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Completed

NCT Number: NCT07153783

Interventional AI-Human Collaboration for Liver Tumor Diagnosis

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.

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Key information

Conditions

Hepatocellular Carcinoma (HCC) Adenocarcinoma Carcinoma Carcinoma, Hepatocellular Cholangiocarcinoma Cirrhosis, Familial, with Pulmonary Hypertension Cyst Cysts Digestive System Diseases Digestive System Neoplasms Focal Nodular Hyperplasia Hepatic Hemangioma Hepatic Metastasis Intrahepatic Cholangiocarcinoma (Icc) Liver Diseases Liver Neoplasms Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Shengjing Hospital of China Medical University

Shenyang, Liaoning, 110004, China

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Age range 18 years and above
  • Underwent dynamic contrast-enhanced abdominal CT examination with liver coverage
  • Imaging must include at least three required phases: non-contrast, arterial phase, and venous phase; an delayed phase is optional
  • Complete imaging data that meet AI system analysis requirements.

Exclusion criteria

  • 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
  • History of recent hepatic trauma (within 30 days)
  • Poor image quality or severe noise artifacts (e.g., metal or motion artifacts)
  • 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)

Treatment and study plan

AI-human collaboration for CE-CTs diagnosis

Diagnostic Test

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.

Primary outcomes

  1. Diagnostic accuracy of the AI System for malignancy diagnosis

    Time frame: 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 outcomes

  1. Secondary diagnostic performance

    Time frame: 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.

  2. Lesion screening performance

    Time frame: 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).

  3. Detection discordance

    Time frame: 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.

  4. Amended radiological report

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Shengjing Hospital

Other

Registry information

Official study title

AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT

Important dates

Study start
2025
Primary completion
2025
Study completion
2025
First posted
Sep 4, 2025
Registry last updated
Nov 18, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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