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

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

Study Overview

• Status: Completed
• Conditions: Pancreatic Ductal Adenocarcinoma; Pancreatitis, Chronic; Pancreatic Neuroendocrine Tumor; Autoimmune Pancreatitis
• Intervention / Treatment: Diagnostic test: EUS-AI model

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 Type: Observational
• Enrollment (Actual): 130

Contacts and Locations

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, 430030
      • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The cohort will be selected from Tongji Hospital, Tongji Medical College, HUST.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Pancreas-EUS; Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort.	Diagnostic test: EUS-AI model; 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.

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Huazhong University of Science and Technology
• Collaborators: LanZhou University; The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Study record dates

Study Major Dates

• Study Start (Actual): July 1, 2022
• Primary Completion (Actual): June 30, 2023
• Study Completion (Actual): January 24, 2024

Study Registration Dates

• First Submitted: July 25, 2022
• First Submitted That Met QC Criteria: July 25, 2022
• First Posted (Actual): July 27, 2022

Study Record Updates

• Last Update Posted (Actual): April 3, 2024
• Last Update Submitted That Met QC Criteria: April 2, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: artificial intelligence; endoscopic ultrasound
• Additional Relevant MeSH Terms: Digestive System Diseases; Pathologic Processes; Immune System Diseases; Neoplasms by Histologic Type; Neoplasms; Autoimmune Diseases; Disease Attributes; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms, Nerve Tissue; Pancreatic Diseases; Chronic Disease; Pancreatitis; Pancreatitis, Chronic; Neuroendocrine Tumors; Autoimmune Pancreatitis
• Other Study ID Numbers: EUS-AI 2022

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No