Identifying and modelling clinical subpopulations from the Malmö breast tomosynthesis screening trial

Virtual Clinical Trials (VCT) are an effective tool to evaluate the performance of novel imaging systems using computer simulations. VCT results depend on the selection of virtual patient populations. In the case of breast imaging, virtual patients should be matched to a desired clinical population in terms of selected anatomical or demographic descriptors. We are developing a virtual population of women who participated in the Malmö Breast Tomosynthesis Screening Trial (MBTST). We have used clinical values of the compressed breast thickness and volumetric breast density to develop a multidimensional distribution of women in MBTST. Breast density and thickness values were obtained from anonymized, previously collected tomosynthesis images of 14,746 women. In this paper, we compare several approaches to identify clinical subpopulations and select virtual patients that represent various groups of clinical subjects. We performed two methods to identify clinical subpopulations by clustering clinical data using the K-means algorithm or woman's age. The obtained clusters have been explored and compared using the silhouette mean. The K-means algorithm yielded grouping of MBTST data into two clusters; however, that grouping was, shown to be suboptimal by the silhouette analysis. The agebased clustering showed significant overlap in terms of breast thickness and density. We also compared two approaches to select sets of representative phantoms. Our analysis has emphasized benefits and limitations of different clustering methods. The preferred method depends on the specific task that should be addressed using VCTs. Simulation of representative phantoms is ongoing. Potential correlations with pathological findings and/or parenchymal properties will be investigated

Evaluation of non-Gaussian statistical properties in virtual breast phantoms

Images derived from a "virtual phantom" can be useful in characterizing the performance of imaging systems. This has driven the development of virtual breast phantoms implemented in simulation environments. In breast imaging, several such phantoms have been proposed. We analyze the non-Gaussian statistical properties from three classes of virtual breast phantoms and compare them to similar statistics from a database of breast images. These include clustered-blob lumpy backgrounds (CBLBs), truncated binary textures, and the UPenn virtual breast phantoms. We use Laplacian fractional entropy (LFE) as a measure of the non-Gaussian statistical properties of each simulation procedure. Our results show that, despite similar power spectra, the simulation approaches differ considerably in LFE with very low scores for the CBLB to high values for the UPenn phantom at certain frequencies. These results suggest that LFE may have value in developing and tuning virtual phantom simulation procedures

"In Europe it's Different": Homonationalism and Peripheral Desires for Europe

The term \u2018homonationalism\u2019 has been coined to designate the assimilation of homosexuality by Western nationalist projects (Puar 2007). First appearing in the US context, the term has been taken up in Europe to criticize the deployment of progressive sexual imaginaries in anti-immigration politics, particularly in France, Germany, the Netherlands and the United Kingdom. Yet, a question remains open as to how we shall conceptualize this phenomenon on the European scale; that is, how to think of European homonationalism. This chapter suggests a way of understanding homonationalism as a European phenomenon by taking the case of Italy as an entry point. Through the analysis of legal, political and cultural texts (including LGBT activist discourses), the chapter elucidates the divide between \u2018progressive\u2019 Europe and \u2018backward\u2019 Italy, illustrating how Europe is produced at its Southern borders as a site of sexual exceptionalism