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
NCT number | NCT05476978 |
Other study ID # | EUS-AI 2022 |
Secondary ID | |
Status | Completed |
Phase | |
First received | |
Last updated | |
Start date | July 1, 2022 |
Est. completion date | January 24, 2024 |
Verified date | April 2024 |
Source | Huazhong University of Science and Technology |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.
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. |
Country | Name | City | State |
---|---|---|---|
China | Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology | Wuhan | Hubei |
Lead Sponsor | Collaborator |
---|---|
Huazhong University of Science and Technology | LanZhou University, The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School |
China,
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 |
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