Clinical trial · Interventional
Pragmatic RCT of AI-Assisted Reading for Malignant Hepatic Lesions on CE-CT
A Multicenter Randomized Controlled Trial of AI-Assisted Reading Compared With Standard Radiology Interpretation for Malignant Hepatic Lesions on Contrast-Enhanced CT: Diagnostic, Clinical and Workflow Outcomes
- 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)
The purpose of this multicenter pragmatic randomized controlled trial is to determine whether AI-assisted interpretation of multiphasic liver contrast-enhanced computed tomography (CE-CT) is non-inferior to standard radiology reporting with respect to missed clinically significant malignant focal liver lesions, \*\*with the non-inferiority margin prespecified in the statistical analysis plan before enrollment\*\*, and whether it improves lesion detection, diagnostic characterization, downstream clinical management, and reporting efficiency. On AI-assisted center-days, AI results will be revealed only after the first-line radiologist has saved an unaided initial assessment and may be used to revise the final report. On control center-days, examinations will be interpreted using the standard radiology workflow without access to AI tools.
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 (2)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI-assisted abdominal CE-CT reporting | Other | — | UNRESOLVED |
| Standard abdominal CE-CT reporting without AI assistance | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- AI-on: AI-assisted abdominal CE-CT reporting
- description
- Intervention: AI-assisted interpretation of abdominal CE-CT
- interventionNames
- Other: AI-assisted abdominal CE-CT reporting
- type
- ACTIVE_COMPARATOR
- label
- AI-off: Standard abdominal CE-CT reporting without AI assistance
- description
- Routine interpretation of abdominal CE-CT without access to AI system (standard of care)
- interventionNames
- Other: Standard abdominal CE-CT reporting without AI assistance
Primary outcomes (1)
- measure
- Per-participant rate of missed clinically significant liver malignancy
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; a delayed phase is optional 4. Complete imaging data that meet AI system and radiologist interpretation 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)
- BACKGROUNDElias-Cabot E, Romero-Martin S, Raya-Povedano JL, Rodriguez-Ruiz A, Alvarez-Benito M. AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial. Nat Med. 2026 Apr;32(4):1296-1305. doi: 10.1038/s41591-026-04277-x. Epub 2026 Mar 19. PMID 41857202
- BACKGROUNDWoznitza N, Smith L, Rawlinson J, Au-Yong I, George B, Djearaman MG, Nair A, Lee RW, Navani N, Ndwandwe S, Clarke CS, Creeden A, Newsome J, Das I, Abaokporo S, Tucker R, Hathorn J, Baldwin DR. AI-based chest X-ray prioritization in the lung cancer diagnostic pathway: the LungIMPACT randomized controlled trial. Nat Med. 2026 May;32(5):1737-1744. doi: 10.1038/s41591-026-04253-5. Epub 2026 Mar 24. PMID 41876649
- 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