A method for exploratory repeated-measures analysis applied to a breast-cancer screening study

When a model may be fitted separately to each individual statistical unit, inspection of the point estimates may help the statistician to understand between-individual variability and to identify possible relationships. However, some information will be lost in such an approach because estimation uncertainty is disregarded. We present a comparative method for exploratory repeated-measures analysis to complement the point estimates that was motivated by and is demonstrated by analysis of data from the CADET II breast-cancer screening study. The approach helped to flag up some unusual reader behavior, to assess differences in performance, and to identify potential random-effects models for further analysis.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS481 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Classification of linear structures in mammographic images

We have developed a novel technique for both the detection and anatomical classification of linear structures in mammographic images. Both stages are based on the statistical modelling of scale-orientation signatures. All experiments are based on a leave-one-out approach. The detection stage has been presented before [1]. For the classification stage real mammographic data was used. As the signatures incorporate scale aspects it is no longer essential to extract scale information which was previously shown to be a bottleneck in obtaining acceptable classification [2]. Spicule classification results show major improvements at low sensitivity and only moderate degradation at high sensitivity. We present spicule images based on both the detection and classification stages

Automatic Classification of Linear Structures in Mammographic Images

Certain kinds of abnormalities in x-ray mammograms are associated with specific anatomical structures – in particular, linear structures. This association can, in principle, be exploited to improve the specificity and sensitivity with which the abnormalities can be detected. We compare annotated and the automatic detection of the scale and orientation associated with linear structure in mammograms. We investigate methods of classifying the detected structures into anatomical classes (spicules, vessel, duct, fibrous tissue etc) from their cross-sectional profiles. Automatic (linear and non-linear) classification results are compared with expert annotations using receiver operating characteristic analysis. We show that useful discrimination between anatomical classes is achieved. Some of this relies on simple attributes such as the width and contrast of the profile, but there is also important information carried by the shape of the profile

Preliminary observations of breast tumor collagen using synchrotron radiation

The most frequently occurring cancer in women is that of the breast where it accounts for almost 20% of all cancer deaths. The U.K. has the world's highest mortality rate from breast cancer with an increasing incidence of 25000 per annum. Characterizing the complex physiological and tissue changes that form the natural history of breast cancer is clearly important for understanding associated biological mechanisms and for diagnosis. We report the initial findings of a diffraction study of breast tissue collagen that we believe may be due to tumor genesis. Small angle, synchrotron X-ray scattering has enabled us to examine `core cut' biopsy specimens and characterize their collagen architecture. We present data that demonstrates possible structural differences between tumor and normal tissue. We discuss the implications of these findings in the context of using molecular structure characteristics as new and novel markers of disease progression