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
Trial ID: NCT04843176
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2021-03-19
Completion date: 2026-06-30
Sponsor: The University of Hong Kong
Collaborators: Education University of Hong Kong
Conditions: HCC; Liver Cancer
Interventions: Prototype artificial intelligence algorithm; LI-RADS
Locations: Department of Medicine, The University of Hong Kong, Queen Mary Hospital
Countries: Hong Kong
Enrollment: 250
Age range: 18 Years - None
Sex: ALL

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.

Eligibility criteria:
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.

Outcomes:
Diagnostic accuracy for HCC