Literature DB >> 30371604

Spleno-hepatic index to predict portal hypertension by equilibrium radionuclide ventriculography.

Laurent Dercle1,2, Chloé Billey3, Thomas Cognet1,2,4, Emmanuelle Cassol1,2, Mathieu Sinigaglia1, Pierre Pascal1,2, Isabelle Berry1,2,5, Philippe Otal6,5, Christophe Bureau3,7, Olivier Lairez1,2,4,5.   

Abstract

BACKGROUND: Structural and morphological changes accompanying liver cirrhosis lead to portal hypertension (PHT), which is the first step of most of the complications in patients with liver cirrhosis. Therefore, the development of noninvasive techniques to detect PHT is crucial for prognosis and treatment. AIM: The aim of our study was to assess the diagnostic performance of a new spleno-hepatic index (SHI) measured from equilibrium radionuclide ventriculography (ERV) images in detecting patients with cirrhotic PHT. METHODS AND
RESULTS: A total of 38 patients with PHT were compared with 30 controls without liver disease. The SHI was measured on the sum of the tomographic images from the ERV and calculated according to the following formula: SHI=(mean splenic count×longest hepatic length)/mean hepatic count. Mean SHI was 54±14 and 36±8 (P<0.001) among patients with PHT and controls, respectively. A cutoff value of 40 for the SHI allowed a sensitivity of 90% and specificity of 77% to detect PHT. SHI greater than 51 was 100% specific. In a subset of 25 patients, SHI was not correlated with hepatic venous pressure gradient measured invasively in the right hepatic vein (R=-0.08, P=0.70).
CONCLUSION: Quantification of SHI derived from ERV could be used to detect liver cirrhosis with PHT although it is not linearly correlated with the hepatic venous pressure gradient. SHI should be considered as a useful index for the identification of PHT in patients referred for the detection/exploration of cirrhotic cardiomyopathy by ERV.

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Year:  2018        PMID: 30371604     DOI: 10.1097/MNM.0000000000000927

Source DB:  PubMed          Journal:  Nucl Med Commun        ISSN: 0143-3636            Impact factor:   1.690


  1 in total

1.  Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.

Authors:  Laurent Dercle; Jingchen Ma; Chuanmiao Xie; Ai-Ping Chen; Deling Wang; Lyndon Luk; Paul Revel-Mouroz; Philippe Otal; Jean-Marie Peron; Hervé Rousseau; Lin Lu; Lawrence H Schwartz; Fatima-Zohra Mokrane; Binsheng Zhao
Journal:  Eur J Radiol       Date:  2020-01-28       Impact factor: 4.531

  1 in total

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