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

NCT ID: NCT06626087

Last Updated: 2024-10-29

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 250 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-11-01
• Study Completion Date: 2026-10-31

Brief 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.

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.

Conditions

Hepatocellular Carcinoma

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

Prototype AI algorithm

In-house prototype deep learning artificial intelligence algorithm

Group Type: ACTIVE_COMPARATOR

Prototype artificial intelligence algorithm

Intervention Type: DIAGNOSTIC_TEST

Developed by the University of Hong Kong

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

Group Type: PLACEBO_COMPARATOR

LI-RADS

Intervention Type: DIAGNOSTIC_TEST

The Liver Imaging Reporting and Data System (LIRADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC

Interventions

Prototype artificial intelligence algorithm

Developed by the University of Hong Kong

Intervention Type: 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

Intervention Type: DIAGNOSTIC_TEST

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Education University of Hong Kong

OTHER

Sponsor Role: collaborator

The University of Hong Kong

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Wai-Kay Seto, MD

Role: PRINCIPAL_INVESTIGATOR

The University of Hong Kong

Locations

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

Hong Kong, , Hong Kong

Site Status: RECRUITING

Department of Medicine, The University of Hong Kong, Queen Mary Hospital

Hong Kong, , Hong Kong

Site Status: NOT_YET_RECRUITING

Countries

Hong Kong

Central Contacts

Wai-Kay Seto, MD

Role: CONTACT

wkseto@hku.hk

85222553579

Facility Contacts

Wai-Kay Seto, MD

Role: primary

wkseto@hku.hk

+85222553994

Wai-Kay Seto, MD

Role: primary

+85222553579

Other Identifiers

HMRF HCC AI

Identifier Type: -

Identifier Source: org_study_id