title: Statistical evaluation of a fully automated mammographic breast density algorithm.
abstract: 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.
authors.affiliation: Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada B3H 2Y9. mo@dal.ca
authors.forename: Mohamed
authors.initials: M
authors.lastname: Abdolell
authors.forename: Kaitlyn
authors.initials: K
authors.lastname: Tsuruda
authors.forename: Gerry
authors.initials: G
authors.lastname: Schaller
authors.forename: Judy
authors.initials: J
authors.lastname: Caines
mesh_terms: D000465:Algorithms; D001331:Automation; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D019295:Computational Biology; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008327:Mammography / Q000706:statistics & numerical data*; D015588:Observer Variation; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011871:Radiology; D012307:Risk Factors