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Proceedings Paper

The impact of mammographic density and lesion location on detection
Author(s): Dana Al Mousa; Elaine Ryan; Warwick Lee; Carolyn Nickson; Mariusz Pietrzyk; Warren Reed; Ann Poulos; Yanpeng Li; Patrick Brennan
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Paper Abstract

The aim of this study is to examine the impact of breast density and lesion location on detection. A set of 55 mammographic images (23 abnormal images with 26 lesions and 32 normal images) were examined by 22 expert radiologists. The images were classified by an expert radiologist according to the Synoptic Breast Imaging Report of the National Breast Cancer Centre (NBCC) as having low mammographic density (D1<25% glandular and D2> 25-50% glandular) or high density (D3 51-75% glandular and D4> 75-glandular). The observers freely examined the images and located any malignancy using a 5-point confidence. Performance was defined using the following metrics: sensitivity, location sensitivity, specificity, receiver operating characteristic (ROC Az) curves and jackknife free-response receiver operator characteristics (JAFROC) figures of merit. Significant increases in sensitivity (p= 0.0174) and ROC (p=0.0001) values were noted for the higher density compared with lower density images according to NBCC classification. No differences were seen in radiologists’ performance between lesions within or outside the fibroglandular region. In conclusion, analysis of our data suggests that radiologists scored higher using traditional metrics in higher mammographic density images without any improvement in lesion localisation. Lesion location whether within or outside the fibroglandular region appeared to have no impact on detection abilities suggesting that if a masking effect is present the impact is minimal. Eye-tracking analyses are ongoing.

Paper Details

Date Published: 28 March 2013
PDF: 9 pages
Proc. SPIE 8673, Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment, 86730U (28 March 2013); doi: 10.1117/12.2006781
Show Author Affiliations
Dana Al Mousa, The Univ. of Sydney (Australia)
Elaine Ryan, The Univ. of Sydney (Australia)
Warwick Lee, Cancer Institute NSW (Australia)
Carolyn Nickson, The Univ. of Melbourne (Australia)
Mariusz Pietrzyk, The Univ. of Sydney (Australia)
Warren Reed, The Univ. of Sydney (Australia)
Ann Poulos, The Univ. of Sydney (Australia)
Yanpeng Li, The Univ. of Sydney (Australia)
Patrick Brennan, The Univ. of Sydney (Australia)

Published in SPIE Proceedings Vol. 8673:
Medical Imaging 2013: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Claudia R. Mello-Thoms, Editor(s)

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