Published online May 27, 2026. doi: 10.4240/wjgs.v18.i5.115903
Revised: December 16, 2025
Accepted: February 4, 2026
Published online: May 27, 2026
Processing time: 209 Days and 6.9 Hours
Acute necrotizing pancreatitis (ANP), a more severe form of acute pancreatitis, requires early diagnosis and accurate severity stratification for optimal patient prognosis and treatment. Currently, most scholars do not clearly differentiate between ANP and severe acute pancreatitis, although they are distinct clinical entities.
To investigate the value of radiomics derived from contrast-enhanced computed tomography (CECT) of the pancreatic parenchyma and peripancreatic necrotic collections, combined with various machine learning algorithms, to differentiate between severe and moderately severe ANP.
We conducted a retrospective cohort study of 184 ANP patients (72 severe, 112 moderately severe), randomly divided into training and test cohorts in a 7:3 ratio. On portal venous phase CECT images, regions of interest encompassing the entire pancreatic parenchyma and peripancreatic necrotic collections were manually delineated on a slice-by-slice basis. Radiomic features were then extracted from the regions of interest using the PyRadiomics package. Feature selection was performed using intraclass and interclass correlation coefficients, independent samples t-tests, and the light gradient boosting machine algorithm. The classification models were constructed using support vector machine, random forest (RF), k-nearest neighbor, gradient boosting decision tree, and extreme gradient boosting algorithms combined with 10-fold cross-validation, developing three distinct models: the pancreatic model, the peripancreatic model, and the combined model. The performance of each model was evaluated by analyzing receiver operating characteristic curves, the area under the curve, accuracy, sensitivity, specificity, F1-score, and Brier score.
The combined RF model demonstrated superior performance compared to other models (support vector machine, k-nearest neighbor, gradient boosting decision tree, and extreme gradient boosting) for differentiating between severe and moderately severe ANP. It achieved the best results in the test cohort, with an area under the curve of 0.896 (95% confidence interval: 0.778-0.977), accuracy of 0.839, sensitivity of 0.650, specificity of 0.944, F1-score of 0.743, and Brier score of 0.134.
Radiomic analysis of both the pancreatic parenchyma and peripancreatic necrotic collections on CECT, combined with machine learning, effectively differentiates between severe and moderately severe ANP. The combined RF model showed superior performance. This approach shows potential for improving early diagnostic accuracy, aiding clinical decision-making, and optimizing treatment strategies. The refined classification system facilitates better resource allocation, patient triage, and stratification.
Core Tip: Acute necrotizing pancreatitis (ANP), a more severe form of acute pancreatitis, requires early diagnosis and accurate severity stratification for optimal patient prognosis and treatment. This study demonstrates that radiomics based on contrast-enhanced computed tomography of both pancreatic parenchyma and peripancreatic necrotic collections, combined with machine learning algorithms, can effectively differentiate between severe and moderately severe ANP. This model may serve as a valuable adjunct clinical decision support tool, and its refined classification of ANP into severe and moderately severe categories could help optimize resource allocation and improve patient triage.