Artificial Intelligence-Assisted Imaging for Diagnosis and Management of Focal Liver Lesions
NCT07926672, Not applicable, Recruiting, Sun Yat-sen University
Trial 10
As registered on ClinicalTrials.gov, in the graph's capture of the registry.
| Field | Value | Source |
|---|---|---|
| Title | Artificial Intelligence-Assisted Imaging for Diagnosis and Management of Focal Liver Lesions | ClinicalTrials.gov |
| NCT number | NCT07926672 | ClinicalTrials.gov |
| Status | Recruiting | ClinicalTrials.gov |
| Phases | Not applicable | ClinicalTrials.gov |
| Enrollment | 10,000 participants, estimated | ClinicalTrials.gov |
| Lead sponsor | Sun Yat-sen University | ClinicalTrials.gov |
| Conditions | Focal Liver Lesions | ClinicalTrials.gov |
| Interventions | Artificial Intelligence-Assisted Imaging Assessment, Conventional Physician Imaging Assessment | ClinicalTrials.gov |
| First posted | 2026-11-09 | ClinicalTrials.gov |
| Last updated | 2026-11-09 | ClinicalTrials.gov |
Registered outcomes 6
Each outcome the trial registered, primary first, in ClinicalTrials.gov's words.
| List | Outcome | Time frame | Description | Source |
|---|---|---|---|---|
| Primary | Overall Diagnostic Accuracy of AI-Assisted Versus Conventional Physician Imaging Assessment | At the index imaging assessment, with reference diagnosis established within 6 months after imaging. | ClinicalTrials.gov | |
| Secondary | Percentage of Participants With Correct Detection of Focal Liver Lesions | At the index computed tomography assessment, with the reference diagnosis established within 6 months after imaging. | 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. | ClinicalTrials.gov |
| Secondary | Percentage of Participants With Correct Benign Versus Malignant Classification of Focal Liver Lesions | At the index computed tomography (CT) imaging assessment, with the reference diagnosis established within 6 months after imaging. | 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. | ClinicalTrials.gov |
| Secondary | Percentage of Participants With Correct Classification of Benign Liver Lesion Subtypes | At the index computed tomography imaging assessment, with the reference diagnosis established within 6 months after imaging. | 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. | ClinicalTrials.gov |
| Secondary | Harrell's Concordance Index for Artificial Intelligence-Based Overall Survival Prediction in Patients With Hepatocellular Carcinoma | From initiation of the index interventional treatment until death from any cause or last known follow-up, assessed up to 36 months. | 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. | ClinicalTrials.gov |
| Secondary | Harrell's Concordance Index for Artificial Intelligence-Based Progression-Free Survival Prediction in Patients With Hepatocellular Carcinoma | 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. | 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. | ClinicalTrials.gov |
Outcomes 0
Each outcome the trial posted results for, in ClinicalTrials.gov's words.
| Type | Outcome | Time frame | Population | Units | Measured as | Source |
|---|
Results by group 0
Each measurement the trial posted, group by group, in the words and numbers it posted.
| Outcome | Group | Category | Value | Spread | Units | Source |
|---|
Participant flow 0
How many started, completed or left each group, period by period.
| Period | Milestone | Group | Participants | Units counted | Source |
|---|
Adverse events 0
Each adverse event the trial posted, group by group: the participants affected out of those at risk.
| Type | Event | Organ system | Group | Affected | At risk | Events | Source |
|---|
Adverse event totals 0
The totals the trial posted for each group.
| Total | Group | Affected | At risk | Source |
|---|
Papers 0
The papers the trial lists on ClinicalTrials.gov.
| PMID | Listed as | Source |
|---|
Grants 0
The grants NIH RePORTER links to the trial, newest funded first: NIH's, and some of AHRQ's, CDC's, FDA's and VA's. Each shows its title, principal investigators, organization and funder in its newest year.
| Project | Title | Principal investigators | Organization | Funder | Fiscal years | Source |
|---|
Trial comparisons 0
Comparisons of the trial's two groups, calculated by ConductScience from the counts it posted.
| Comparison | Source |
|---|
Tests 0
The ConductCognition tests this trial's registered outcomes name, as ScienceGraph matched them in trial-tests-2026-11-12-0.2.1.
| Test | Named as | Source |
|---|
Source 5
| Field | Value | Source |
|---|---|---|
| Release | trials-2026-11-12-0.4.0 | ClinicalTrials.gov |
| Release manifest SHA-256 | 0f4a8d8c48aee2a7573ae0fa94ecbeab70c1adbe449bbc95c424bc7d3e97b9d4 | ClinicalTrials.gov |
| Registry capture | 2026-11-12 | ClinicalTrials.gov |
| Results copy, AACT | 2026-03-23 | ClinicalTrials.gov |
| NIH clinical studies file | 2026-11-13 | ClinicalTrials.gov |
Release trials-2026-11-12-0.4.0
ClinicalTrials.gov's posted results, through CTTI's AACT: ClinicalTrials.gov data via Aggregate Analysis of ClinicalTrials.gov (AACT), CTTI. CTTI makes its materials freely available and requests acknowledgement. https://aact.ctti-clinicaltrials.org/documentation/299 ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
ClinicalTrials.gov: none stated ClinicalTrials.gov's data carry an international copyright outside the United States: https://clinicaltrials.gov/about-site/terms-conditions.
NIH RePORTER's clinical studies and project files: Public domain (NIH RePORTER)