Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case-control study.

Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method.

Quantification and normalization of x-ray mammograms.

The analysis of (x-ray) mammograms remains qualitative, relying on the judgement of clinicians. We present a novel method to compute a quantitative, normalized measure of tissue radiodensity traversed by the primary beam incident on each pixel of a mammogram, a measure we term the standard attenuation rate (SAR). SAR enables: the estimation of breast density which is linked to cancer risk; direct comparison between images; the full potential of computer aided diagnosis to be utilized; and a basis for digital breast tomosynthesis reconstruction. It does this by removing the effects of the imaging conditions under which the mammogram is acquired. First, the x-ray spectrum incident upon the breast is calculated, and from this, the energy exiting the breast is calculated. The contribution of scattered radiation is calculated and subtracted. The SAR measure is the scaling factor that must be applied to the reference material in order to match the primary attenuation of the breast. Specifically, this is the scaled reference material attenuation which when traversed by an identical beam to that traversing the breast, and when subsequently detected, results in the primary component of the pixel intensity observed in the breast image. We present results using two tissue equivalent phantoms, as well as a sensitivity analysis to detector response changes over time and possible errors in compressed thickness measurement.

A model of primary and scattered photon fluence for mammographic x-ray image quantification.

We present an efficient method to calculate the primary and scattered x-ray photon fluence component of a mammographic image. This can be used for a range of clinically important purposes, including estimation of breast density, personalized image display, and quantitative mammogram analysis. The method is based on models of: the x-ray tube; the digital detector; and a novel ray tracer which models the diverging beam emanating from the focal spot. The tube model includes consideration of the anode heel effect, and empirical corrections for wear and manufacturing tolerances. The detector model is empirical, being based on a family of transfer functions that cover the range of beam qualities and compressed breast thicknesses which are encountered clinically. The scatter estimation utilizes optimal information sampling and interpolation (to yield a clinical usable computation time) of scatter calculated using fundamental physics relations. A scatter kernel arising around each primary ray is calculated, and these are summed by superposition to form the scatter image. Beam quality, spatial position in the field (in particular that arising at the air-boundary due to the depletion of scatter contribution from the surroundings), and the possible presence of a grid, are considered, as is tissue composition using an iterative refinement procedure. We present numerous validation results that use a purpose designed tissue equivalent step wedge phantom. The average differences between actual acquisitions and modelled pixel intensities observed across the adipose to fibroglandular attenuation range vary between 5% and 7%, depending on beam quality and, for a single beam quality are 2.09% and 3.36% respectively with and without a grid.

Classification of clusters of microcalcifications in digital breast tomosynthesis.

The detection of microcalcifications, reconstruction of clusters of microcalcifications and their subsequent classification into malignant and benign are important tasks in the early detection of breast cancer. Digital breast tomosynthesis (DBT) provides new opportunities in such tasks. By utilizing the multiple projections in DBT and using the geometry of DBT, we have developed an approach to them based on epipolar curves. It improves the sensitivity and specificity in detection; provides information for estimation of 3D positions of microcalcifications; and facilitates classification. We have generated 15 simulated datasets, each with a microcalcification cluster based on an ellipsoidal shape. We estimate the 3D positions of the microcalcifications in each of the clusters and reconstruct the clusters as ellipsoids. We classify each cluster as malignant or benign based on the parameters of the ellipsoids. The classification result is compared with the ground truth. Our results show that the deviations between the actual and estimated 3D positions of the microcalcification, and the actual and estimated parameters of the ellipsoids are sufficiently small that the classification results are 100% correct. This demonstrates the feasibility in cluster classification in 3D.