Clinical trial NCT05068492: A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104)
Official title: A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104): A Prospective, Multicenter Study
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Portal Hypertension
Keywords: Cirrhosis
Portal hypertension
Artificial intelligence
CT
MRI
Ultrasound
Lead sponsor: Hepatopancreatobiliary Surgery Institute of Gansu Province
Sponsor class: OTHER
Collaborators: name: Portal Hypertension Alliance in China
First posted: 2021-10-05
Enrollment: 2000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Diagnostic value
timeFrame: 24 months
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
name: Xiaolong Qi, Prof.
role: PRINCIPAL_INVESTIGATOR
References:
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.
citation: 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.