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Reader performance in visual assessment of breast density using visual analogue scales: Are some readers more predictive of breast cancer?
Author(s): Millicent Rayner; Elaine F. Harkness; Philip Foden; Mary Wilson; Soujanya Gadde; Ursula Beetles; Yit Y. Lim; Anil Jain; Sally Bundred; Nicky Barr; D. Gareth Evans; Anthony Howell; Anthony Maxwell; Susan M. Astley
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Paper Abstract

Mammographic breast density is one of the strongest risk factors for breast cancer, and is used in risk prediction and for deciding appropriate imaging strategies. In the Predicting Risk Of Cancer At Screening (PROCAS) study, percent density estimated by two readers on Visual Analogue Scales (VAS) has shown a strong relationship with breast cancer risk when assessed against automated methods. However, this method suffers from reader variability. This study aimed to assess the performance of PROCAS readers using VAS, and to identify those most predictive of breast cancer. We selected the seven readers who had estimated density on over 6,500 women including at least 100 cancer cases, analysing their performance using multivariable logistic regression and Receiver Operator Characteristic (ROC) analysis. All seven readers showed statistically significant odds ratios (OR) for cancer risk according to VAS score after adjusting for classical risk factors. The OR was greatest for reader 18 at 1.026 (95% Cl 1.018-1.034). Adjusted Area Under the ROC Curves (AUCs) were statistically significant for all readers, but greatest for reader 14 at 0.639. Further analysis of the VAS scores for these two readers showed reader 14 had higher sensitivity (78.0% versus 42.2%), whereas reader 18 had higher specificity (78.0% versus 46.0%). Our results demonstrate individual differences when assigning VAS scores; one better identified those with increased risk, whereas another better identified low risk individuals. However, despite their different strengths, both readers showed similar predictive abilities overall. Standardised training for VAS may improve reader variability and consistency of VAS scoring.

Paper Details

Date Published: 7 March 2018
PDF: 8 pages
Proc. SPIE 10577, Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment, 105770W (7 March 2018); doi: 10.1117/12.2293307
Show Author Affiliations
Millicent Rayner, The Univ. of Manchester (United Kingdom)
Elaine F. Harkness, The Univ. of Manchester (United Kingdom)
Manchester Univ. NHS Foundation Trust (United Kingdom)
Philip Foden, Manchester Univ. NHS Foundation Trust (United Kingdom)
Mary Wilson, Manchester Univ. NHS Foundation Trust (United Kingdom)
Soujanya Gadde, Manchester Univ. NHS Foundation Trust (United Kingdom)
Ursula Beetles, Manchester Univ. NHS Foundation Trust (United Kingdom)
Yit Y. Lim, Manchester Univ. NHS Foundation Trust (United Kingdom)
Anil Jain, Manchester Univ. NHS Foundation Trust (United Kingdom)
Sally Bundred, Manchester Univ. NHS Foundation Trust (United Kingdom)
Nicky Barr, Manchester Univ. NHS Foundation Trust (United Kingdom)
D. Gareth Evans, Manchester Univ. NHS Foundation Trust (United Kingdom)
The Univ. of Manchester (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)
Anthony Howell, Manchester Univ. NHS Foundation Trust (United Kingdom)
The Univ. of Manchester (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)
Anthony Maxwell, The Univ. of Manchester (United Kingdom)
Manchester Univ. NHS Foundation Trust (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)
Susan M. Astley, The Univ. of Manchester (United Kingdom)
Manchester Univ. NHS Foundation Trust (United Kingdom)
Manchester Cancer Research Ctr. (United Kingdom)


Published in SPIE Proceedings Vol. 10577:
Medical Imaging 2018: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

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