Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

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

Detailed Description
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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.

Official Title
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Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions

Conditions
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Pancreatitis, Chronic, Pancreatic Neuroendocrine Tumor, Autoimmune Pancreatitis, Pancreatic Ductal Adenocarcinoma

Intervention / Treatment
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* Diagnostic Test: EUS-AI model


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Pancreas-EUS<br>Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort. | Diagnostic Test: EUS-AI model<br>* The test subset (approximately 20% of total patients) is reserved for the final evaluation of the EUS-AI model. Clinical parameters and EUS pictures of each patient in the test subset will be inputed into the trained EUS-AI model, and the most possible diagnosis will be given by the model.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion | 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. | After the training process of the EUS-AI model is completed | 

Secondary Outcome Measures

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

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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artificial intelligence, endoscopic ultrasound