Portal HypertensionNCT05068492
A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104)
An observational study of Portal Hypertension, run by Hepatopancreatobiliary Surgery Institute of Gansu Province.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Portal Hypertension
- What is being tested?
- CT, MRI and HVPG
- How many people
- 2,000 participants
- Where
- No sites listed yet
- Study dates
- 10 Dec 2023to 1 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- age > 18 years old;
- confirmed cirrhosis (laboratory, imaging and clinical symptoms);
- with ultrasound/CT/MRI within 1 month prior to HVPG measurement;
- written informed consent.
Who cannot join
- any previous liver or spleen surgery;
- liver cancer; chronic acute liver failure;
- acute portal hypertension;
- unreliable HVPG or ultrasound/CT/MRI results due to technical reasons.
- with liver interventional therapy between HVPG and ultrasound/CT/MRI
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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.
Read the 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.
- Conditions
- Portal Hypertension
- Being tested
- CT, MRI, HVPG, Ultrasound
- Keywords
- Cirrhosis, Portal hypertension, Artificial intelligence, CT, MRI, Ultrasound
- Medical subject headings
- Hypertension, Portal, Fibrosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
CTDiagnostic test
enhanced CT with standard procedure
MRIDiagnostic test
enhanced MRI with standard procedure
HVPGDiagnostic test
HVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures
UltrasoundDiagnostic test
Digestive ultrasound with standard procedure
| Study group | Role | What they receive |
|---|---|---|
| Training cohort | Training cohort was set to develop the novel non-invasive model for virtual HVPG Receives: Diagnostic Test: CT; Diagnostic Test: MRI; Diagnostic Test: HVPG; Diagnostic Test: Ultrasound | |
| Validation cohort | Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments Receives: Diagnostic Test: CT; Diagnostic Test: MRI; Diagnostic Test: HVPG; Diagnostic Test: Ultrasound | |
| Test cohort | Test cohort was set to test the novel non-invasive model for virtual HVPG in different environments Receives: Diagnostic Test: CT; Diagnostic Test: MRI; Diagnostic Test: HVPG; Diagnostic Test: Ultrasound |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic value
Accuracy of the novel model for virtual HVPG
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Oct 2021
- Study start10 Dec 2023 (estimated)
- Primary completion1 Dec 2025 (estimated)
- Study completion1 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Portal HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 2,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hepatopancreatobiliary Surgery Institute of Gansu Province
- Sponsor type
- University, hospital or other
- Collaborators
- Portal Hypertension Alliance in China
- Responsible party
- Principal investigator, Xiaolong Qi, Hepatopancreatobiliary Surgery Institute of Gansu Province
- Investigators
- Xiaolong Qi, Prof., Principal investigator, CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05068492
- Sponsor's protocol ID
- CHESS2104
- Status last verified
- Apr 2023
- First submitted
- 27 Sep 2021
- Last updated
- 25 Apr 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 32205218Liu 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.
PubMed 30268833
Study at a glance
From the registry record, last updated by the sponsor on 25 Apr 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Novel Imaging Based Quantitative Model-aided Detection of Portal Hypertension in Patients With Cirrhosis (CHESS2104): A Prospective, Multicenter Study
- ClinicalTrials.gov ID
- NCT05068492
- Acronym / sponsor ID
- CHESS2104
- Conditions
- Portal Hypertension
- Keywords
- Cirrhosis, Portal hypertension, Artificial intelligence, CT, MRI, Ultrasound
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 2,000 participants (estimated)
- Lead sponsor
- Hepatopancreatobiliary Surgery Institute of Gansu ProvinceUniversity, hospital or other
- Collaborators
- Portal Hypertension Alliance in China
- Study start
- 10 Dec 2023 (estimated)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 1 Dec 2025 (estimated)
- First posted
- 5 Oct 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Training cohort | – | Diagnostic Test: CT, Diagnostic Test: MRI, Diagnostic Test: HVPG, Diagnostic Test: UltrasoundTraining cohort was set to develop the novel non-invasive model for virtual HVPG |
| Validation cohort | – | Diagnostic Test: CT, Diagnostic Test: MRI, Diagnostic Test: HVPG, Diagnostic Test: UltrasoundValidation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments |
| Test cohort | – | Diagnostic Test: CT, Diagnostic Test: MRI, Diagnostic Test: HVPG, Diagnostic Test: UltrasoundTest cohort was set to test the novel non-invasive model for virtual HVPG in different environments |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- age > 18 years old;
- confirmed cirrhosis (laboratory, imaging and clinical symptoms);
- with ultrasound/CT/MRI within 1 month prior to HVPG measurement;
- written informed consent.
Key exclusion criteria (5 listed)
- any previous liver or spleen surgery;
- liver cancer; chronic acute liver failure;
- acute portal hypertension;
- unreliable HVPG or ultrasound/CT/MRI results due to technical reasons.
- with liver interventional therapy between HVPG and ultrasound/CT/MRI
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| CTenhanced CT with standard procedure | Diagnostic test | Test cohort, Training cohort, Validation cohort |
| MRIenhanced MRI with standard procedure | Diagnostic test | Test cohort, Training cohort, Validation cohort |
| HVPGHVPG measurements are performed by well-trained interventional radiologists in accordance with standard operating procedures | Diagnostic test | Test cohort, Training cohort, Validation cohort |
| UltrasoundDigestive ultrasound with standard procedure | Diagnostic test | Test cohort, Training cohort, Validation cohort |
Primary outcome
Diagnostic value
Time frame: 24 months
Accuracy of the novel model for virtual HVPG
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Apr 2023.
- Background: PubMed 32205218Liu 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.
- Background: PubMed 30268833Liu 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xiaolong Qi, Prof. principal investigator
CHESS Center, Institute of Portal Hypertension, The First Hospital of Lanzhou University, Lanzhou, China
Questions about this trial
What is this clinical trial studying?
An observational study of Portal Hypertension, run by Hepatopancreatobiliary Surgery Institute of Gansu Province.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 25 Apr 2023. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yifei Huang (15800004518, huangyf1995@foxmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic value (time frame: 24 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05068492, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Apr 2023.