Computational and Mathematical Methods in Medicine
Volume 2013 (2013), Article ID 651091, 6 pages
Statistical Evaluation of a Fully Automated Mammographic Breast Density Algorithm
1Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada B3H 2Y9
2Department of Diagnostic Imaging, Capital District Health Authority, Halifax, NS, Canada B3H 2Y9
3Division of Medical Education/Informatics, Dalhousie University, Halifax, NS, Canada B3H 2Y9
4Nova Scotia Breast Screening Program, Halifax, NS, Canada B3H 2Y9
Received 2 October 2012; Revised 5 April 2013; Accepted 9 April 2013
Academic Editor: Giner Alor-Hernández
Copyright © 2013 Mohamed Abdolell et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Visual assessments of mammographic breast density by radiologists are used in clinical practice; however, these assessments have shown weaker associations with breast cancer risk than area-based, quantitative methods. The purpose of this study is to present a statistical evaluation of a fully automated, area-based mammographic density measurement algorithm. Five radiologists
estimated density in 5% increments for 138 “For Presentation” single MLO views; the median of the radiologists’ estimates was used as the reference standard. Agreement amongst radiologists was excellent, ICC = 0.884, 95% CI (0.854, 0.910). Similarly, the agreement between the algorithm and the reference standard was excellent, ICC = 0.862, falling within the 95% CI of the radiologists’ estimates. The Bland-Altman plot showed that the reference standard was slightly positively biased (+1.86%) compared to the algorithm-generated densities. A scatter plot showed that the algorithm moderately overestimated low densities and underestimated high densities. A box plot showed that 95% of the algorithm-generated assessments fell within one BI-RADS category of the reference standard. This study demonstrates the effective use of several statistical techniques that collectively produce a comprehensive evaluation of the algorithm and its potential to provide mammographic density measures that can be used to inform clinical practice.