Comparing measurements of breast density

Highnam, R; Jeffreys, M; McCormack, V; Warren, R; Smith, GD; Brady, M; (2007) Comparing measurements of breast density. Physics in medicine and biology, 52 (19). pp. 5881-5895. ISSN 0031-9155 DOI:

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Breast density measurements can be made from mammograms using either area-based methods, such as the six category classification (SCC), or volumetric based methods, such as the standard mammogram form (SMF). Previously, we have shown how both types of methods generate breast density estimates which are generally close. In this paper, we switch our attention to the question of why, for certain cases, they provide widely differing estimates. First, we show how the underlying physical models of the breast employed in the methods need to be consistent, and how area-based methods are susceptible to projection effects. We then analyse a set of patients whose mammograms show large differences between their SCC and SMF assessments. More precisely, 12% of 657 patients were found to fall into this category. Of these, 2.7% were attributable to errors either in the SMF segmentation algorithms, human error in SCC categorization or poor image exposure. More importantly, 9.3% of the cases appear to be due to fundamental differences between the area- and volume-based techniques. We conclude by suggesting how we might remove half of those discrepancies by introducing a new categorization of the SMF estimates based on the breast thickness. We note however, that this still leaves 6% of patients with large differences between SMF and SCC estimates. We discuss why it might not be appropriate to assume SMF (or any volume measure) has a similar breast cancer risk prediction capability to SCC.

Item Type: Article
Keywords: Adult, Aged, Algorithms, Breast Neoplasms, radiography, Densitometry, methods, Female, Humans, Mammography, methods, Middle Aged, Observer Variation, Radiographic Image Enhancement, methods, Radiographic Image Interpretation, Computer-Assisted, methods, Reproducibility of Results, Sensitivity and Specificity
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Non-Communicable Disease Epidemiology
PubMed ID: 17881806
Web of Science ID: 250443200010


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