title: An Investigation into the Consistency in Mammographic Density Identification by Radiologists: Effect of Radiologist Expertise and Mammographic Appearance.
abstract: The aim of this work is to investigate how radiologist expertise and image appearance may have an impact on inter-reader variability of mammographic density (MD) identification. Seventeen radiologists, divided into three expertise groups, were asked to manually segment the areas they consider to be MD in 40 clinical images. The variation in identification of MD for each image was quantified by finding the range of segmentation areas. The impact of radiologist expertise and image appearance on this variation was explored. The range of areas chosen by participating radiologists varied from 7 to 73% across the 40 images, with a mean range of 35 ± 13%. Participants with high expertise were more likely to choose similar areas to one another, compared to participants with medium and low expertise levels (mean range were 19 ± 10%, 29 ± 13% and 25 ± 14 %, respectively, p < 0.0001). There was a significantly higher average grey level for the area segmented by all radiologists as MD compared to the area of variation, with mean grey level value for 8-bit images being 146 ± 19 vs. 99 ± 14, respectively. MD segmentation borders were consistent in areas where there was a sharp intensity change within a short distance. In conclusion, radiologists with high expertise tend to have a higher agreement when identifying MD. Tissues which have a lower contrast and a less visually sharp gradient change at the interface between high density tissue and adipose background lead to inter-reader variation in choosing mammographic density.
authors.affiliation: Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia. yali3465@uni.sydney.edu.au.
authors.forename: Yanpeng
authors.initials: Y
authors.lastname: Li
authors.affiliation: Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia.
authors.forename: Patrick C
authors.initials: PC
authors.lastname: Brennan
authors.affiliation: Cancer Institute NSW, 8 Central Avenue, Eveleigh, NSW, 2015, Australia.
authors.forename: Warwick
authors.initials: W
authors.lastname: Lee
authors.affiliation: Melbourne School of Population and Global Health, The University of Melbourne, Grattan Street, Parkville, Victoria, 3010, Australia.
authors.forename: Carolyn
authors.initials: C
authors.lastname: Nickson
authors.affiliation: Institute of Physics, 76 Portland Place, London, W1B 1NT, UK.
authors.forename: Mariusz W
authors.initials: MW
authors.lastname: Pietrzyk
authors.affiliation: Faculty of Health Sciences, The University of Sydney, Cumberland Campus C42, M219, East St, Lidcombe, NSW, 2141, Australia.
authors.forename: Elaine A
authors.initials: EA
authors.lastname: Ryan
mesh_terms: D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002983:Clinical Competence / Q000706:statistics & numerical data*; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities*; D008327:Mammography / Q000706:statistics & numerical data*; D015588:Observer Variation; D015203:Reproducibility of Results
keywords: Density segmentation; Mammographic density (MD); Mammography; Observer variation