An observational study in Pancreatic Ductal Adenocarcinoma, Pancreatitis, Chronic and Pancreatic Neuroendocrine Tumor, sponsored by Huazhong University of Science and Technology. Completed at 1 site in China. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2024-04-03.
Sponsored by Huazhong University of Science and Technology · Observational
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.
676 studies on the registry are indexed under Neuroendocrine Tumors; 169 are open to participants now.
This study's enrollment of 130 is above the median of 115 across 176 observational studies indexed under Neuroendocrine Tumors.
Browse Neuroendocrine Tumors studies →Huazhong University of Science and Technology is the lead sponsor of 242 studies on the registry; 61 are open to participants now.
Counted across the registry records on this site, refreshed daily.
The cohort will be selected from Tongji Hospital, Tongji Medical College, HUST.
Exclusion Criteria:
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.
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.
Time frame: After the training process of the EUS-AI model is completed
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.
Time frame: After the training process of the EUS-AI model is completed
This study is completed, as verified in Apr 2024. You cannot join it, but the record below documents what was studied.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Huazhong University of Science and Technology