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Proceedings Paper

Mammographic phenotypes of breast cancer risk driven by breast anatomy
Author(s): Aimilia Gastounioti; Andrew Oustimov; Meng-Kang Hsieh; Lauren Pantalone; Emily F. Conant; Despina Kontos
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Paper Abstract

Image-derived features of breast parenchymal texture patterns have emerged as promising risk factors for breast cancer, paving the way towards personalized recommendations regarding women’s cancer risk evaluation and screening. The main steps to extract texture features of the breast parenchyma are the selection of regions of interest (ROIs) where texture analysis is performed, the texture feature calculation and the texture feature summarization in case of multiple ROIs. In this study, we incorporate breast anatomy in these three key steps by (a) introducing breast anatomical sampling for the definition of ROIs, (b) texture feature calculation aligned with the structure of the breast and (c) weighted texture feature summarization considering the spatial position and the underlying tissue composition of each ROI. We systematically optimize this novel framework for parenchymal tissue characterization in a case-control study with digital mammograms from 424 women. We also compare the proposed approach with a conventional methodology, not considering breast anatomy, recently shown to enhance the case-control discriminatory capacity of parenchymal texture analysis. The case-control classification performance is assessed using elastic-net regression with 5-fold cross validation, where the evaluation measure is the area under the curve (AUC) of the receiver operating characteristic. Upon optimization, the proposed breast-anatomy-driven approach demonstrated a promising case-control classification performance (AUC=0.87). In the same dataset, the performance of conventional texture characterization was found to be significantly lower (AUC=0.80, DeLong's test p-value<0.05). Our results suggest that breast anatomy may further leverage the associations of parenchymal texture features with breast cancer, and may therefore be a valuable addition in pipelines aiming to elucidate quantitative mammographic phenotypes of breast cancer risk.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013417 (3 March 2017); doi: 10.1117/12.2254630
Show Author Affiliations
Aimilia Gastounioti, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Andrew Oustimov, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Meng-Kang Hsieh, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Lauren Pantalone, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Emily F. Conant, Perelman School of Medicine, Univ. of Pennsylvania (United States)
Despina Kontos, Perelman School of Medicine, Univ. of Pennsylvania (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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