MedTrace Logo

Artificial Intelligence vs. LIRADS in Diagnosing HCC on CT

NCT04843176 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 250

Last updated 2022-05-18

No results posted yet for this study

Summary

Liver cancer is the sixth most commonly diagnosed cancer and the fourth leading cause of cancer death worldwide. It is the 3rd most common cause of cancer death in Hong Kong. The five-year survival rates of liver cancer differ greatly with disease staging, ranging from 91.5% in early-stage to 11% in late-stage. The early and accurate diagnosis of liver cancer is paramount in improving cancer survival. Liver cancer is diagnosed radiologically via cross sectional imaging, e.g. computed tomography (CT), without the routine use of liver biopsy. However, with current internationally-recommended radiological reporting methods, up to 49% of liver lesions may be inconclusive, resulting in repeated scans and a delay in diagnosis and treatment. An artificial intelligence (AI) algorithm that that can accurately diagnosed liver cancer has been developed. Based on an interim analysis, the algorithm achieved a high diagnostic accuracy. The AI algorithm is now ready for implementation.

This study aims to prospective validate this AI algorithm in comparison with the current standard of radiological reporting in a randomized manner in the at-risk population undergoing triphasic contrast CT. This research project is totally independent and separated from the actual clinical reporting of the CT scan by the duty radiologist. The primary study outcome is the diagnostic accuracy of liver cancer, which will be unbiasedly based on a composite clinical reference standard.

Conditions

Interventions

DIAGNOSTIC_TEST

Prototype artificial intelligence algorithm

Developed by the University of Hong Kong

DIAGNOSTIC_TEST

LI-RADS

The Liver Imaging Reporting and Data System (LI-RADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC

Sponsors & Collaborators

  • Education University of Hong Kong

    collaborator OTHER

  • The University of Hong Kong

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-03-19
Primary Completion
2025-12-31
Completion
2026-06-30

Countries

  • Hong Kong

Study Locations

More Related Trials

Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04843176 on ClinicalTrials.gov