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

A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104): A Prospective, Multicenter Study

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Portal Hypertension
• Intervention / Treatment: Diagnostic test: CT; Diagnostic test: MRI; Diagnostic test: HVPG; Diagnostic test: Ultrasound

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 Type: Observational
• Enrollment (Anticipated): 2000

Contacts and Locations

• Study Contact: Name: Yifei Huang; Phone Number: 15800004518; Email: huangyf1995@foxmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 85 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Organize paticipating units to collect standard-of-care data including radiological and clinical data. Patients diagnosed with cirrhosis who received HVPG measurement and enhanced abdominal ultrasound/CT/MRI scan should be enrolled.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Training cohort; Training cohort was set to develop the novel non-invasive model for virtual HVPG	Diagnostic test: CT; enhanced CT with standard procedure; Diagnostic test: MRI; enhanced MRI with standard procedure; Diagnostic test: HVPG; HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures; Diagnostic test: Ultrasound; Digestive ultrasound with standard procedure
Validation cohort; Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments	Diagnostic test: CT; enhanced CT with standard procedure; Diagnostic test: MRI; enhanced MRI with standard procedure; Diagnostic test: HVPG; HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures; Diagnostic test: Ultrasound; Digestive ultrasound with standard procedure
Test cohort; Test cohort was set to test the novel non-invasive model for virtual HVPG in different environments	Diagnostic test: CT; enhanced CT with standard procedure; Diagnostic test: MRI; enhanced MRI with standard procedure; Diagnostic test: HVPG; HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures; Diagnostic test: Ultrasound; Digestive ultrasound with standard procedure

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Diagnostic value	Accuracy of the novel model for virtual HVPG	24 months

Collaborators and Investigators

• Sponsor: Hepatopancreatobiliary Surgery Institute of Gansu Province
• Collaborators: Portal Hypertension Alliance in China
• Investigators: Principal Investigator: Xiaolong Qi, Prof., CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China

Publications and helpful links

General Publications

• Liu F, Ning Z, Liu Y, Liu D, Tian J, Luo H, An W, Huang Y, Zou J, Liu C, Liu C, Wang L, Liu Z, Qi R, Zuo C, Zhang Q, Wang J, Zhao D, Duan Y, Peng B, Qi X, Zhang Y, Yang Y, Hou J, Dong J, Li Z, Ding H, Zhang Y, Qi X. Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. EBioMedicine. 2018 Oct;36:151-158. doi: 10.1016/j.ebiom.2018.09.023. Epub 2018 Sep 27.
• Liu Y, Ning Z, Ormeci N, An W, Yu Q, Han K, Huang Y, Liu D, Liu F, Li Z, Ding H, Luo H, Zuo C, Liu C, Wang J, Zhang C, Ji J, Wang W, Wang Z, Wang W, Yuan M, Li L, Zhao Z, Wang G, Li M, Liu Q, Lei J, Liu C, Tang T, Akcalar S, Celebioglu E, Ustuner E, Bilgic S, Ellik Z, Asiller OO, Liu Z, Teng G, Chen Y, Hou J, Li X, He X, Dong J, Tian J, Liang P, Ju S, Zhang Y, Qi X. Deep Convolutional Neural Network-Aided Detection of Portal Hypertension in Patients With Cirrhosis. Clin Gastroenterol Hepatol. 2020 Dec;18(13):2998-3007.e5. doi: 10.1016/j.cgh.2020.03.034. Epub 2020 Mar 21.

Study record dates

Study Major Dates

• Study Start (Anticipated): December 10, 2023
• Primary Completion (Anticipated): December 1, 2025
• Study Completion (Anticipated): December 1, 2025

Study Registration Dates

• First Submitted: September 27, 2021
• First Submitted That Met QC Criteria: September 27, 2021
• First Posted (Actual): October 5, 2021

Study Record Updates

• Last Update Posted (Actual): April 25, 2023
• Last Update Submitted That Met QC Criteria: April 23, 2023
• Last Verified: April 1, 2023

More Information

Terms related to this study

• Keywords: MRI; Ultrasound; CT; Cirrhosis; Artificial intelligence; Portal hypertension
• Additional Relevant MeSH Terms: Digestive System Diseases; Pathologic Processes; Cardiovascular Diseases; Vascular Diseases; Liver Diseases; Fibrosis; Hypertension; Hypertension, Portal
• Other Study ID Numbers: CHESS2104

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No