AUTHOR=Xue Ming , Lin Shuai , Xie Dexuan , Wang Hongzhen , Gao Qi , Zou Lei , Xiao Xigang , Jia Yulin 

TITLE=The value of CT-based radiomics in predicting the prognosis of acute pancreatitis

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1289295

DOI=10.3389/fmed.2023.1289295

ISSN=2296-858X

ABSTRACT=Early judgment of AP progress and timely intervention are crucial to the prognosis of patients. The purpose of this study is to investigate the application value of CT-based radiomics of pancreatic parenchyma in predicting the prognosis of early AP.This retrospective study enrolled 137 patients diagnosed with AP (95 cases in the progressive group and 42 cases in the non-progressive group) who undergo CT scan. Patients were randomly divided into training set (n=95) and validation set(n=42) in a ratio of 7:3. The ROI was outlined along the inner edge of the pancreatic parenchyma manually, and MCTSI was performed.The radiomics features were downscaled and filtered using mRMR and LASSO regression. In addition, the rad-score was calculated. Clinical data was also analyzed to predict the prognosis of AP. Three prediction models, including clinical model, radiomics model, and combined clinical-radiomics model are constructed. The effectiveness of each model was evaluated using ROC curve analysis. The DeLong test was employed to compare the differences between the ROC curves. The DCA is used to assess the net benefit of the model.The mRMR algorithm and LASSO regression were used to select 13 radiomics features with high value.The rad-score of each texture feature was calculated to fuse MCTSI to establish the radiomics model, and both clinical model and clinical-radiomics model were established. The clinical-radiomics model showed the best performance, the AUC, accuracy, sensitivity, and specificity of the clinical-radiomics model in the training set were 0.984, 0.947, 0.955, and 0.931, respectively. In the validation set, they were 0.942, 0.929, 0.966, and 0.846, respectively. The Delong test showed that the predictive efficacy of the clinical-radiomics model was higher than that of the clinical model ( P=0.005) and the radiomics model (P=0.042) in the validation set. DCA demonstrated higher net clinical benefit for the clinical-radiomics model.The pancreatic parenchymal CT clinical-radiomics model has high diagnostic efficacy in predicting the progression of early AP patients, which is significantly better than the clinical model or radiomics model. The combined model can help identify and determine the progression trend of patients with AP and improve the prognosis and survival of patients as early as possible.