Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

Pancreatic Ductal Adenocarcinoma Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05476978
• Other study ID #: EUS-AI 2022
• Status: Completed
• Start date: July 1, 2022
• Est. completion date: January 24, 2024

Study information

• Verified date: April 2024
• Source: Huazhong University of Science and Technology
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 130
• Est. completion date: January 24, 2024
• Est. primary completion date: June 30, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Autoimmune Pancreatitis
• Neuroendocrine Tumors
• Pancreatic Ductal Adenocarcinoma
• Pancreatic Neuroendocrine Tumor
• Pancreatitis
• Pancreatitis, Chronic

Intervention

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.

Locations

Country	Name	City	State
China	Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology	Wuhan	Hubei

Sponsors (3)

Lead Sponsor	Collaborator
Huazhong University of Science and Technology	LanZhou University, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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