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NCT Number: NCT06616103

Quantitative CT Imaging Parameters for Assessing Hepatic Steatosis in CHB

This study aims to evaluate diagnostic performance of CT attenuation parameters acquired using deep learning algorithm in assessing hepatic steatosis and fibrosis.

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Key information

Conditions

Chronic Hepatitis B Blood-Borne Infections Chronic Disease Communicable Diseases DNA Virus Infections Digestive System Diseases Disease Attributes Fatty Liver Fibrosis Fibrosis and Cirrhosis of Liver Hepadnaviridae Infections Hepatitis Hepatitis B Hepatitis B, Chronic Hepatitis, Chronic Hepatitis, Viral, Human Infections Liver Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Steatosis of Liver Virus Diseases

Age range

19 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

computerized tomography

Diagnostic Test

noncontrast abdominal CT

Primary outcomes

  1. 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

  1. Consistency between MRI-derived body composition data and CT-derived data

    Time frame: At the time of enrollment

    Consistency between MRI-derived body composition data and CT-derived data using automated 3D organ segmentation algorithm

Sponsors and collaborators

Lead sponsor

Seoul National University Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2024
Primary completion
2025
Study completion
2025
First posted
Sep 27, 2024
Registry last updated
Sep 27, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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