HCCNCT04843176
Artificial Intelligence vs. LIRADS in Diagnosing HCC on CT
A study testing Prototype artificial intelligence algorithm and LI-RADS in people with HCC and Liver Cancer, run by The University of Hong Kong at 1 site in Hong Kong.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- HCC, Liver Cancer
- What is being tested?
- Prototype artificial intelligence algorithm and LI-RADS
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 250 participants
- Where
- 1 siteHong Kong
- Study dates
- 19 Mar 2021to 30 Jun 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 1. 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.
- 3. At least one new-onset focal liver nodule detected on liver ultrasonography.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Hong Kong1 site, 1 recruiting
- Recruiting
Department of Medicine, The University of Hong Kong, Queen Mary Hospital
Hong Kong
Wai-Kay Seto, MD, +85222553579, wkseto@hku.hk
Keith Chiu, FRCR
About this study
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
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.
Read the 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.
- Conditions
- HCC, Liver Cancer
- Being tested
- Prototype artificial intelligence algorithm, LI-RADS
- Keywords
- HCC, liver cancer, AI, deep learning, CT, imaging
- Medical subject headings
- Liver Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Prototype artificial intelligence algorithmDiagnostic test
Developed by the University of Hong Kong
LI-RADSDiagnostic 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
| Study group | Role | What they receive |
|---|---|---|
| Prototype AI algorithm | Active comparator | In-house prototype deep learning artificial intelligence algorithm Receives: 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 Receives: Diagnostic Test: LI-RADS |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 12 months
Secondary outcomes (3)
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.
Measured: 12 months
Interpretation time
Mean time for AI interpretation for recruited participants
Measured: 12 months
Occurrence of technical failures
Number of technical failures overall
Measured: 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Apr 2021
- Study start19 Mar 2021
- Primary completion31 Dec 2025 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HCCStudies of the same condition, open and under way.
- More trials run by The University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- 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
- Masking
- Single blind
- Who is masked
- Investigator
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- The University of Hong Kong
- Sponsor type
- University, hospital or other
- Collaborators
- Education University of Hong Kong
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04843176
- Sponsor's protocol ID
- UW 20-445
- Status last verified
- May 2022
- First submitted
- 6 Apr 2021
- Last updated
- 18 May 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Available to bona fide researchers who approach to principal investigator
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04843176
- Acronym / sponsor ID
- UW 20-445
- Conditions
- HCC, Liver Cancer
- Keywords
- HCC, liver cancer, AI, deep learning, CT, imaging
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- The University of Hong KongUniversity, hospital or other
- Collaborators
- Education University of Hong Kong
- Study start
- 19 Mar 2021 (actual)
- Primary completion
- 31 Dec 2025 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 13 Apr 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (investigator)
- Number of arms
- 2
- 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
| Arm | Type | Receives |
|---|---|---|
| Prototype AI algorithm | Active comparator | Diagnostic Test: Prototype artificial intelligence algorithmIn-house prototype deep learning artificial intelligence algorithm |
| LI_RADS interpretation | Placebo comparator | Diagnostic Test: LI-RADSLI-RADS criteria will be assessed independently by two specified abdominal radiologists with at least 10 years of experience in cross-sectional abdominal imaging |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 1. Age >=18 years. 2. Defined as the at-risk population requiring regular liver ultrasonography surveillance. These include:
- 3. At least one new-onset focal liver nodule detected on liver ultrasonography.
Key exclusion criteria (3 listed)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Prototype artificial intelligence algorithmDeveloped by the University of Hong Kong | Diagnostic test | Prototype AI algorithm |
| LI-RADSThe Liver Imaging Reporting and Data System (LI-RADS) was established to standardize the lexicon, interpretation and communication of radiological findings related to HCC | Diagnostic test | LI_RADS interpretation |
Primary outcome
Diagnostic accuracy for HCC
Time frame: 12 months
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.
3 secondary outcomes
Other diagnostic performance parameters for HCC
Time frame: 12 months
Interpretation time
Time frame: 12 months
Occurrence of technical failures
Time frame: 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 May 2022.
- Individual participant dataSharing planned: No. Available to bona fide researchers who approach to principal investigator
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A Post Approval Study to Evaluate Safety and Effectiveness of Multicompartmental Dosimetry Planning.
Hepatocellular Carcinoma (HCC)
Active, not recruitingPhase 2
TheraSphere With Durvalumab and Tremelimumab for HCC
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A DNAJB1-PRKACA Fusion Kinase Peptide Vaccine Combined With Glutamine Antagonist DRP-104, Nivolumab, and Ipilimumab in Patients With Advanced Stage Fibrolamellar Carcinoma (FLC)
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Prototype artificial intelligence algorithm and LI-RADS in people with HCC and Liver Cancer, run by The University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 May 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. Some participants in this study may receive a placebo. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Wai-Kay Seto, MD (85222553579, wkseto@hku.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic accuracy for HCC (time frame: 12 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 May 2022.