One-year recurrence rate of acute pancreatitis at about 20%. 36% of the patients with recurrent acute pancreatitis will develop into chronic pancreatitis. In addition to negative impact on patient's quality of life, chronic pancreatitis is also associated with the occurrence of pancreatic cancer. The etiology of recurrent acute pancreatitis (RAP) can be divided into mechanical obstructive factors (e.g. cholelithiasis, cholestasis), metabolic abnormality and toxic substance factors (e.g. hyperlipidemia and alcoholism), and other or idiopathic factors. At present, the diagnosis and treatment of RAP remains highly challenging. Early identification and intervention on risk factors of recurrence will be effective in reducing incidence and improving prognosis. Contrast-enhanced Computed Tomography (CT) can not only provide more imaging information and further assess the severity of acute pancreatitis, but also aid in the differentiation of other diseases associated with acute abdominal pain. In addition, radiomics based on raw radiographic data has become a research hotspot in recent years. The purpose of this study is to establish and validate a deep learning model based on high concentration iopromide-enhanced abdominal CT images which is designed to predict the recurrence of pancreatitis in patients with first episode of pancreatitis within the 1-year follow-up period.
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The sensitivity and specificity of the model established with relevant clinical factors and radiomic features
Timeframe: 12 months