Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms

Rationale and Objectives. Mammographic density and parenchymal patterns have been shown to be related to the risk of developing breast cancer. Thus, computerized texture analysis of breast parenchymal patterns on mammograms may be useful in assessing breast cancer risk. Materials and Methods. A comparative evaluation was conducted of various computer-extracted texture features of mammographic parenchymal patterns of women with BRCA1/BRCA2 gene mutations and those of women at low risk of developing breast cancer. Mammograms from 172 subjects (30 women with either the BRCA1 or BRCA2 gene mutation and 142 low-risk women) were analyzed. Computerized texture features were extracted from regions-of-interest to assess the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of these features in the task of distinguishing between the two groups of women. Results. Quantitative texture analysis on digitized mammograms demonstrated that gene-mutation carriers and low-risk women have different mammographic parenchymal patterns. Gene-mutation carriers presented with parenchymal patterns that were denser, coarser, and lower in contrast than those of the low-risk group. For the gene-mutation carriers, their mammographic patterns appear to contain less high-frequency component as indicated by higher coarseness values, lower fractal dimensions, and smaller edge gradients, which yielded corresponding A(z) values of 0.79, 0.84, and 0.78, respectively, in the task of distinguishing between gene-mutation carriers and the low-risk group with the entire dataset. The contrast measure calculated from co-occurrence matrix method, which describes local image variation, yielded an Az value of 0.86 in distinguishing between the two groups of women. Conclusion. Computerized texture analysis of mammograms provides radiographic descriptors of mammographic parenchymal patterns. The computer-extracted features may be useful for identifying women at high risk for breast cancer and for monitoring the treatment of breast cancer patients.