Using AI to improve liver cancer diagnosis on CT scans
A Prototype Artificial Intelligence Algorithm Versus Liver Imaging Reporting and Data System (LI-RADS) Criteria in Diagnosing Hepatocellular Carcinoma on Computed Tomography: a Randomized Trial
This study is testing a new AI tool to see if it can help doctors diagnose liver cancer more accurately in patients at risk, like those with cirrhosis or chronic hepatitis B.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 250 (estimated) |
| Ages | 18 Years and up |
| Sex | All |
| Sponsor | The University of Hong Kong Academic / other |
| Locations | 1 site (Hong Kong) |
| Trial ID | NCT04843176 on ClinicalTrials.gov |
What this trial studies
This study evaluates the effectiveness of a newly developed artificial intelligence (AI) algorithm in diagnosing hepatocellular carcinoma (HCC) using computed tomography (CT) scans, compared to the standard Liver Imaging Reporting and Data System (LI-RADS). Given the high rate of inconclusive results with LI-RADS, the AI aims to enhance diagnostic accuracy and reduce delays in treatment. The study involves patients at risk for liver cancer, specifically those with cirrhosis or chronic hepatitis B, and focuses on new liver nodules detected through ultrasound. The research team comprises clinicians, radiologists, and statistical scientists who will analyze a database of over 4,000 liver images to validate the AI's performance.
Who should consider this trial
Good fit: Ideal candidates include adults aged 18 and older who are at risk for liver cancer and have new liver nodules detected on ultrasound.
Not a fit: Patients with liver nodules smaller than 1 cm or those with contraindications for contrast CT imaging may not benefit from this study.
Why it matters
Potential benefit: If successful, this AI algorithm could significantly improve early diagnosis and treatment outcomes for patients with liver cancer.
How similar studies have performed: Other studies have shown promising results in applying AI to medical imaging, suggesting potential success for this approach.
Eligibility criteria
Show full inclusion / exclusion 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.
Where this trial is running
Hong Kong
- Department of Medicine, The University of Hong Kong, Queen Mary Hospital — Hong Kong, Hong Kong (Recruiting)
Study contacts
- Study coordinator: Wai-Kay Seto, MD
- Email: wkseto@hku.hk
- Phone: 85222553579
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.