Prediction of acute pancreatitis severity based on early CT radiomics

作者：Mingyao Qi, Chao Lu, Rao Dai, Jiulou Zhang, Hui Hu, Xiuhong Shan · 发表于：BMC Medical Imaging · 年份：2024 · DOI：10.1186/s12880-024-01509-9 · 被引用次数：15 · 研究领域：Pancreatitis Pathology and Treatment、Pancreatic and Hepatic Oncology Research、Amoebic Infections and Treatments

BACKGROUND: This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity. METHODS: A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis. RESULTS: A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. T...