An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension.

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (HVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools.

Noncontrast-enhanced MRI-based Noninvasive Score for Portal Hypertension (CHESS1802): An International Multicenter Study.

Background And Aims: This study aimed to determine the performance of the non-invasive score using noncontrast-enhanced MRI (CHESS-DIS score) for detecting portal hypertension in cirrhosis.

Methods: In this international multicenter, diagnostic study (ClinicalTrials.gov, NCT03766880), patients with cirrhosis who had hepatic venous pressure gradient (HVPG) measurement and noncontrast-enhanced MRI were prospectively recruited from four university hospitals in China (=4) and Turkey (=1) between December 2018 and April 2019.

Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis.

Background & Aims: Noninvasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We investigated the ability of deep convolutional neural network (CNN) analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH.

Methods: We collected liver and spleen images from patients who underwent contrast-enhanced CT or MR analysis within 14 days of transjugular catheterization for hepatic venous pressure gradient measurement.