Numerous studies have investigated the relation between mammographic density and breast cancer risk. These
studies indicate that women with dense breasts have a four to six fold risk increase. There is currently no gold
standard for automatic assessment of mammographic density.
In previous work two different automated methods for measuring the effect of HRT w.r.t. changes in breast
density have been presented. One is a percentage density based on an adaptive global threshold, and the other is
an intensity invariant measure, which provides structural information orthogonal to intensity-based methods. In
this article we investigate the ability to detect density changes induced by HRT for these measures and compare
to a radiologist's BI-RADS rating and interactive threshold percentage density.
In the experiments, two sets of mammograms of 80 patients from a double blind, placebo controlled HRT
experiment are used. The p-values for the statistical significance of the separation of density means, for the HRT
group and the placebo group at end of study, are 0.2, 0.1, 0.02 and 0.02 for the automatic threshold, BI-RADS,
the stripyness and the interactive threshold respectively.
Numerous studies have investigated the relation between mammographic density and breast cancer risk. These studies indicate that women with high breast density have a four to six fold risk increase. An investigation of whether or not this relation is causal is important for, e.g., hormone replacement therapy (HRT), which has been shown to actually increase the density. No gold standard for automatic assessment of mammographic density exists. Manual methods such as Wolfe patterns and BI-RADS are helpful for communication of diagnostic sensitivity, but they are both time consuming and crude. They may be sufficient in certain cases and for single measurements, but for serial, temporal analysis it is necessary to be able to detect more subtle changes and, in addition, to be more reproducible. In this work an automated method for measuring the effect of HRT w.r.t. changes in biological density in the breast is presented. This measure is a novel measure, which provides structural information orthogonal to intensity-based methods. Hessian eigenvalues at different scales are used as features and a clustering of these is employed to divide a mammogram into four structurally different areas. Subsequently, based on the relative size of the areas, a density score is determined. In the experiments, two sets of mammograms of 50 patients from a double blind, placebo controlled HRT experiment were used. The change in density for the HRT group, measured with the new method, was significantly higher (p = 0.0002) than the change in the control group.
This paper is one of the first steps towards the development of a mass-screening tool, well-suited for quantizing the extend of calcific deposits in the lumbar aorta, which should deliver reliable and easily reproducible data. The major problem is that non-calcified parts of the aorta are not visible on conventional x-ray images. We investigate whether or not it is possible to predict the location of the lumbar aorta, using the first four lumbar vertebrae as prior.
We build a conditional probabilistic model from 90 manually annotated datasets. Using this model we made inferences on the position of the aortic walls given the position and shape of the four vertebrae.
Of particular interest is the performance of the probabilistic model in comparison to the mean aortic shape. Due to the fact that our data set for this particular study only contained 90 hand-annotated images, we evaluated the model using the "leave-one-out" method. The resulting distance from the predicted to the actual aorta was then compared to the distance from the mean aorta to the actual aorta.
The obtained results are encouraging; our conditional model provides results that are up to 38% better than the prediction using only the mean shape, and yields an overlap index of 0.89, whereas the mean shape only produces 0.83.
An important question in mammographic image analysis is the importance of the projected view of the breast. Can temporal changes in density be detected equally well using either one of the commonly available views Medio-Lateral (ML) and Cranio-Caudal (CC) or a combination of the two? Two sets of mammograms of 50 patients in a double-blind, placebo controlled hormone replacement therapy (HRT) experiment were used. One set of ML and CC view from 1999 and one from 2001. HRT increases density which means that the degree of
separation of the populations (one group receiving HRT and the other placebo) can be used as a measure of how much density change information is carried in a particular view or combination of views. Earlier results have shown a high correlation between CC and ML views leading to the conclusion that only one of them is needed for density assessment purposes. A similar high correlation coefficient was observed in this study (0.85), while the correlation between changes was a bit lower (0.71). Using both views to separate the patients receiving hormones from the ones receiving placebo increased the area under corresponding ROC curves from 0.76 ± 0.04 to 0.79 ± 0.04.