A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

Yuanjie Zheng; Yan Wang; Brad M. Keller; Emily Conant; James C. Gee; Despina Kontos

doi:10.1117/12.2008155

28 February 2013 A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study

Yuanjie Zheng, Yan Wang, Brad M. Keller, Emily Conant, James C. Gee, Despina Kontos

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86701B (2013) https://doi.org/10.1117/12.2008155
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Estimating a woman’s risk of breast cancer is becoming increasingly important in clinical practice. Mammographic density, estimated as the percent of dense (PD) tissue area within the breast, has been shown to be a strong risk factor. Studies also support a relationship between mammographic texture and breast cancer risk. We have developed a fullyautomated software pipeline for computerized analysis of digital mammography parenchymal patterns by quantitatively measuring both breast density and texture properties. Our pipeline combines advanced computer algorithms of pattern recognition, computer vision, and machine learning and offers a standardized tool for breast cancer risk assessment studies. Different from many existing methods performing parenchymal texture analysis within specific breast subregions, our pipeline extracts texture descriptors for points on a spatial regular lattice and from a surrounding window of each lattice point, to characterize the local mammographic appearance throughout the whole breast. To demonstrate the utility of our pipeline, and optimize its parameters, we perform a case-control study by retrospectively analyzing a total of 472 digital mammography studies. Specifically, we investigate the window size, which is a lattice related parameter, and compare the performance of texture features to that of breast PD in classifying case-control status. Our results suggest that different window sizes may be optimal for raw (12.7mm²) versus vendor post-processed images (6.3mm2). We also show that the combination of PD and texture features outperforms PD alone. The improvement is significant (p=0.03) when raw images and window size of 12.7mm² are used, having an ROC AUC of 0.66. The combination of PD and our texture features computed from post-processed images with a window size of 6.3 mm² achieves an ROC AUC of 0.75.

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Yuanjie Zheng, Yan Wang, Brad M. Keller, Emily Conant, James C. Gee, and Despina Kontos "A fully-automated software pipeline for integrating breast density and parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86701B (28 February 2013); https://doi.org/10.1117/12.2008155

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