AI Assisted the Diagnosis of Pancreatic Solid Lesions

Study Overview
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Brief Summary
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Solid lesions of the pancreas mainly include tumor and non tumor lesions. More than 90% of pancreatic tumor lesions are pancreatic cancer, which is characterized by high mortality and poor prognosis and requires surgical treatment; Non-tumor lesions of the pancreas are mainly inflammatory lesions, which usually do not require surgical treatment, but can be treated with drugs. The common ones are chronic pancreatitis and autoimmune pancreatitis, with a good prognosis. Clinically, the differential diagnosis between them is very difficult. Multi-disciplinary diagnosis and treatment of MDT makes our understanding of pancreatic diseases increasingly rich and in-depth. From disease diagnosis to preoperative evaluation and curative effect evaluation, non-invasive imaging involves almost every link under MDT mode. In view of this, improving the differential diagnosis of pancreatic solid space-occupying lesions on imaging will be more conducive to the diagnosis and treatment under MDT mode, so new technologies such as artificial intelligence should be considered. Our goal is to develop a clinically applicable artificial intelligence system, which uses multiple modes to simulate the routine clinical workflow and assist in the diagnosis of benign and malignant pancreatic solid space-occupying lesions.

Detailed Description
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The diagnosis of solid pancreatic lesions is challenging, MDT is a very effective method, but it has a certain misdiagnosis rate. This is a multi-center, prospective and observational clinical study. Our goal is to develop a clinically applicable artificial intelligence system. On the one hand, our artificial intelligence based on clinical data+CT imaging images can assist MDT doctors to diagnose the nature of pancreatic space-occupying lesions and reduce misdiagnosis; On the other hand, if a patient needs EUS-FNA puncture, the multimodal artificial intelligence system based on clinical data+CT+EUS developed by us can help MDT doctors understand the nature of pancreatic space-occupying lesions and reduce the probability of misdiagnosis or secondary puncture.

Official Title
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Enhanced Deep Learning Model for Diagnosis of Pancreatic Solid Lesions Through Multimodal Clinical Images

Conditions
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AI Assist in the Diagnosis of Pancreatic Solid Lesions

Intervention / Treatment
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* Diagnostic Test: Clinicians will review the suggestions of a hypothetical AI


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: pancreatic solid mass in CT and EUS Exclusion Criteria: insufficient imaging quality of CT or EUS endoscopic ultrasound non accessible lesions

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| patients with solid lesions of pancreas<br> | Diagnostic Test: Clinicians will review the suggestions of a hypothetical AI<br>* There is no intervention. Clinicians will review the suggestions of a hypothetical AI<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Researchers use artificial intelligence (AI) support system to assist in diagnosis of pancreatic solid space-occupying lesions | A multi-layer screening deep convolution network based on deep convolution network was developed to observe its accuracy, sensitivity and specificity in assisting MDT doctors to identify benign and malignant pancreatic space-occupying lesions. | 2 months |