A Novel Imging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis

• Sponsor: Hepatopancreatobiliary Surgery Institute of Gansu Province (Other)
• Overall Status: Not yet recruiting
• CT.gov ID: NCT05068492
• Collaborator: Chinese Portal hypertension Alliance (Other), Interventional Medicine Branch of Chinese Hospital Association (Other)
• Enrollment: 2,000
• Anticipated Duration (Months): 24.3

Study Details

Study Description

Brief Summary

Noninvasive and accurate methods are needed to identify patients with clinically significant portal hypertension (CSPH). We plan to investigate the ability of AI analysis of computed tomography (CT) or magnetic resonance (MR) to identify patients with CSPH.

Condition or Disease	Intervention/Treatment	Phase
Portal Hypertension	Device: CT; Device: MRI; Device: HVPG

Study Design

• Study Type: Observational
• Anticipated Enrollment: 2000 participants
• Observational Model: Cohort
• Time Perspective: Cross-Sectional
• Official Title: A Novel Imging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104): A Nationwide Multicenter Study
• Anticipated Study Start Date: Oct 23, 2021
• Anticipated Primary Completion Date: Nov 1, 2022
• Anticipated Study Completion Date: Nov 1, 2023

Arms and Interventions

Arm	Intervention/Treatment
Training cohort	Device: CT; enhanced CT with standard procedure; Device: MRI; enhanced MRI with standard procedure; Device: HVPG; HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures
Validation cohort	Device: CT; enhanced CT with standard procedure; Device: MRI; enhanced MRI with standard procedure; Device: HVPG; HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures
Test cohort	Device: CT; enhanced CT with standard procedure; Device: MRI; enhanced MRI with standard procedure; Device: HVPG; HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures

Outcome Measures

Primary Outcome Measures

1. Diagnostic value [12 months]

   With HVPG as the gold standard, a non-invasive HVPG model was established and validated based on enhanced computed tomography (CT) or enhanced magnetic resonance imaging (MRI) combined with artificial intelligence to evaluate portal vein hypertension

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 85 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Inclusion Criteria:

• confirmed cirrhosis (laboratory, imaging and clinical symptoms); with enhanced abdominal CT scan within 14 days prior to HVPG measurement; age > 18 years old; written informed consent.

Exclusion Criteria:

• any previous liver or spleen surgery; liver cancer; chronic acute liver failure; acute portal hypertension; unreliable HVPG or CT results due to technical reasons.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

• Hepatopancreatobiliary Surgery Institute of Gansu Province
• Chinese Portal hypertension Alliance
• Interventional Medicine Branch of Chinese Hospital Association

Investigators

• Principal Investigator: Xiaolong Qi, Prof., CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China

Study Documents (Full-Text)

None provided.

More Information

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, Örmeci 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, Akçalar S, Çelebioğlu E, Üstüner E, Bilgiç S, Ellik Z, Asiller ÖÖ, 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.

• Responsible Party: Xiaolong Qi, Chief, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Hepatopancreatobiliary Surgery Institute of Gansu Province
• ClinicalTrials.gov Identifier: NCT05068492
• Other Study ID Numbers: CHESS2104
• First Posted: Oct 5, 2021
• Last Update Posted: Oct 5, 2021
• Last Verified: Sep 1, 2021

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Xiaolong Qi, Chief, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Hepatopancreatobiliary Surgery Institute of Gansu Province: cirrhosis; portal hypertension; AI; CT; MRI
• Additional relevant MeSH terms: Hypertension, Portal; Hypertension; Fibrosis

Study Results

No Results Posted as of Oct 5, 2021