Comparing an AI algorithm to standard imaging for liver cancer diagnosis

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 is testing a new AI tool to see if it can diagnose liver cancer better than the standard imaging methods used for people who need regular checks for liver issues.

Quick facts

• Phase: NA
• Study type: Interventional
• Enrollment: 250 (estimated)
• Ages: 18 Years and up
• Sex: All
• Sponsor: The University of Hong Kong (other)
• Locations: 2 sites (Hong Kong and 1 other locations)
• Trial ID: NCT06626087 on ClinicalTrials.gov

What this trial studies

This study aims to validate a prototype artificial intelligence algorithm against the established Liver Imaging Reporting and Data System (LI-RADS) criteria for diagnosing hepatocellular carcinoma (HCC) using triphasic contrast CT scans. The research will involve a randomized approach in an at-risk population, specifically those requiring regular liver surveillance. The primary outcome is to assess the diagnostic performance of the AI algorithm compared to LI-RADS in identifying HCC. The study is independent of the clinical reporting done by radiologists, ensuring unbiased results.

Who should consider this trial

Good fit: Ideal candidates include adults aged 18 and older who are part of the at-risk population for liver cancer, such as cirrhotic patients or those with chronic hepatitis B.

Not a fit: Patients with liver nodules smaller than 1 cm or those with contraindications for contrast CT imaging may not benefit from this study.

Why it matters

Potential benefit: If successful, this study could lead to earlier and more accurate diagnoses of liver cancer, improving patient outcomes.

How similar studies have performed: There have been pioneering studies applying AI in medical imaging, suggesting potential for success, although this specific approach is relatively novel.

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.

Where this trial is running

Hong Kong and 1 other locations

• Department of Medicine and Department of Surgery, The University of Hong Kong, Queen Mary Hospital — Hong Kong, Hong Kong (RECRUITING)
• Department of Medicine, The University of Hong Kong, Queen Mary Hospital — Hong Kong, Hong Kong (NOT_YET_RECRUITING)

Study contacts

• Principal investigator: Wai-Kay Seto, MD — The University of Hong Kong
• Study coordinator: Wai-Kay Seto, MD

Conditions: Hepatocellular Carcinoma, HCC, Liver cancer, Artificial Intelligence Algorithm, Medical imaging