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Trial Title:
A Prototype AI Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing HCC on CT
NCT ID:
NCT06626087
Condition:
Hepatocellular Carcinoma
Conditions: Official terms:
Carcinoma
Carcinoma, Hepatocellular
Conditions: Keywords:
HCC
Liver cancer
Artificial Intelligence Algorithm
Medical imaging
Study type:
Interventional
Study phase:
N/A
Overall status:
Recruiting
Study design:
Allocation:
Randomized
Intervention model:
Parallel Assignment
Intervention model description:
Scanned images are randomized individually 1:1 to either the prototype AI algorithm or
LI-RADS criteria interpretation by two specialist gastrointestinal radiologists
Primary purpose:
Diagnostic
Masking:
Single (Investigator)
Masking description:
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.
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Prototype artificial intelligence algorithm
Description:
Developed by the University of Hong Kong
Arm group label:
Prototype AI algorithm
Intervention type:
Diagnostic Test
Intervention name:
LI-RADS
Description:
The Liver Imaging Reporting and Data System (LIRADS) was established to standardize the
lexicon, interpretation and communication of radiological findings related to HCC
Arm group label:
LI_RADS interpretation
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.
Criteria for 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.
Gender:
All
Minimum age:
18 Years
Maximum age:
N/A
Healthy volunteers:
No
Locations:
Facility:
Name:
Department of Medicine and Department of Surgery, The University of Hong Kong, Queen Mary Hospital
Address:
City:
Hong Kong
Country:
Hong Kong
Status:
Recruiting
Contact:
Last name:
Wai-Kay Seto, MD
Phone:
+85222553994
Email:
wkseto@hku.hk
Investigator:
Last name:
Wai-Kay Seto, MD
Email:
Principal Investigator
Facility:
Name:
Department of Medicine, The University of Hong Kong, Queen Mary Hospital
Address:
City:
Hong Kong
Country:
Hong Kong
Status:
Not yet recruiting
Contact:
Last name:
Wai-Kay Seto, MD
Phone:
+85222553579
Start date:
November 1, 2023
Completion date:
October 31, 2026
Lead sponsor:
Agency:
The University of Hong Kong
Agency class:
Other
Collaborator:
Agency:
Education University of Hong Kong
Agency class:
Other
Source:
The University of Hong Kong
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06626087
