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Journal of Medical Imaging

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls
Author(s): Hui Li; Maryellen L. Giger; Li Lan; Jyothi Janardanan; Charlene A. Sennett
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Paper 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 (SE=0.05) and 0.57 (SE=0.04) 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.

Paper Details

Date Published: 13 November 2014
PDF: 9 pages
J. Med. Img. 1(3) 031009 doi: 10.1117/1.JMI.1.3.031009
Published in: Journal of Medical Imaging Volume 1, Issue 3
Show Author Affiliations
Hui Li, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Li Lan, The Univ. of Chicago (United States)
Jyothi Janardanan, The Univ. of Chicago (United States)
Charlene A. Sennett, The Univ. of Chicago (United States)


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