Portal hypertension: a comparison between portal-venous pressure measurement and ARFI measurement of liver and spleen stiffness

PURPOSE. Portal pressure is measured invasively as Hepatic Venous Pressure Gradient (HVPG) in the angiography room. Liver stiffness measured by Fibroscan was shown to correlate with HVPG values below 12 mmHg. This is not surprising, since in cirrhosis the increase of portal pressure is not directly linked with liver fibrosis and consequently to liver stiffness. We hypothesized that, given the spleen’s privileged location upstream to the whole portal system, splenic stiffness could provide relevant information about portal pressure. Aim of the study was to assess the relationship between liver and spleen stiffness measured by Virtual Touch™ (ARFI) and HVPG in cirrhotic patients. METHODS. 40 consecutive patients (30 males, mean age 62y, mean BMI=26, mean Child-Pugh A6, mean platelet count=92.000/mmc, 19 HCV+, 7 with ascites) underwent to ARFI stiffness measurement (10 valid measurements in right liver lobe both surface and centre, left lobe and 20 in the spleen) and HPVG, blindly to each other. Median ARFI values of 10 samplings on every liver area and of 20 samplings on spleen were calculated. RESULTS. Stiffness could be easily measured in all patients with ARFI, resulting a mean of 2,61±0,76, 2,5±0,62 and 2,55±0,66 m/sec in the liver areas and 3.3±0,5 m/s in the spleen. Median HPVG was 14 mmHg (range 5-27); 28 patients showed values ≥10 mmHg. A positive significant correlation was found between spleen stiffness and HPVG values (r=0.744, p<0.001). No significant correlation was found between all liver stiffness and HVPG (p>0,05). AUROC was calculated to test spleen stiffness ability in discriminating patients with HVPG ≥10. AUROC = 0.911 was obtained, with sensitivity of 69% and specificity of 91% at a cut-off of 3.26 m/s. CONCLUSION. Spleen stiffness measurement with ARFI correlates with HVPG in patients with cirrhosis, with a potential of identifying patients with clinically significant portal hypertension.

Differential expression analysis for sequence count data via mixtures of negative binomials

The recent advent of Next-generation sequencing technologies has revolutionized the way of analyzing the genome. This innovation allows to get deeper information at a lower cost and in less time, and provides data that are discrete measurements. One of the most important applications with these data is the differential analysis, that is investigating if one gene exhibit a different expression level in correspondence of two (or more) biological conditions (such as disease states, treatments received and so on). As for the statistical analysis, the final aim will be statistical testing and for modeling these data the Negative Binomial distribution is considered the most adequate one especially because it allows for "over dispersion". However, the estimation of the dispersion parameter is a very delicate issue because few information are usually available for estimating it. Many strategies have been proposed, but they often result in procedures based on plug-in estimates, and in this thesis we show that this discrepancy between the estimation and the testing framework can lead to uncontrolled first-type errors. We propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Afterwards, three consistent statistical tests are developed for differential expression analysis. We show that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it is the best one in reaching the nominal value for the first-type error, while keeping elevate power. The method is finally illustrated on prostate cancer RNA-seq data.