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Proceedings Paper

Roles of biologic breast tissue composition and quantitative image analysis of mammographic images in breast tumor characterization
Author(s): Karen Drukker; Maryellen L. Giger; Fred Duewer; Serghei Malkov; Christopher I. Flowers; Bonnie Joe; Karla Kerlikowske; Jennifer S. Drukteinis; John Shepherd
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Paper Abstract

Purpose. Investigate whether knowledge of the biologic image composition of mammographic lesions provides imagebased biomarkers above and beyond those obtainable from quantitative image analysis (QIA) of X-ray mammography. Methods. The dataset consisted of 45 in vivo breast lesions imaged with the novel 3-component breast (3CB) imaging technique based on dual-energy mammography (15 malignant, 30 benign diagnoses). The 3CB composition measures of water, lipid, and protein thicknesses were assessed and mathematical descriptors, ‘3CB features’, were obtained for the lesions and their periphery. The raw low-energy mammographic images were analyzed with an established in-house QIA method obtaining ‘QIA features’ describing morphology and texture. We investigated the correlation within the ‘3CB features’, within the ‘QIA features’, and between the two. In addition, the merit of individual features in the distinction between malignant and benign lesions was assessed. Results. Whereas many descriptors within the ‘3CB features’ and ‘QIA features’ were, often by design, highly correlated, correlation between descriptors of the two feature groups was much weaker (maximum absolute correlation coefficient 0.58, p<0.001) indicating that 3CB and QIA-based biomarkers provided potentially complementary information. Single descriptors from 3CB and QIA appeared equally well-suited for the distinction between malignant and benign lesions, with maximum area under the ROC curve 0.71 for a protein feature (3CB) and 0.71 for a texture feature (QIA). Conclusions. In this pilot study analyzing the new 3CB imaging modality, knowledge of breast tissue composition appeared additive in combination with existing mammographic QIA methods for the distinction between benign and malignant lesions.

Paper Details

Date Published: 20 March 2014
PDF: 9 pages
Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 90351U (20 March 2014); doi: 10.1117/12.2043746
Show Author Affiliations
Karen Drukker, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)
Fred Duewer, Univ. of California, San Francisco (United States)
Serghei Malkov, Univ. of California, San Francisco (United States)
Christopher I. Flowers, Univ. of South Florida, Tampa (United States)
Bonnie Joe, Univ. of California, San Francisco (United States)
Karla Kerlikowske, Univ. of California, San Francisco (United States)
Jennifer S. Drukteinis, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
John Shepherd, Univ. of California, San Francisco (United States)


Published in SPIE Proceedings Vol. 9035:
Medical Imaging 2014: Computer-Aided Diagnosis
Stephen Aylward; Lubomir M. Hadjiiski, Editor(s)

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