A Prototype AI Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing HCC on CT (Hepatocellular)

May 14, 2026 updated by: The University of Hong Kong

A Prototype Artificial Intelligence Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing Hepatocellular Carcinoma on Computed Tomography: a Randomized Trial

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 to compare the diagnostic performance of the prototype AI algorithm versus LI-RADS criteria in determining HCC on CT in the at-risk population.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Liver cancer is the sixth most commonly diagnosed cancer and the fourth leading cause of cancer death worldwide. The main disease burden is found in East Asia, in which the age-standardized incidence is 26.8 and 8.7 per 100,000 in men and women respectively. In 2017, among the top 10 most common cancers in Hong Kong, liver cancer had the highest case fatality rate of 84.6%. The five-year survival rates of hepatocellular carcinoma (HCC) differ greatly with disease staging, ranging from 91.5% in <2 cm with surgical resection to 11% in >5 cm with adjacent organ involvement. The early and accurate diagnosis of HCC is paramount in improving cancer survival.

Unlike other common cancers, HCC is diagnosed by highly characteristic dynamic patterns on contrast-enhanced cross sectional imaging, without the need of pathological confirmation. The Liver Imaging Reporting and Data System (LI-RADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC. However, up to 49% of nodules identified in computed tomography (CT) in the at-risk population are categorized by LI-RADS as indeterminate, further delaying the establishment of diagnosis.

There are currently studies pioneering the application of artificial intelligence (AI) in the field of medical imaging. An interdisciplinary research team of clinicians, radiologists and statistical scientists, based on the clinical and radiological database of over 4,000 liver images, have developed an AI algorithm to accurately diagnose liver cancer on CT. Based on retrospective data, an interim analysis found the AI algorithm able to achieve a diagnostic accuracy of >97% and a negative predictive value of >99%.

If the prototype AI algorithm proves to have a better one-off diagnostic performance when compared to LI-RADS, it can facilitate the earlier diagnosis of HCC, allowing earlier definitive treatment and improving cancer survival.

Study Type

Interventional

Enrollment (Actual)

300

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Hong Kong
      • Hong Kong, Hong Kong
        • Department of Medicine, The University of Hong Kong, Queen Mary Hospital
      • Hong Kong, Hong Kong
        • Department of Medicine and Department of Surgery, The University of Hong Kong, Queen Mary Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • 1. Age >=18 years.
  • 2. Defined as the at-risk population requiring regular liver ultrasonography surveillance.

These include:

  1. Cirrhotic patients of any disease etiology,

  2. Chronic hepatitis B patients of age ≥40 years for men, age ≥50 years for women or with a family history of HCC.

    • 3. At least one new-onset focal liver nodule detected on liver ultrasonography.

Exclusion Criteria:

  • 1. Liver nodules of <1 cm. Currently such nodules are not reported using LI-RADS criteria but are recommended for a repeat scan in 3-6 months. In patients with multiple liver nodules, the largest nodule will be assessed.
  • 2. Patients with contraindications for contrast CT imaging, including a history of contrast anaphylaxis and impaired renal function (glomerular filtration rate <30 ml/min).
  • 3. Patients with prior transarterial chemoembolization or other interventional procedures with intrahepatic injection of lipiodol. Lipiodol is extremely hyperdense on computed tomography and will preclude objective interpretation. Such patients were also excluded in the development of our prototype AI algorithm.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: Prototype AI algorithm
In-house prototype deep learning artificial intelligence algorithm
Diagnostic test: Prototype artificial intelligence algorithm
Developed by the University of Hong Kong
Placebo Comparator: LI_RADS interpretation
LI-RADS criteria will be assessed independently by two specified abdominal radiologists with at least 10 years of experience in cross-sectional abdominal imaging
Diagnostic test: LI-RADS
The Liver Imaging Reporting and Data System (LIRADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic accuracy for HCC
Time Frame: 12 months
Number of participants diagnosed with HCC using a composite clinical reference standard. A lesion will be considered positive for HCC based on histology (biopsy, surgical resection or explant) or achieving LR-5 criteria in subsequent imaging. A lesion will be considered negative for HCC if it demonstrated stability at imaging for at least 12 months, unequivocal spontaneous reduction, or disappearance in the absence of tumor treatment.
12 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Other diagnostic performance parameters for HCC
Time Frame: 12 months
Number of participants diagnosed with HCC using a composite clinical reference standard. A lesion will be considered positive for HCC based on histology (biopsy, surgical resection or explant) or achieving LR-5 criteria in subsequent imaging. A lesion will be considered negative for HCC if it demonstrated stability at imaging for at least 12 months, unequivocal spontaneous reduction, or disappearance in the absence of tumor treatment.
12 months
Interpretation time
Time Frame: 12 months
Mean time for AI interpretation for recruited participants
12 months
Occurrence of technical failures
Time Frame: 12 months
Number of technical failures overall
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

The University of Hong Kong

Collaborators

Education University of Hong Kong

Investigators

  • Principal Investigator: Wai-Kay Seto, MD, The University of Hong Kong

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

November 1, 2023

Primary Completion (Actual)

March 31, 2026

Study Completion (Actual)

March 31, 2026

Study Registration Dates

First Submitted

October 1, 2024

First Submitted That Met QC Criteria

October 1, 2024

First Posted (Actual)

October 3, 2024

Study Record Updates

Last Update Posted (Actual)

May 15, 2026

Last Update Submitted That Met QC Criteria

May 14, 2026

Last Verified

October 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • HMRF HCC AI

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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