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Proceedings Paper

Does the prediction of breast cancer improve using a combination of mammographic density measures compared to individual measures alone?
Author(s): Joseph Ryan Wong Sik Hee; Elaine F. Harkness; Soujanya Gadde; Yit Y. Lim; Anthony J. Maxwell; D. Gareth Evans; Anthony Howell; Susan M. Astley
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Paper Abstract

High mammographic density is associated with an increased risk of breast cancer, however whether the association is stronger when there is agreement across measures is unclear. This study investigates whether a combination of density measures is a better predictor of breast cancer risk than individual methods alone. Women recruited to the Predicting Risk of Cancer At Screening (PROCAS) study and with mammographic density assessed using three different methods were included (n=33,304). Density was assessed visually using Visual Analogue Scales (VAS) and by two fully automated methods, Quantra and Volpara. Percentage breast density was divided into (high, medium and low) and combinations of measures were used to further categorise individuals (e.g. ‘all high’). A total of 667 breast cancers were identified and logistic regression was used to determine the relationship between breast density and breast cancer risk. In total, 44% of individuals were in the same tertile for all three measures, 8.6% were in non-adjacent (high and low) or mixed categories (high, medium and low). For individual methods the strongest association with breast cancer risk was for medium and high tertiles of VAS with odds ratios (OR) adjusted for age and BMI of 1.63 (95% CI 1.31-2.03) and 2.33 (1.87-2.90) respectively. For the combination of density methods the strongest association was for ‘all high’ (OR 2.42, 1.77-3.31) followed by “two high” (OR 1.90, 1.35-3.31) and “two medium” (OR 1.88, 1.40-2.52). Combining density measures did not affect the magnitude of risk compared to using individual methods.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342P (3 March 2017); doi: 10.1117/12.2254291
Show Author Affiliations
Joseph Ryan Wong Sik Hee, The Univ. of Manchester (United Kingdom)
Elaine F. Harkness, The Univ. of Manchester (United Kingdom)
Univ. Hospital South Manchester (United Kingdom)
Soujanya Gadde, Univ. Hospital of South Manchester (United Kingdom)
Yit Y. Lim, Univ. Hospital of South Manchester (United Kingdom)
Anthony J. Maxwell, The Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)
D. Gareth Evans, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Anthony Howell, Univ. Hospital of South Manchester (United Kingdom)
The Univ. of Manchester (United Kingdom)
Susan M. Astley, The Univ. of Manchester (United Kingdom)
Univ. Hospital of South Manchester (United Kingdom)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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