Focal Liver LesionsNCT07926672
Artificial Intelligence-Assisted Imaging for Diagnosis and Management of Focal Liver Lesions
A study testing Artificial Intelligence-Assisted Imaging Assessment and Conventional Physician Imaging Assessment in people with Focal Liver Lesions, run by Sun Yat-sen University at 2 sites in China.
Trial summary
This study aims to evaluate the clinical value of artificial intelligence (AI)-assisted medical imaging in the diagnosis and management of focal liver lesions. Accurate diagnosis of focal liver lesions is important for selecting appropriate treatment strategies. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Focal Liver Lesions
- What is being tested?
- Artificial Intelligence-Assisted Imaging Assessment and Conventional Physician Imaging Assessment
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10,000 participants
- Where
- 2 sitesChina
- Study dates
- 15 Dec 2026to 31 May 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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 to 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients aged 18-75 years.
- Patients undergoing clinically indicated liver computed tomography (CT) examinations.
Who cannot join
- -Liver CT examinations that do not meet the predefined technical requirements or are nondiagnostic because of severe imaging artifacts.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
China2 sites, 2 recruiting
- Recruiting
The First Affiliated Hospital, Sun Yat-sen University
Guangzhou, Guangdong, 510080
Zhenwei Peng, 020-87755766, pzhenw@mail.sysu.edu.cn
- Recruiting
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong
Chao An, 20-87343088, anchao-1983@163.com
About this study
Official title: A Prospective, Randomized Controlled Trial of Artificial Intelligence-Assisted Imaging for the Diagnosis and Clinical Management of Focal Liver Lesions
This study aims to evaluate the clinical value of artificial intelligence (AI)-assisted medical imaging in the diagnosis and management of focal liver lesions. Accurate diagnosis of focal liver lesions is important for selecting appropriate treatment strategies. However, distinguishing between different types of liver lesions can be challenging because of overlapping imaging features. AI-based imaging systems may help improve diagnostic accuracy and support clinical decision-making.
- Conditions
- Focal Liver Lesions
- Being tested
- Artificial Intelligence-Assisted Imaging Assessment, Conventional Physician Imaging Assessment
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence-Assisted Imaging AssessmentDiagnostic test
An artificial intelligence-based imaging system will be used to assist the diagnosis of focal liver lesions through hierarchical classification. The system will evaluate lesion presence, distinguish benign from malignant lesions, and classify specific malignant and benign lesion types. For patients with independently confirmed hepatocellular carcinoma who undergo interventional therapy, an artificial intelligence-based prognostic model will additionally be evaluated for predicting overall survival and progression-free survival.
Conventional Physician Imaging AssessmentDiagnostic test
Qualified physicians will independently interpret liver imaging examinations according to routine clinical practice without assistance from the study artificial intelligence system. The diagnostic assessment will include lesion detection, differentiation between benign and malignant lesions, and classification of lesion subtypes using established imaging criteria and clinical information.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence-Assisted Imaging Group | Experimental | Not described Receives: Diagnostic Test: Artificial Intelligence-Assisted Imaging Assessment |
| Conventional Physician Imaging Group | Active comparator | Not described Receives: Diagnostic Test: Conventional Physician Imaging Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Overall Diagnostic Accuracy of AI-Assisted Versus Conventional Physician Imaging Assessment
Measured: At the index imaging assessment, with reference diagnosis established within 6 months after imaging.
Secondary outcomes (5)
Percentage of Participants With Correct Detection of Focal Liver Lesions
Detection accuracy is defined as the percentage of participants correctly classified as having or not having focal liver lesions, based on the prespecified reference standard. Accuracy will be calculated as the number of correctly classified participants divided by the total number of participants with an evaluable reference diagnosis, multiplied by 100. Results will be reported separately for the artificial intelligence-assisted and standard diagnostic groups. Higher percentages indicate better diagnostic performance.
Measured: At the index computed tomography assessment, with the reference diagnosis established within 6 months after imaging.
Percentage of Participants With Correct Benign Versus Malignant Classification of Focal Liver Lesions
The outcome is the percentage of participants whose benign or malignant liver lesion classification matches the reference standard. Binary classification accuracy will be calculated as the number of correctly classified participants (true positives plus true negatives) divided by the total number of evaluable participants with a confirmed benign or malignant focal liver lesion, multiplied by 100. The seven-class diagnostic outputs will be grouped into benign and malignant categories according to a prespecified classification scheme. Results will be reported separately for the artificial intelligence (AI)-assisted and standard diagnostic groups. Higher percentages indicate better diagnostic accuracy.
Measured: At the index computed tomography (CT) imaging assessment, with the reference diagnosis established within 6 months after imaging.
Percentage of Participants With Correct Classification of Benign Liver Lesion Subtypes
The outcome is the percentage of participants with correctly classified benign liver lesion subtypes, based on the prespecified reference standard. The analysis will include participants with confirmed hepatic hemangioma or hepatic cyst. Subtype classification accuracy will be calculated as the number of participants whose predicted benign lesion subtype matches the reference diagnosis divided by the total number of evaluable participants with either of these two benign lesions, multiplied by 100. Predictions of an incorrect benign subtype, a malignant category, or no lesion will be counted as incorrect. Results will be reported separately for the artificial intelligence-assisted and standard diagnostic groups. Higher percentages indicate better diagnostic accuracy.
Measured: At the index computed tomography imaging assessment, with the reference diagnosis established within 6 months after imaging.
Harrell's Concordance Index for Artificial Intelligence-Based Overall Survival Prediction in Patients With Hepatocellular Carcinoma
The discriminative performance of the artificial intelligence (AI)-based model for predicting overall survival (OS) in patients with hepatocellular carcinoma (HCC) will be evaluated using Harrell's concordance index (C-index). Overall survival is defined as the time from initiation of the index interventional treatment to death from any cause. Patients who remain alive will be censored at their last known follow-up. The C-index measures the concordance between predicted survival risk and observed survival outcomes, accounting for censored observations. Values range from 0 to 1, with 0.5 indicating discrimination comparable to chance and 1 indicating perfect concordance. Higher values indicate better prognostic discrimination.
Measured: From initiation of the index interventional treatment until death from any cause or last known follow-up, assessed up to 36 months.
Harrell's Concordance Index for Artificial Intelligence-Based Progression-Free Survival Prediction in Patients With Hepatocellular Carcinoma
The discriminative performance of the artificial intelligence (AI)-based model for predicting progression-free survival (PFS) in patients with hepatocellular carcinoma (HCC) will be evaluated using Harrell's concordance index (C-index). Progression-free survival is defined as the time from initiation of the index interventional treatment to the first documented disease progression or death from any cause, whichever occurs first. Disease progression will be determined according to the prespecified assessment criteria. Patients without progression or death will be censored at their last adequate disease assessment. The C-index measures the concordance between predicted progression risk and observed PFS outcomes, accounting for censored observations. Values range from 0 to 1, with 0.5 indicating discrimination comparable to chance and 1 indicating perfect concordance. Higher values indicate better prognostic discrimination.
Measured: From initiation of the index interventional treatment until first documented disease progression or death from any cause, whichever occurs first, assessed up to 36 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Nov 2026
- Study start15 Dec 2026 (estimated)
- Primary completion15 Apr 2027 (estimated)
- Study completion31 May 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Focal Liver LesionsStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe 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
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 10,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Sun Yat-Sen University Cancer Center, First Affiliated Hospital, Sun Yat-Sen University, First Affiliated Hospital of Jinan University
- Responsible party
- Principal investigator, Zhen-Wei Peng, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07926672
- Sponsor's protocol ID
- B2025-001-X02
- Status last verified
- Nov 2026
- First submitted
- 28 Oct 2026
- Last updated
- 9 Nov 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 9 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective, Randomized Controlled Trial of Artificial Intelligence-Assisted Imaging for the Diagnosis and Clinical Management of Focal Liver Lesions
- ClinicalTrials.gov ID
- NCT07926672
- Acronym / sponsor ID
- B2025-001-X02
- Conditions
- Focal Liver Lesions
- Study type
- Interventional
- Recruitment status
- Recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Collaborators
- Sun Yat-Sen University Cancer Center, First Affiliated Hospital, Sun Yat-Sen University, First Affiliated Hospital of Jinan University
- Study start
- 15 Dec 2026 (estimated)
- Primary completion
- 15 Apr 2027 (estimated)
- Study completion
- 31 May 2027 (estimated)
- First posted
- 9 Nov 2026 (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.
This study aims to evaluate the clinical value of artificial intelligence (AI)-assisted medical imaging in the diagnosis and management of focal liver lesions. Accurate diagnosis of focal liver lesions is important for selecting appropriate treatment strategies. However, distinguishing between different types of liver lesions can be challenging because of overlapping imaging features. AI-based imaging systems may help improve diagnostic accuracy and support clinical decision-making.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence-Assisted Imaging Group | Experimental | Diagnostic Test: Artificial Intelligence-Assisted Imaging Assessment |
| Conventional Physician Imaging Group | Active comparator | Diagnostic Test: Conventional Physician Imaging Assessment |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (2 listed)
- Patients aged 18-75 years.
- Patients undergoing clinically indicated liver computed tomography (CT) examinations.
Key exclusion criteria (1 listed)
- -Liver CT examinations that do not meet the predefined technical requirements or are nondiagnostic because of severe imaging artifacts.
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 |
|---|---|---|
| Artificial Intelligence-Assisted Imaging AssessmentAn artificial intelligence-based imaging system will be used to assist the diagnosis of focal liver lesions through hierarchical classification. The system will evaluate lesion presence, distinguish benign from malignant lesions, and classify specific malignant and benign lesion types. For patients with independently confirmed hepatocellular carcinoma who undergo interventional therapy, an artificial intelligence-based prognostic model will additionally be evaluated for predicting overall survival and progression-free survival. | Diagnostic test | Artificial Intelligence-Assisted Imaging Group |
| Conventional Physician Imaging AssessmentQualified physicians will independently interpret liver imaging examinations according to routine clinical practice without assistance from the study artificial intelligence system. The diagnostic assessment will include lesion detection, differentiation between benign and malignant lesions, and classification of lesion subtypes using established imaging criteria and clinical information. | Diagnostic test | Conventional Physician Imaging Group |
Primary outcome
Overall Diagnostic Accuracy of AI-Assisted Versus Conventional Physician Imaging Assessment
Time frame: At the index imaging assessment, with reference diagnosis established within 6 months after imaging.
5 secondary outcomes
Percentage of Participants With Correct Detection of Focal Liver Lesions
Time frame: At the index computed tomography assessment, with the reference diagnosis established within 6 months after imaging.
Percentage of Participants With Correct Benign Versus Malignant Classification of Focal Liver Lesions
Time frame: At the index computed tomography (CT) imaging assessment, with the reference diagnosis established within 6 months after imaging.
Percentage of Participants With Correct Classification of Benign Liver Lesion Subtypes
Time frame: At the index computed tomography imaging assessment, with the reference diagnosis established within 6 months after imaging.
Harrell's Concordance Index for Artificial Intelligence-Based Overall Survival Prediction in Patients With Hepatocellular Carcinoma
Time frame: From initiation of the index interventional treatment until death from any cause or last known follow-up, assessed up to 36 months.
Harrell's Concordance Index for Artificial Intelligence-Based Progression-Free Survival Prediction in Patients With Hepatocellular Carcinoma
Time frame: From initiation of the index interventional treatment until first documented disease progression or death from any cause, whichever occurs first, assessed up to 36 months.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Nov 2026.
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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.
The First Affiliated Hospital, Sun Yat-sen University
Guangzhou, China
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence-Assisted Imaging Assessment and Conventional Physician Imaging Assessment in people with Focal Liver Lesions, run by Sun Yat-sen University at 2 sites in China.
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 2 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 9 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in China. 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. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Overall Diagnostic Accuracy of AI-Assisted Versus Conventional Physician Imaging Assessment (time frame: At the index imaging assessment, with reference diagnosis established within 6 months after imaging.). 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 9 Nov 2026.