Clinical trial NCT04843176: Artificial Intelligence vs. LIRADS in Diagnosing HCC on CT
Official title: 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
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: HCC
Liver Cancer
Keywords: HCC
liver cancer
AI
deep learning
CT
imaging
Lead sponsor: The University of Hong Kong
Sponsor class: OTHER
Collaborators: name: Education University of Hong Kong
First posted: 2021-04-13
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
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

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.

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. A interdisciplinary research team of clinicians, radiologists and statistical scientists, based on the clinical and radiological database of over 4,000 liver images, and 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%.

Can this novel prototype AI algorithm achieve a better performance in diagnosing HCC in the at-risk population when compared to LI-RADS? This question is especially relevant when the key to improved survival is early diagnosis, of which AI can potentially improve. Currently, errors in radiologist reporting are estimated to be 3-5% on a day-to-basis, equating to 40 million errors per annum worldwide. This prototype algorithm can be a solution to reduce human misinterpretation of radiological findings.

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.

Primary outcomes:
measure: Diagnostic accuracy for HCC
timeFrame: 12 months
Secondary outcomes:
measure: Other diagnostic performance parameters for HCC
timeFrame: 12 months
measure: Interpretation time
timeFrame: 12 months
measure: Occurrence of technical failures
timeFrame: 12 months
Other outcomes:
Locations:
city: Hong Kong
country: Hong Kong
facility: Department of Medicine, The University of Hong Kong, Queen Mary Hospital
Study officials:
References: