NCT06616103
Not yet recruiting
Not Applicable
Utility of Quantitative Imaging Parameters from Deep Learning-based CT Segmentation in Assessing Hepatic Steatosis and Fibrosis in Chronic Hepatitis B: a Prospective Study Using MRI As the Reference Standard
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Chronic Hepatitis B
- Sponsor
- Seoul National University Hospital
- Enrollment
- 111
- Locations
- 1
- Primary Endpoint
- diagnostic performance of CT attenuatio parameters in assessing hepatic steatosis and fibrosis
- Status
- Not yet recruiting
- Last Updated
- last year
Overview
Brief Summary
This study aims to evaluate diagnostic performance of CT attenuation parameters acquired using deep learning algorithm in assessing hepatic steatosis and fibrosis.
Investigators
Jeongin Yoo
Clinical assistant professor
Seoul National University Hospital
Eligibility Criteria
Inclusion Criteria
- •chronic hepatitis B
- •no chronic liver disease other than chronic hepatitis B
- •Body mass index \>= 23
Exclusion Criteria
- •pregnant women
- •unable to perform MRI examinations due to claustrophobia or metallic foreign body
- •suspicious hepatic malignancy on previous imaging studies
- •history of local treatment for hepatic lesions
- •history of surgery or catheter insertion of liver or spleen
Outcomes
Primary Outcomes
diagnostic performance of CT attenuatio parameters in assessing hepatic steatosis and fibrosis
Time Frame: At the time of enrollment
diagnostic performance of CT attenuatio parameters in assessing hepatic steatosis and fibrosis using MRI-PDFF and MR elastography as reference standards
Secondary Outcomes
- Consistency between MRI-derived body composition data and CT-derived data(At the time of enrollment)
Study Sites (1)
Loading locations...
Similar Trials
Completed
Not Applicable
Quantitative Imaging Metrics From CECT in Measuring Disease Response or Progression in Patients With Kidney CancerRenal Cell CarcinomaNCT02370290University of Southern California74
Withdrawn
Not Applicable
CT Biomarkers Identification by Artificial Intelligence for COVID-19 PrognosisCovid-19NCT04418245Centre Hospitalier Universitaire de Nīmes1,000
Terminated
Not Applicable
CT Perfusion Images in Assessing Treatment Response in Patients With Pancreatic CancerPancreatic Ductal AdenocarcinomaNCT03012282University of Washington70
Completed
Not Applicable
Radiographic Characteristics of Mediastinal and Hilar Lymph Nodes in SarcoidosisSarcoidosisLymph Node DiseaseNCT04735302Karadeniz Technical University192
Completed
Not Applicable
The Optimization of a Low-dose Computed Tomography Protocol in Patients With Suspected Uncomplicated Acute AppendicitisAppendicitisOther and Unspecified Acute AppendicitisAcute DiseaseGastrointestinal DiseasesIntra-abdominal InfectionNCT02533869Turku University Hospital60