Radiomics-Based Machine Learning Models Improve Acute Pancreatitis Severity Prediction

(1) Acute pancreatitis (AP) is a medical emergency associated with high mortality rates. Early and accurate prognosis assessment during admission is crucial for optimizing patient management and outcomes. This study seeks to develop robust radiomics-based machine learning (ML) models to classify the severity of AP using contrast-enhanced computed tomography (CECT) scans. (2) Methods: A retrospective cohort of 287 AP patients with CECT scans was analyzed, and clinical data were collected within 72 h of admission. Patients were classified as mild or moderate/severe based on the Revised Atlanta classification. Two radiologists manually segmented the pancreas and peripancreatic regions on CECT scans, and 234 radiomic features were extracted. The performance of the ML algorithms was compared with that of traditional scoring systems, including Ranson and Glasgow-Imrie scores. (3) Results: Traditional severity scoring systems produced AUC values of 0.593 (Ranson, Admission), 0.696 (Ranson, 48 h), 0.677 (Ranson, Cumulative), and 0.663 (Glasgow-Imrie). Using LASSO regression, 12 radiomic features were selected for the ML classifiers. Among these, the best-performing ML classifier achieved an AUC of 0.876 in the training set and 0.777 in the test set. (4) Conclusions: Radiomics-based ML classifiers significantly enhanced the prediction of AP severity in patients undergoing CECT scans within 72 h of admission, outperforming traditional severity scoring systems. This research is the first to successfully predict prognosis by analyzing radiomic features from both pancreatic and peripancreatic tissues using multiple ML algorithms applied to early CECT images.