Clinical trial · Interventional
LEAF (Liver Tumor dEtection And classiFication AI)
Clinical Research on the Use of Non-contrast CT Combined With AI for Early Screening for Liver Malignancy
NCT06859840CI-TRIAL-00118427LEAFactive not recruitingN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 16, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260916-000001
Summary
Brief summary (as posted)
This study aims to assess the feasibility of leveraging non-contrast CT and artificial intelligence to detect liver cancer in consecutive real-world patients. To this end, we deploy LEAF in a prospective real-world clinical setting for real-time monitoring, with a particular focus on flagging cases with liver cancer that may be missed by routine clinical workflow.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Liver Malignancy | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| LEAF(Liver tumor dEtection And classiFication AI) | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- LEAF
- description
- Patients diagnosed with liver cirrosis or those with extrahepatic malignant tumors will be enrolled within three weeks. Non-contrast chest and abdominal CT scans will be simultaneously reviewed by radiologists in routine clinical workflow and processed by LEAF in real-time. Daily logs of LEAF-positive alerts will be maintained by the research team. A prespecified clinical action committee composed of hepatobiliary surgeons and abdominal radiologists will review the case to assess whether the AI finding warrants communication to the treating physician of these patients. For patients with suspected malignant liver tumors, the committee's consensus on the presence of suspicious lesions will be communicated to their attending physicians, who will then decide whether additional diagnostic assessment is indicated according to standard clinical practice.
- interventionNames
- Device: LEAF(Liver tumor dEtection And classiFication AI)
Primary outcomes (1)
- measure
- Detection accuracy in liver tumor assisted by LEAF (Liver tumor dEtection And classiFication AI)
- timeFrame
- Within 4 weeks after enrollment
- description
- Sensitivity, specificity of liver malignancy identification (defined as liver malignancy vs. liver benign tumor and non-tumor)
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 90 Years
Show eligibility criteria text
Inclusion Criteria: Age range 18 years and above; Underwent non-contrast chest or abdominal CT examination with liver coverage; Patients with an established diagnosis of cirrhosis; Patients with an established diagnosis of extrahepatic cancer. Exclusion criteria: Patients who have been diagnosed with malignant liver tumor; Patients who underwent liver transplantation; Low quality image, severe artifacts and noise.
References
Publications (0)
Data not yet available
No reference posted for this study.