To construct a radiomics-score model based on enhanced CT images of patients with first-time acute pancreatitis for the prediction of recurrence.

A total of 135 patients with acute pancreatitis treated at our hospital from February 2019 to June 2023 were included. The study cohort was randomly divided into a training group (n=95) and a validation group (n=40). Radiomics features were extracted and selected from arterial and venous phase enhanced CT images. Clinical factors were collected, and a multifactor logistic regression was used to build radiomics-score model, clinical models, and combined model. Receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), and predictive indicators were employed to assess the predictive capabilities of the three models. Decision curve analysis (DCA) was used to compare the net benefits of different models in clinical decision-making. The combined model was presented as a nomogram to provide the risk probability of recurrence for patients with acute pancreatitis.

Thirty-three of 95 patients in the training group had recurrent acute pancreatitis, and 13 of 40 patients in the verification group had recurrent pancreatitis, which was divided into 46 patients in the recurrent group and 89 patients in the non-recurrent group. After dimensionality reduction of radiomics features and clinical factor selection, four optimal imageomics features and two clinical factors (hyperlipidemia and modified CT severity index) were used to construct radiomics-score model and clinical model, respectively. The radiomics-score model demonstrated better predictive performance in both the training and validation groups compared to the clinical model (AUC_{training group}: 0.874 vs. 0.713, P=0.01; AUC_{validation group}: 0.822 vs. 0.734, P=0.036). The combined model improved the clinical net benefit in predicting pancreatitis recurrence.

The radiomics-score model based on enhanced CT images shows significant advantages in enhancing individualized prediction of acute pancreatitis recurrence. This model can be valuable in clinical practice for the prevention and treatment of pancreatitis recurrence.