13 November 2014 Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls
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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.
© 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)
Hui Li, Maryellen L. Giger, Li Lan, Jyothi Janardanan, Charlene A. Sennett, "Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing between BRCA1/2 cases, unilateral cancer cases, and controls," Journal of Medical Imaging 1(3), 031009 (13 November 2014). https://doi.org/10.1117/1.JMI.1.3.031009 . Submission:
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