Interventional AI-Human Collaboration for Liver Tumor Diagnosis
AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Yu Shi, MD PhD
- Phone Number: 18940259980
- Email: 18940259980@163.com
Study Locations
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Liaoning
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Shenyang, Liaoning, China, 110004
- Shengjing Hospital of China Medical University
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
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)
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Experimental: AI-human collaboration in CE-CT diagnosis for liver lesions
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.
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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.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Diagnostic accuracy of the AI System for malignancy diagnosis
Time Frame: Up to 90 days
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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.
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Up to 90 days
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Secondary diagnostic performance
Time Frame: Up to 90 days
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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.
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Up to 90 days
|
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Lesion screening performance
Time Frame: Up to 90 days
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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).
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Up to 90 days
|
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Detection discordance
Time Frame: Up to 90 days
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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.
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Up to 90 days
|
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Amended radiological report
Time Frame: Up to 90 days
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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.
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Up to 90 days
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Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Yu Shi, MD PhD, Shengjing Hospital
Publications and helpful links
General Publications
- Cao K, Xia Y, Yao J, Han X, Lambert L, Zhang T, Tang W, Jin G, Jiang H, Fang X, Nogues I, Li X, Guo W, Wang Y, Fang W, Qiu M, Hou Y, Kovarnik T, Vocka M, Lu Y, Chen Y, Chen X, Liu Z, Zhou J, Xie C, Zhang R, Lu H, Hager GD, Yuille AL, Lu L, Shao C, Shi Y, Zhang Q, Liang T, Zhang L, Lu J. Large-scale pancreatic cancer detection via non-contrast CT and deep learning. Nat Med. 2023 Dec;29(12):3033-3043. doi: 10.1038/s41591-023-02640-w. Epub 2023 Nov 20.
- Ding W, Meng Y, Ma J, Pang C, Wu J, Tian J, Yu J, Liang P, Wang K. Contrast-enhanced ultrasound-based AI model for multi-classification of focal liver lesions. J Hepatol. 2025 Aug;83(2):426-439. doi: 10.1016/j.jhep.2025.01.011. Epub 2025 Jan 21.
- Ying H, Liu X, Zhang M, Ren Y, Zhen S, Wang X, Liu B, Hu P, Duan L, Cai M, Jiang M, Cheng X, Gong X, Jiang H, Jiang J, Zheng J, Zhu K, Zhou W, Lu B, Zhou H, Shen Y, Du J, Ying M, Hong Q, Mo J, Li J, Ye G, Zhang S, Hu H, Sun J, Liu H, Li Y, Xu X, Bai H, Wang S, Cheng X, Xu X, Jiao L, Yu R, Lau WY, Yu Y, Cai X. A multicenter clinical AI system study for detection and diagnosis of focal liver lesions. Nat Commun. 2024 Feb 7;15(1):1131. doi: 10.1038/s41467-024-45325-9.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Estimated)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Neoplasms by Site
- Neoplasms
- Pathological Conditions, Anatomical
- Neoplasms by Histologic Type
- Digestive System Neoplasms
- Digestive System Diseases
- Liver Diseases
- Neoplasms, Glandular and Epithelial
- Adenocarcinoma
- Liver Neoplasms
- Carcinoma
- Pathological Conditions, Signs and Symptoms
- Carcinoma, Hepatocellular
- Cholangiocarcinoma
- Cysts
- Cirrhosis, Familial, with Pulmonary Hypertension
- Focal Nodular Hyperplasia
Other Study ID Numbers
Other Study ID Numbers
- SH-CMU-FLL-Intervention
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
IPD Sharing Supporting Information Type
- STUDY_PROTOCOL
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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