title: Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls.
abstract: We statistically compare the contributions of parenchymal phenotypes to mammographic density in distinguishing between high-risk cases and low-risk controls. The age-matched evaluation included computerized mammographic assessment of breast percent density (PD) and parenchymal patterns (phenotypes of coarseness and contrast) from radiographic texture analysis (RTA) of the full-field digital mammograms from 456 cases: 53 women with BRCA1/2 gene mutations, 75 with unilateral cancer, and 328 at low risk of developing breast cancer. Image-based phenotypes of parenchymal pattern coarseness and contrast were each found to significantly discriminate between the groups; however, PD did not. From ROC analysis, PD alone yielded area under the fitted ROC curve (AUC) values of 0.53 ([Formula: see text]) and 0.57 ([Formula: see text]) in the classification task between BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. In a round-robin evaluation with Bayesian artificial neural network (BANN) analysis, RTA yielded AUC values of 0.81 (95% confidence interval [0.71, 0.89]) and 0.70 (95% confidence interval [0.63, 0.77]) between the BRCA1/2 gene-mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results show that high-risk and low-risk women have different mammographic parenchymal patterns with significantly higher discrimination resulting from characteristics of the parenchymal patterns than just the breast PD.
authors.affiliation: The University of Chicago , Department of Radiology, MC 2026, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.affiliation: The University of Chicago , Department of Radiology, MC 2026, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.
authors.forename: Maryellen L
authors.initials: ML
authors.lastname: Giger
authors.affiliation: The University of Chicago , Department of Radiology, MC 2026, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.
authors.forename: Li
authors.initials: L
authors.lastname: Lan
authors.affiliation: The University of Chicago , Department of Radiology, MC 2026, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.
authors.forename: Jyothi
authors.initials: J
authors.lastname: Janardanan
authors.affiliation: The University of Chicago , Department of Radiology, MC 2026, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.
authors.forename: Charlene A
authors.initials: CA
authors.lastname: Sennett
keywords: breast cancer risk assessment; breast percent density; coarseness; contrast; full-field digital mammograms; image-based phenotypes; mammographic parenchymal patterns; quantitative imaging analysis; radiographic texture analysis