessing risk categories and as a tool in studies of the etiology of breast cancer, in particular for monitoring changes in breast parenchyma during potential preventive interventions.