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

The University of Hong Kong (HKU)

Status: Enrolling

Conditions: Hepatocellular Carcinoma

Treatments: Diagnostic Test: LI-RADS; Diagnostic Test: Prototype artificial intelligence algorithm

Study type: Interventional

Funder types: Other

Identifiers

NCT06626087

HMRF HCC AI

Details and patient eligibility

About

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.

Full 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.

Enrollment: 250 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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250 participants in 2 patient groups, including a placebo group

Prototype AI algorithm

Active Comparator group

Description:

In-house prototype deep learning artificial intelligence algorithm

Treatment:

Diagnostic Test: Prototype artificial intelligence algorithm

LI_RADS interpretation

Placebo Comparator group

Description:

LI-RADS criteria will be assessed independently by two specified abdominal radiologists with at least 10 years of experience in cross-sectional abdominal imaging

Treatment:

Diagnostic Test: LI-RADS