Clinical trial · Interventional
Interventional AI-Human Collaboration for Liver Tumor Diagnosis
AI-human Collaborative Diagnosis of Liver Tumors Using CE-CT
NCT07153783CI-TRIAL-00097394completedN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (6)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Cyst | — | UNRESOLVED | — |
| Focal Nodular Hyperplasia | — | UNRESOLVED | — |
| Hepatic Hemangioma | Liver Hemangioma | ALIAS | 0.90 |
| Hepatic Metastasis | — | UNRESOLVED | — |
| Hepatocellular Carcinoma (HCC) | Hepatocellular Carcinoma | ONTOLOGY_EXACT | 0.85 |
| Intrahepatic Cholangiocarcinoma (Icc) | Intrahepatic Cholangiocarcinoma | ONTOLOGY_EXACT | 0.85 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-human collaboration for CE-CTs diagnosis | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- AI-human collaboration in CE-CT diagnosis for liver lesions
- description
- 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.
- interventionNames
- Diagnostic Test: AI-human collaboration for CE-CTs diagnosis
Primary outcomes (1)
- measure
- Diagnostic accuracy of the AI System for malignancy diagnosis
- timeFrame
- Up to 90 days
- description
- 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.
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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)
References
Publications (4)
- BACKGROUNDDing 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. PMID 39848548
- BACKGROUNDYing 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. PMID 38326351
- BACKGROUNDCao 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. PMID 37985692
- DERIVEDZhang X, Li C, Han X, Yin X, Zhang C, Zhou J, Cao K, Liu Q, Hong Y, Gao Y, Cui B, Zhang Q, Qiao J, Sun M, Liu W, Gao Y, Zhong S, Bai R, Jiang T, Humbert O, Ourselin S, Zuluaga MA, Yan K, Zhang Q, Hou Y, Liang T, Zhang L, Shi Y. Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial. Nat Med. 2026 Aug 19. doi: 10.1038/s41591-026-04589-y. Online ahead of print. PMID 42618635