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Completed

NCT Number: NCT05476978

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.

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Key information

Conditions

Pancreatic Ductal Adenocarcinoma Adenoma Adenoma, Islet Cell Autoimmune Diseases Autoimmune Pancreatitis Chronic Disease Digestive System Diseases Digestive System Neoplasms Disease Attributes Endocrine Gland Neoplasms Endocrine System Diseases Immune System Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Pancreatic Diseases Pancreatic Neoplasms Pancreatic Neuroendocrine Tumor Pancreatitis Pancreatitis, Chronic Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology

Wuhan, Hubei, 430030, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

EUS-AI model

Diagnostic Test

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.

Primary outcomes

  1. The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion

    Time frame: 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 outcomes

  1. The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET

    Time frame: 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.

Sponsors and collaborators

Lead sponsor

Huazhong University of Science and Technology

Other

Collaborators

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

Registry information

Official study 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

Important dates

Study start
2022
Primary completion
2023
Study completion
2024
First posted
Jul 27, 2022
Registry last updated
Apr 3, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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