Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

Completed

• Conditions: Pancreatitis, Chronic; Pancreatic Neuroendocrine Tumor; Autoimmune Pancreatitis; Pancreatic Ductal Adenocarcinoma
• Interventions: Diagnostic Test: EUS-AI model

• Registration Number: NCT05476978
• Lead Sponsor: Huazhong University of Science and Technology

Brief Summary

We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.

Detailed Description

EUS is considered to be a more sensitive modality than CT in detecting pancreatic solid lesions due to its high spatial resolution. However, the diagnostic performance is largely dependent on the experience and the technical abilities of the practitioners. Therefore, we aim to develop an objective EUS diagnostic model based on the convolutional neural network, an artificial intelligence technique. In addition, clinical parameters such as risk factors, tumor biomarkers and radiology findings are also added to this artificial intelligence model in order to mimic the actual clinical diagnosis procedures and to increase the performance of this model.

Study Timeline

• Study Start: 2022-07-01
• Study First Submitted: 2022-07-25
• Study First Submitted QC: 2022-07-25
• Study First Posted: 2022-07-27
• Primary Completion: 2023-06-30
• Study Completion: 2024-01-24
• Status Verified: 2024-04
• Last Update Submitted: 2024-04-02
• Last Update Posted: 2024-04-03

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 130

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Pancreas-EUS	EUS-AI model	Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort.

Primary Outcome Measures

Name	Time	Method
The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion	After the training process of the EUS-AI model is completed	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 Outcome Measures

Name	Time	Method
The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET	After the training process of the EUS-AI model is completed	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.

Trial Locations

Locations (1)

Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology; 🇨🇳 Wuhan, Hubei, China