NCT04843176

Artificial Intelligence vs. LIRADS in Diagnosing HCC on CT

Recruiting now NA Last updated 18 May 2022

What this trial tests

NA trial testing Prototype artificial intelligence algorithm in HCC in 250 participants. Currently enrolling.

Timeline

19 March 2021

Primary endpoint
31 December 2025

30 June 2026

Quick facts

Lead sponsor	The University of Hong Kong
Phase	NA
Status	Recruiting now
Study type	INTERVENTIONAL
Allocation	randomized
Design	parallel
Masking	single
Primary purpose	diagnostic
Enrollment	250
Start date	19 March 2021
Primary completion	31 December 2025
Estimated completion	30 June 2026
Sites	1 location across Hong Kong

Drugs / interventions tested

• Prototype artificial intelligence algorithm
• LI-RADS

Conditions studied

• HCC — all drugs for HCC →
• Liver Cancer — all drugs for Liver Cancer →

Sponsor

The University of Hong Kong

Who can join

18 and older, any sex, with HCC or Liver Cancer. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

1. Artificial intelligence for the prevention and clinical management of hepatocellular carcinoma.
   Calderaro J, Seraphin TP, Luedde T, Simon TG. · · 2022 · cited 196× · PMID 35589255 · DOI 10.1016/j.jhep.2022.01.014
2. Bridging the Gap Between Imaging and Molecular Characterization: Current Understanding of Radiomics and Radiogenomics in Hepatocellular Carcinoma.
   Ren L, Chen DB, Yan X, She S, et al · · 2024 · cited 2× · PMID 39619602 · DOI 10.2147/jhc.s423549
3. [Diagnosis and treatment of liver diseases in the era of artificial intelligence].
   Ren LY, Chen DB, Chen HS. · · 2025 · PMID 41461549 · DOI 10.3760/cma.j.cn501113-20250910-00376