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

Portal Hypertension Clinical Trial

Official title:

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

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05068492
• Other study ID #: CHESS2104
• Status: Not yet recruiting
• Start date: December 10, 2023
• Est. completion date: December 1, 2025

Study information

• Verified date: April 2023
• Source: Hepatopancreatobiliary Surgery Institute of Gansu Province
• Contact: Yifei Huang
• Phone: 15800004518
• Email: huangyf1995[@]foxmail.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 2000
• Est. completion date: December 1, 2025
• Est. primary completion date: December 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Fibrosis
• Hypertension
• Hypertension, Portal
• Portal Hypertension

Intervention

Diagnostic Test:
• CT
enhanced CT with standard procedure
• MRI
enhanced MRI with standard procedure
• HVPG
HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures
• Ultrasound
Digestive ultrasound with standard procedure

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
Hepatopancreatobiliary Surgery Institute of Gansu Province	Portal Hypertension Alliance in China

References & Publications (2)

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. — View Citation

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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Diagnostic value	Accuracy of the novel model for virtual HVPG	24 months