An interventional study of AI-human collaboration for CE-CTs diagnosis in Hepatocellular Carcinoma (HCC), Intrahepatic Cholangiocarcinoma (Icc) and Hepatic Metastasis, sponsored by Shengjing Hospital. Completed at 1 site in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-11-18.
Sponsored by Shengjing Hospital · Not applicable, Interventional, and Diagnostic
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.
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.
3,648 studies on the registry are indexed under Carcinoma, Hepatocellular; 1,061 are open to participants now.
This study's enrollment of 10,333 is above the median of 54 across 2,618 interventional studies indexed under Carcinoma, Hepatocellular.
Browse Carcinoma, Hepatocellular studies →Shengjing Hospital is the lead sponsor of 127 studies on the registry; 59 are open to participants now.
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In the prospective analysis phase, patients undergo routine Multiphasic Contrast-Enhanced Computed Tomography (CE-CT) imaging. The scans are evaluated through two parallel pathways: standard radiologist interpretation (without AI input) and independent AI analysis. When diagnostic discrepancies occur, a senior radiologist or multidisciplinary expert panel reviews the case and provides the definitive diagnosis.
Diagnostic Test: AI-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.
Diagnostic accuracy of the AI System for malignancy diagnosis
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.
Time frame: Up to 90 days
Secondary diagnostic performance
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.
Time frame: Up to 90 days
Lesion screening performance
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).
Time frame: Up to 90 days
Detection discordance
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.
Time frame: Up to 90 days
Amended radiological report
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.
Time frame: Up to 90 days
Plan to share: Yes — We plan to share IPD related to abdominal dynamic-contrast enhanced CT scans and clinical outcomes for hepatic tumor diagnosis.
Supporting information: Study protocol
Ran from Sep 2025 to primary completion in Oct 2025, 1 month.
Of 360 non-industry interventional studies with no phase under Carcinoma, Hepatocellular, half reached primary completion within 48 months of starting (the median), counting those still running; a quarter within 24 and three quarters within 76. This study's 1 month is below that.
Counting every sponsor, half of 384 studies reached it within 47 months; a quarter within 23 and three quarters within 75.
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This study is completed, as verified in Nov 2025. You cannot join it, but the record below documents what was studied.
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