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

NCT06626087 | Recruiting

• 1 other identifier: HMRF HCC AI
• Study Type: interventional
• Target: 250
• Locations: 1 country
• Sites: 2

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.

Conditions

Hepatocellular Carcinoma

Keywords

HCC; Liver cancer; Artificial Intelligence Algorithm; Medical imaging

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy for HCC

  Number of participants diagnosed with HCC using a composite clinical reference standard. A lesion will be considered positive for HCC based on histology (biopsy, surgical resection or explant) or achieving LR-5 criteria in subsequent imaging. A lesion will be considered negative for HCC if it demonstrated stability at imaging for at least 12 months, unequivocal spontaneous reduction, or disappearance in the absence of tumor treatment.

  12 months

Secondary Outcomes (3)

• Other diagnostic performance parameters for HCC

  12 months

• Interpretation time

  12 months

• Occurrence of technical failures

  12 months

Study Arms (2)

Prototype AI algorithm

ACTIVE COMPARATOR

In-house prototype deep learning artificial intelligence algorithm

Diagnostic Test: Prototype artificial intelligence algorithm

LI_RADS interpretation

PLACEBO COMPARATOR

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

Diagnostic Test: LI-RADS

Interventions

Prototype artificial intelligence algorithm DIAGNOSTIC_TEST

Developed by the University of Hong Kong

Prototype AI algorithm

LI-RADS 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

LI_RADS interpretation

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• \. Age \>=18 years.
• \. Defined as the at-risk population requiring regular liver ultrasonography surveillance.
• These include:
• Cirrhotic patients of any disease etiology,
• Chronic hepatitis B patients of age ≥40 years for men, age ≥50 years for women or with a family history of HCC.
• \. At least one new-onset focal liver nodule detected on liver ultrasonography.

You may not qualify if:

• \. 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.
• \. Patients with contraindications for contrast CT imaging, including a history of contrast anaphylaxis and impaired renal function (glomerular filtration rate \<30 ml/min).
• \. 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.

Sponsors & Collaborators

• The University of Hong Kong: lead
• Education University of Hong Kong: collaborator

Study Sites (2)

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

MeSH Terms

Conditions

Carcinoma, Hepatocellular; Liver Neoplasms

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases

Study Officials

• Wai-Kay Seto, MD

  The University of Hong Kong

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Wai-Kay Seto, MD

CONTACT

85222553579; wkseto@hku.hk

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: INVESTIGATOR
• Masking Details: Both radiologists will be blinded to the clinical characteristics and subsequent management of participants, with any discordance in assessment resolved by consensus before reaching a final decision.
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Scanned images are randomized individually 1:1 to either the prototype AI algorithm or LI-RADS criteria interpretation by two specialist gastrointestinal radiologists

Study Record Dates

• First Submitted: October 1, 2024
• First Posted: October 3, 2024
• Study Start: November 1, 2023
• Primary Completion (Estimated): October 31, 2026
• Study Completion (Estimated): October 31, 2026
• Last Updated: October 29, 2024

Record last verified: 2023-10

Locations

HK (2)