A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104)

Not yet recruiting

• Conditions: Portal Hypertension
• Interventions: Diagnostic Test: CT; Diagnostic Test: MRI; Diagnostic Test: HVPG; Diagnostic Test: Ultrasound

• Registration Number: NCT05068492
• Lead Sponsor: Hepatopancreatobiliary Surgery Institute of Gansu Province

Brief Summary

How to construct a novel, non-invasive, accurate, and convenient method to achieve prediction of hepatic venous pressure gradient (HVPG) is an important general problem in the management of portal hypertension in cirrhosis. We plan to investigate the ability of AI analysis of Ultrasound, computed tomography (CT) or magnetic resonance (MR) to establish a risk stratification system and perform tailored management for portal hypertension in cirrhosis.

Detailed Description

China suffers the heaviest burden of liver disease in the world. The number of chronic liver disease is more than 400 million. Either viral-related hepatitis, alcoholic hepatitis, or metabolic-related fatty hepatitis, etc. may progress to cirrhosis, which greatly threatens public health. Portal hypertension is a critical risk factor that correlates with clinical prognosis of patients with cirrhosis. According to the Consensus on clinical application of hepatic venous pressure gradient in China (2018), hepatic venous pressure gradient (HVPG) greater than 10,12,16,20 mmHg correspondingly predicts different outcomes of patients with cirrhosis portal hypertension. It is of great significance to establish a risk stratification system and perform tailored management for portal hypertension in cirrhosis. As a universal gold standard for diagnosing and monitoring portal hypertension, HVPG remains limitation for clinical application due to its invasiveness. How to construct a novel, non-invasive, accurate, and convenient method to achieve prediction of HVPG is an important general problem in the management of portal hypertension in cirrhosis.

The development of radiomics technique provides an approach to solve abovementioned clinical issues. Based on artificial intelligence algorithms, radiomics harnesses mineable, high-resolution, and quantitative features from encrypted medical images, along with clinical or genetic data to produce evidence-based decision support system, to achieve the clinical targets including diagnosis, treatment effect evaluation, and prognosis prediction. In this project, aiming at development of a risk stratification system for hypertension management in cirrhosis, we will construct a standard-of-care database and utilize radiomics tool to construct the decision making system. We will take responsibility for achievement of organ and vessel segmentation, radiomic feature selection, and signature construction for prediction of hypertension classification, and accomplish the development of prototype system which would integrate four modules including database management, HVPG risk stratification application module, predicted outcome presentation module, and prognostic information curation module. This project will focus on two aspects which are correspondingly machine learning algorithms optimization and prototype system development, so as to promote the precision medicine in liver disease.

Study Timeline

• Study First Submitted: 2021-09-27
• Study First Submitted QC: 2021-09-27
• Study First Posted: 2021-10-05
• Status Verified: 2023-04
• Last Update Submitted: 2023-04-23
• Last Update Posted: 2023-04-25
• Study Start: 2023-12-10
• Primary Completion: 2025-12-01
• Study Completion: 2025-12-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 2000

Inclusion Criteria

1. age > 18 years old;
2. confirmed cirrhosis (laboratory, imaging and clinical symptoms);
3. with ultrasound/CT/MRI within 1 month prior to HVPG measurement;
4. written informed consent.

Exclusion Criteria

1. any previous liver or spleen surgery;
2. liver cancer; chronic acute liver failure;
3. acute portal hypertension;
4. unreliable HVPG or ultrasound/CT/MRI results due to technical reasons.
5. with liver interventional therapy between HVPG and ultrasound/CT/MRI

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Test cohort	Ultrasound	Test cohort was set to test the novel non-invasive model for virtual HVPG in different environments
Training cohort	CT	Training cohort was set to develop the novel non-invasive model for virtual HVPG
Training cohort	Ultrasound	Training cohort was set to develop the novel non-invasive model for virtual HVPG
Validation cohort	CT	Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments
Validation cohort	MRI	Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments
Validation cohort	HVPG	Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments
Test cohort	HVPG	Test cohort was set to test the novel non-invasive model for virtual HVPG in different environments
Validation cohort	Ultrasound	Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments
Training cohort	MRI	Training cohort was set to develop the novel non-invasive model for virtual HVPG
Training cohort	HVPG	Training cohort was set to develop the novel non-invasive model for virtual HVPG
Test cohort	CT	Test cohort was set to test the novel non-invasive model for virtual HVPG in different environments
Test cohort	MRI	Test cohort was set to test the novel non-invasive model for virtual HVPG in different environments

Primary Outcome Measures

Name	Time	Method
Diagnostic value	24 months	Accuracy of the novel model for virtual HVPG