Radiomic fEatures of Pancreas From Contrast Enhanced CT Image Predict One-Year RecUrrence Risk of Acute PancReatitis — RECUR  

Pancreatitis, Chronic Clinical Trial

Official title: Radiomic fEatures of Pancreas From High-Concentration Contrast Enhanced CT Image Predict One-Year RecUrrence Risk of Acute PancReatitis: A Prospective, Multicenter Study

Status Not yet recruiting

Clinical Trial Summary

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.

Clinical Trial Description

Primary objective(s) To evaluate the sensitivity and specificity of the deep-learning integrated model established with relevant clinical factors and radiomic features based on the high concentration (370 mgI/ml) Iopromide-enhanced pancreatic CT obtained within 14 days after the first onset of symptoms for quantitative prediction of (the first) recurrence of acute pancreatitis in 12 months follow-up period. Sample Size: According to previously published data, the average time of occurrence of RAP is 12.5 ± 3.6 months and one-year recurrence rate of acute pancreatitis is about 20%. In addition, the recurrence rate is estimated to be about 17% within the 12-month follow-up window in this study, when taking into account the clinical experience of our hospital. The calculation parameters for sample size of the training set in the study are as follows: 1. Z1-α/2 is1.96 at α=0.05 2. L, the width of the acceptable 95% confidential interval of sensitivity or specificity, 0.03-0.1 3. The sensitivity is 0.85, the specificity is 0.98, and the disease prevalence is 0.17 Calculated based on sensitivity, N1= 1.962X0.85 X (1-0.85)/0.12 X 0.17= 0.490/0.0017=288 Calculated based on specificity, N2=1.962X0.98 X (1-0.98)/0.12 X (1-0.17) = 0.075/0.008=61 The sample size of the training set is 288 x 1.2 = 346 considering a dropout rate of 20% in the study. The training set, test set and validation set are estimated in a ratio of 5:2:3. The total sample size for this prospective study is 694. According to the order of patient enrollment, the last 200 patients recruited will form the validation set. Statistical Analyses: At baseline and follow-up, descriptive statistics will be used to describe the entire population and subgroups of interest. Summary statistics such as mean, median, standard deviation and range will be used to describe continuous variables. Categorical variables will be presented in a frequency table. - Primary endpoint analysis For patients with acute pancreatitis undergoing enhanced CT scan, the model that used the combination of radiomics and clinical features is used to predict the recurrence probability of acute pancreatitis within 12 months. The sensitivity and specificity of prediction and corresponding 95% CI are calculated. - Secondary endpoint analysis Chi-square testing for all potential clinical risk factors included (as described in the Chapter on Variables and Criteria Used in Determining Primary Endpoints). The variables with p< 0.05 are analyzed for multivariate logistic regression and clinical modeling. Also based on the logistic regression model, a combination model of radioomic features and clinical factors is established. The sensitivity, specificity and corresponding 95% CI for prediction of recurrence within 1, 3, 6 and 12 months are calculated based on the model that used both clinical features and/or radiomics features. Only the first recurrence is calculated. Brief statistics of the quality of CT images will be provided. • Baseline demographic characteristics Demographic and baseline characteristics will be summarized descriptively. Sensitivity= TP/(TP+FN) Specificity=TN/(TN+FP) Accuracy = (TP+TN)/(TP+FN+TN+FP) TP=True positive TP=True negative FN=False negative FP=False positive TP+FN+TN+FP=Total number of patients Statistical analyses are performed using R software (R Core Team, Vienna, Austria) version 3.4.3 All tests are two-sided. A P value < 0.05 is considered statistically significant. All therapies will be coded using the World Health Organization - Drug Dictionary (WHO-DD). Medical history and any disease will be coded using the most current version of ICH Medical Dictionary for Regulatory Activities (MedDRA). ;

Related Conditions & MeSH terms

• Pancreatitis
• Pancreatitis, Chronic
• Recurrence

• NCT number: NCT05778929
• Study type: Observational
• Source: Peking University People's Hospital
• Status: Not yet recruiting
• Start date: April 1, 2023
• Completion date: April 1, 2025