Percutaneous real-time sonoelastography as a non-invasive tool for the characterization of solid focal liver lesions: A prospective study.

BACKGROUND: Real-time sonoelastography is currently used for the characterization of superficial solid lesions such as thyroid and breast masses. This study evaluates the usefulness of percutaneous sonoelastography for the characterization of solid focal liver lesions. METHODS: 30 out of 43 patients with 38 known liver lesions were included in a prospective, diagnostic study. Qualitative analysis (pattern of deformation, elasticity type of liver tumour) and semi-quantitative measurements (strain ratio, hardness percentage, histogram) were evaluated. Sensitivity, specificity, positive and negative predictive values were calculated and the area under the receiver operating characteristics curve was constructed. RESULTS: Patterns A and C-D are specific of benign lesions and metastases respectively. The patterns for haemangiomas, focal nodular hyperplasia and metastases were significantly different to each other in terms of strain ratio, hardness percentage and histogram (p<0.05). A statistically significant difference (p<0.001) was observed between the median values of the 3 measured parameters for benign (1.02; 12%; 47) and malignant lesions (1.66; 65%; 20.5) respectively. The area under the receiver operating characteristics curve values for strain ratio, hardness percentage and histogram were 0.88, 0.89, and 0.86 respectively for cut-off values of 1.2, 45, and 30. CONCLUSIONS: By percutaneous sonoelastography it is possible to differentiate benign versus malignant focal liver lesions, metastases in particular, with good diagnostic performance.

Wilks' Λ dissimilarity measures for gene clustering: an approach based on the identification of transcription modules.

Clustering methods are widely used in the analysis of microarray data for their ability to uncover coordinated expression profiles. One important goal of clustering is to discover coregulated genes because it has been postulated that genes targeted by the same transcription factors tend to show similar expression patterns. We focus on agglomerative hierarchical clustering and consider the problem of choosing a dissimilarity measure on the basis of its ability to identify functional modules consisting of a transcription factor and the associated target genes. We first propose two criteria that constitute a theoretical framework for assessing the adequacy and comparing different dissimilarity measures. We show that the proposed criteria allow one to gain insight into the behavior of dissimilarity measures and lead to a ranking of some of the most commonly used dissimilarity measures. Next, we introduce two dissimilarity measures based on the Wilks' Λ statistic and show that, according to the above criteria, they have better performance than the other considered measures. The theoretical results are supported by an applied analysis on both simulated and real data.