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Proceedings Paper

Visual assessment of breast density using Visual Analogue Scales: observer variability, reader attributes and reading time
Author(s): Teri Ang; Elaine F. Harkness; Anthony J. Maxwell; Yit Y. Lim; Richard Emsley; Anthony Howell; D. Gareth Evans; Susan Astley; Soujanya Gadde
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Paper Abstract

Breast density is a strong risk factor for breast cancer and has potential use in breast cancer risk prediction, with subjective methods of density assessment providing a strong relationship with the development of breast cancer. This study aims to assess intra- and inter-observer variability in visual density assessment recorded on Visual Analogue Scales (VAS) among trained readers, and examine whether reader age, gender and experience are associated with assessed density. Eleven readers estimated the breast density of 120 mammograms on two occasions 3 years apart using VAS. Intra- and inter-observer agreement was assessed with Intraclass Correlation Coefficient (ICC) and variation between readers visualised on Bland-Altman plots. The mean scores of all mammograms per reader were used to analyse the effect of reader attributes on assessed density. Excellent intra-observer agreement (ICC>0.80) was found in the majority of the readers. All but one reader had a mean difference of <10 percentage points from the first to the second reading. Inter-observer agreement was excellent for consistency (ICC 0.82) and substantial for absolute agreement (ICC 0.69). However, the 95% limits of agreement for pairwise differences were -6.8 to 15.7 at the narrowest and 0.8 to 62.3 at the widest. No significant association was found between assessed density and reader age, experience or gender, or with reading time. Overall, the readers were consistent in their scores, although some large variations were observed. Reader evaluation and targeted training may alleviate this problem.

Paper Details

Date Published: 10 March 2017
PDF: 9 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 1013608 (10 March 2017); doi: 10.1117/12.2253797
Show Author Affiliations
Teri Ang, Manchester Medical School, The Univ. of Manchester (United Kingdom)
Elaine F. Harkness, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Anthony J. Maxwell, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Christie Hospital (United Kingdom)
Yit Y. Lim, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Richard Emsley, The Univ. of Manchester (United Kingdom)
Anthony Howell, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Christie Hospital (United Kingdom)
D. Gareth Evans, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Christie Hospital (United Kingdom)
Susan Astley, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Christie Hospital (United Kingdom)
Soujanya Gadde, Univ. Hospital of South Manchester (United Kingdom)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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