Artificial Intelligence vs. LIRADS in Diagnosing HCC on CT

NCT04843176 | Recruiting

• 1 other identifier: UW 20-445
• Study Type: interventional
• Target: 250
• Locations: 1 country
• Sites: 1

Brief Summary

Liver cancer is the sixth most commonly diagnosed cancer and the fourth leading cause of cancer death worldwide. It is the 3rd most common cause of cancer death in Hong Kong. The five-year survival rates of liver cancer differ greatly with disease staging, ranging from 91.5% in early-stage to 11% in late-stage. The early and accurate diagnosis of liver cancer is paramount in improving cancer survival. Liver cancer is diagnosed radiologically via cross sectional imaging, e.g. computed tomography (CT), without the routine use of liver biopsy. However, with current internationally-recommended radiological reporting methods, up to 49% of liver lesions may be inconclusive, resulting in repeated scans and a delay in diagnosis and treatment. An artificial intelligence (AI) algorithm that that can accurately diagnosed liver cancer has been developed. Based on an interim analysis, the algorithm achieved a high diagnostic accuracy. The AI algorithm is now ready for implementation. 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 the diagnostic accuracy of liver cancer, which will be unbiasedly based on a composite clinical reference standard.

Conditions

HCC; Liver Cancer

Keywords

HCC; liver cancer; AI; deep learning; CT; 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 (LI-RADS) 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. 2. 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 (1)

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

Hong Kong, Hong Kong

RECRUITING

MeSH Terms

Conditions

Liver Neoplasms

Condition Hierarchy (Ancestors)

Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Liver Diseases

Central Study Contacts

Wai-Kay Seto, MD

CONTACT

85222553579; wkseto@hku.hk

Keith Chiu, FRCR

CONTACT

85222553111; kwhchiu@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: April 6, 2021
• First Posted: April 13, 2021
• Study Start: March 19, 2021
• Primary Completion: December 31, 2025
• Study Completion (Estimated): June 30, 2026
• Last Updated: May 18, 2022

Record last verified: 2022-05

Data Sharing

• IPD Sharing: Will not share

Locations

HK (1)