Cyst, Focal Nodular Hyperplasia, Hepatic Hemangioma, Hepatic Metastasis, Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (Icc)
Conditions
Keywords
Dynamic Contrast-Enhanced CT, Focal Liver Lesions
Brief summary
Recent advances in artificial intelligence (AI), particularly deep learning technology, have transformed medical imaging analysis. AI systems have demonstrated diagnostic performance comparable to or exceeding that of expert radiologists in specific tasks. Liver-focused AI diagnostic systems have achieved promising results in multi-center validations; however, these retrospective studies have not yet addressed two critical gaps. First, large-scale prospective trials are required to establish real-world clinical effectiveness. Second, it remains unclear whether AI can be organically embedded into clinical diagnostic workflows to reshape diagnostic and therapeutic pathways, particularly by enhancing the detection and follow-up of hepatic malignancies and ultimately improving patient outcomes.
Detailed description
This study aims to evaluate the effectiveness of AI-human collaboration in liver tumor diagnosis by embedding real-time AI analysis into conventional multiphasic contrast-enhanced CT (CE-CT) workflows. Specifically, this prospective validation trial will assess diagnostic performance in detecting and characterizing hepatic lesions, particularly malignancies, evaluate the feasibility and efficiency of workflow integration, and determine the potential clinical impact on treatment decision-making and patient management.
Interventions
DIAGNOSTIC_TESTAI-human collaboration for CE-CTs diagnosis
The system automatically processes all eligible same-day scans and generates results for review the following day. To maintain efficient AI-human collaboration while preserving the standard clinical workflow, the conventional radiological interpretation process remains unchanged (first-line radiologists provide initial reports followed by senior radiologists' review). A dedicated senior radiologist then evaluates any discordances between AI findings and primary radiological report. For complex cases, the review process escalates to a consensus review panel (i.e., pre-designated senior radiologists, Multidisciplinary Team (MDT)). The MDT can recommend clinical interventions including follow-up (e.g., additional imaging examinations, active surveillance), surgical procedures, or adjustments to adjuvant therapy (initiation or modification of treatment regimens). All discordant cases and their outcomes are systematically documented for longitudinal tracking and follow-up analysis.
Sponsors
Shengjing Hospital
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. Age range 18 years and above 2. Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage 3. Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional 4. Complete imaging data that meet AI system analysis requirements.
Exclusion criteria
1. History of recent upper-abdominal surgery (within 30 days) or major hepatobiliary-pancreatic surgery affecting liver evaluation (e.g., liver transplantation or Whipple procedure); patients with prior simple cholecystectomy or single-lesion interventional procedures are not excluded 2. History of recent hepatic trauma (within 30 days) 3. Poor image quality or severe noise artifacts (e.g., metal or motion artifacts) 4. Missing required imaging phases (required at least non-contrast, arterial, and venous phases) or inadequate scan range (e.g., lower-abdomen CT such as pelvic or rectal scans not covering the liver)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic accuracy of the AI System for malignancy diagnosis | Up to 90 days | Measures the patient-level diagnostic accuracy of the AI system for differentiating malignant vs. non-malignant lesions. The primary metric is the Area under the Receiver Operating Characteristic Curve (AUC). The primary analysis will test the one-sided superiority hypothesis H1: AUC \> 0.90 against H0: AUC \<= 0.90. The trial will be considered successful if the lower bound of the 95% Confidence Interval (CI) for the AUC is greater than 0.90. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Secondary diagnostic performance | Up to 90 days | Measures the patient-level diagnostic performance of the AI system for malignant versus non-malignant classification. Metrics include sensitivity, specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV). These will be calculated from the continuous probability score using a fixed operating point prior to prospective analysis. |
| Lesion screening performance | Up to 90 days | Measures the patient-level screening ability of the AI system to distinguish patients with any lesion from those with no lesions. This is a binary classification task (AUC) comparing lesion patient (malignant or benign) versus no lesion (normal liver or diffuse disease only). |
| Detection discordance | Up to 90 days | Measures the number of FLLs identified by the AI-human collaborative workflow that were overlooked by the initial radiologist report. An overlooked lesion is defined as an event meeting all three criteria: (1) detected by the AI system; (2) not described in the initial radiological report; (3) confirmed as a true lesion by senior radiologist/MDT re-review. |
| Amended radiological report | Up to 90 days | Measures the number of formal addenda issued to finalized radiology reports. An amended report is defined as a formal addendum that explicitly corrects a diagnosis or adds a previously missed finding based on the AI-human collaborative review. |
Countries
China
Outcome results
None listed