Study Links Parenchymal Pattern Analysis, Cancer Risk Assessment

Twitter icon
Facebook icon
LinkedIn icon
e-mail icon
Google icon
imageAnalyzing mammographic parenchymal patterns to measure the density of breast tissue has the potential to help clinicians better determine a woman's breast cancer risk as well as lead to better patient care by enabling more accurate density measurements. These are among the findings of a new study conducted by researchers at the University of Michigan and published in the March 15 online edition of Radiology. The researchers developed a computerized mammographic parenchymal pattern (MPP) measure and explored the association between texture patterns of fibroglandular breast tissue in the retroareaolar region with breast cancer risk. According to Lead Author Jun Wei, PhD, and his associates, the most common approach to determining breast density—i.e., visually assessing the fibroglandular tissue imaged on the mammogram and assigning a BI-RADS category to it—is subjective."A more accurate and reproducible measure…for breast density that does not depend on the subjective impression of human readers may further improve the reliability of breast density estimates for risk prediction," the authors write. Computerized quantitative measurements of breast density are still new on the clinical scene, but studies have shown that these techniques can improve reproducibility and intraobserver agreement of breast density measurements, they wrote. In their study, Wei and his colleagues used computerized analysis of the distribution of breast tissue on mammograms, compared with mammographic density measurements (percentage density, or PD) in which density is measured as the percentage of dense area estimated from the segmented breast on mammograms. They applied an automated method that had previously been developed in the University of Michigan’s laboratory to estimate the percentage of fibroglandular tissue area relative to total breast area. The researchers also configured a computerized mammographic parenchymal pattern algorithm to analyze texture patterns of fibroglandular tissue behind the nipple and areola. Wei and his team indicate that in order to use the textural information in the breast parenchyma to improve risk prediction, the MPP descriptor needed to be able to characterize spatial relationships of breast structures while also having a low correlation with percentage density measures. The Pearson product-moment correlation coefficient between the MPP and the PD was 0.13, a low correlation, they note. However, they observe, the computerized MPP measure had a strong association (p