Deep Learning Image Reconstruction for Abdominal CT of Hepatocellular Carcinoma Compared With 3-TESLA MRI

New algorithms for processing CT acquisitions, based on artificial intelligence, have been reported to improve acquisition quality. Thats' why it's possible to imagine that new scan post-processing algorithms enable better detection and characterization of hepatocellular carcinoma lesions than with standard reconstructions. DLIR reconstructions could even match with MRI detection.

The aim of the study is to compare the detection and characterization of hepatic lesions according to the LI-RADS classification in CT with DLIR artificial intelligence reconstruction, compared with ASIR-V reconstruction and the gold standard of MRI.

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Key information

Conditions

Hepatocellular Carcinoma Adenocarcinoma Carcinoma Carcinoma, Hepatocellular Digestive System Diseases Digestive System Neoplasms Liver Diseases Liver Neoplasms Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

CHRU de Nancy

Nancy, 5400, France

Who can participate

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

Inclusion criteria

undergoing CT and MRI scans in the same week, with protocols dedicated to the detection of HCC lesions

Exclusion criteria

imaging with radiological artefact

Treatment and study plan

Primary outcomes

Lesion size in mm

Time frame: Through study completion, an average of 1 year
Hypervascular appearance of lesion

Time frame: Through study completion an average of 1 year

Qualitative measurement: presence or absence of hypervascular lesion
Hypervascular capsule

Time frame: Through study completion an average of 1 year

Qualitative measurement : presence or absence of hypervascular capsule

Secondary outcomes

Infiltrative nature Classification of the hepatic lesion by LI-RADS in MRI

Time frame: Through study completion an average of 1 year

Sponsors and collaborators

Lead sponsor

Central Hospital, Nancy, France

Other

Registry information

Important dates

Study start: 2023
Primary completion: 2023
Study completion: 2023
First posted: Sep 14, 2023
Registry last updated: Feb 19, 2025

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.

Published trials that share one or more normalized conditions with this study.

Completed