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. © 2010 Springer-Verlag.
|Number of pages||6|
|Journal||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Publication status||Published - 2010|
- breast density
- observer variability
- volumetric technique