Clinical trial · Observational
Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions
Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions
NCT05476978CI-TRIAL-00075696completedClinicalTrials.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)
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Autoimmune Pancreatitis | — | UNRESOLVED | — |
| Pancreatic Ductal Adenocarcinoma | Pancreatic Ductal Adenocarcinoma | ONTOLOGY_EXACT | 0.98 |
| Pancreatic Neuroendocrine Tumor | Pancreatic Neuroendocrine Tumor | ONTOLOGY_EXACT | 0.98 |
| Pancreatitis, Chronic | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| EUS-AI model | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Pancreas-EUS
- description
- Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort.
- interventionNames
- Diagnostic Test: EUS-AI model
Primary outcomes (1)
- measure
- The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
- timeFrame
- After the training process of the EUS-AI model is completed
- description
- Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model.
Secondary outcomes (1)
- measure
- The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Patients who underwent EUS using a curved line array echoendoscope (GF-UCT260; Olympus Medical Systems) since 2014 in our affiliation. * For each patient, all available native EUS pictures are included. * Patients' diagnosis are validated by surgical outcomes or fine-needle aspiration (FNA) findings and have a compatible clinical course with a follow-up period of more than 6 months. Exclusion Criteria: * The image is of poor quality. * The images contain unique marks which can potentially bias the model, such as the biopsy needle.
References
Publications (1)
- DERIVEDCui H, Zhao Y, Xiong S, Feng Y, Li P, Lv Y, Chen Q, Wang R, Xie P, Luo Z, Cheng S, Wang W, Li X, Xiong D, Cao X, Bai S, Yang A, Cheng B. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open. 2024 Jul 1;7(7):e2422454. doi: 10.1001/jamanetworkopen.2024.22454. PMID 39028670