An interventional study of Prototype artificial intelligence algorithm and LI-RADS in Hepatocellular Carcinoma, sponsored by The University of Hong Kong. Completed at 2 sites in Hong Kong. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-05-15.
Sponsored by The University of Hong Kong · Not applicable, Interventional, and Diagnostic
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.
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.
3,182 studies on the registry are indexed under Carcinoma, Hepatocellular; 954 are open to participants now.
This study's enrollment of 300 is above the median of 55 across 2,298 interventional studies indexed under Carcinoma, Hepatocellular.
Browse Carcinoma, Hepatocellular studies →The University of Hong Kong is the lead sponsor of 1,262 studies on the registry; 340 are open to participants now.
Of its 8 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
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These include:
Chronic hepatitis B patients of age ≥40 years for men, age ≥50 years for women or with a family history of HCC.
Exclusion Criteria:
In-house prototype deep learning artificial intelligence algorithm
Diagnostic Test: Prototype artificial intelligence algorithm
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
Developed by the University of Hong Kong
The Liver Imaging Reporting and Data System (LIRADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC
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.
Time frame: 12 months
Other diagnostic performance parameters 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.
Time frame: 12 months
Interpretation time
Mean time for AI interpretation for recruited participants
Time frame: 12 months
Occurrence of technical failures
Number of technical failures overall
Time frame: 12 months
This study is completed, as verified in Oct 2023. You cannot join it, but the record below documents what was studied.
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The University of Hong Kong