Search

Both interactive thresholding tools and human visual assessment have been related to the risk of developing breast cancer. In this paper we explore the relationship between human assessment of area of dense tissue and the actual thickness of tissue in the breast by using a volumetric density technique to compute areas of dense tissue, varying the threshold below which areas of low density are discounted and observing the correlation with visual assessment of density at different thresholds. Based on analysis of thresholds used in the automated method, radiologists΄ definition of a dense pixel is one in which the percentage of glandular tissue is between 10% and 20% of the total thickness of the compressed breast at that point. [ABSTRACT FROM AUTHOR]

Breast density is positively linked to the risk of developing breast cancer. Furthermore, the addition of breast density as an input to breast cancer risk prediction models has been shown to improve their predictive power. Such models are used in the management of women at high risk but could potentially be used to determine screening strategy. A stepwedge-based technique has been used to measure volumetric density from the mammograms of 1,289 women in the UK screening programme who additionally completed a questionnaire on risk-related factors. The sample had a mean age of 60.1 (range 48.0 – 78.0), a mean breast thickness of 59mm (range 21 – 102mm) and a mean volumetric breast density of 11% (range 0.5 – 58%). Using Pearson΄s correlation coefficient, breast density was found to be significantly correlated with weight (r = -0.45), body mass index (r = -0.48), age (r = -0.13) and breast thickness (r =-0.65) at the p = 0.01 level. Absolute glandular volume was also found to be significantly correlated with these parameters although the extent of correlation was weaker. [ABSTRACT FROM AUTHOR]

If breast density is to be incorporated into breast cancer risk prediction models, the technique used for measurement must be quantitative, accurate, objective and reproducible. We present a semi-automated method that has been used by three independent operators to measure glandular volume from the digitised mammograms of 29 women (116 images). Additionally, one operator used the method on 10 separate occasions on a sample of 24 images. Intra-observer variability was found to be acceptably low, with coefficients of variation ranging from 3.5 – 5.7% depending on mammographic view (intra-class correlation coefficient close to 1 in all cases). However, inter-observer variability was greater with significant differences in glandular volume recorded between observers. This was attributed to the method of breast edge detection. The development of a new automatic breast edge detection algorithm has resolved the issue. The average difference in glandular volume measurement between two independent operators in the cranio-caudal view is -0.89cm3 (95% confidence interval -2.77 – 0.99 cm3) using the new method, compared to 5.99cm3 (95% confidence interval 2.72 – 9.76 cm3) using the old method. [ABSTRACT FROM AUTHOR]