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

Summary

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.

Eligibility

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.

Conditions4

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