Artificial Intelligence In EUS For Diagnosing Pancreatic Solid Lesions

Study Overview

Artificial Intelligence in EUS for Diagnosing 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.

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.

Pancreatic Ductal Adenocarcinoma
Pancreatitis, Chronic
Pancreatic Neuroendocrine Tumor
Autoimmune Pancreatitis

DIAGNOSTIC_TEST: EUS-AI model

EUS-AI 2022

Study Record Dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Registration Dates	Results Reporting Dates	Study Record Updates
First Submitted 2022-07-25	Results First Submitted N/A	Last Update Submitted that met QC Criteria 2024-04-02
First Submitted that Met QC Criteria 2022-07-25	Results First Submitted that Met QC Criteria N/A	Last Update Posted (ACTUAL) 2024-04-03
First Posted (ACTUAL) 2022-07-27	Results First Posted N/A	Last Verified 2024-04

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

Design Details

Primary Purpose:
N/A

Allocation:
N/A

Interventional Model:
N/A

Masking:
N/A

Arms and Interventions

Participant Group/Arm	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 di

Participant Group/Arm

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 di

Primary Outcome Measures	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	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

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person’s general health condition or prior treatments.

Ages Eligible for Study:
ALL

Sexes Eligible for Study:
18 Years

Accepts Healthy Volunteers:

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.

The image is of poor quality.
The images contain unique marks which can potentially bias the model, such as the biopsy needle.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School
LanZhou University

Investigators

Publications

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Cui 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.

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Resources

PatientS

Caregivers

Clinical Trial Record