# Radiomics-based prediction of recurrent acute pancreatitis in individuals with metabolic syndrome using T2WI magnetic resonance imaging data

## TL;DR

This study shows that T2-weighted MRI radiomics can better predict recurring acute pancreatitis in people with metabolic syndrome compared to traditional clinical methods.

## Contribution

A novel radiomics model using T2WI MRI data improves prediction of acute pancreatitis recurrence in metabolic syndrome patients.

## Key findings

- The radiomics model achieved an AUC of 0.825 in training and 0.776 in testing, outperforming clinical models.
- The combined model (clinical + radiomics) had the highest AUC of 0.883 in training and 0.878 in testing.
- Decision curve analysis confirmed the clinical utility of the radiomics and combined models over clinical models.

## Abstract

This study sought to clarify the utility of T2-weighted imaging (T2WI)-based radiomics to predict the recurrence of acute pancreatitis (AP) in subjects with metabolic syndrome (MetS).

Data from 196 patients with both AP and MetS from our hospital were retrospectively analyzed. These patients were separated into two groups according to their clinical follow-up outcomes, including those with first-onset AP (n = 114) and those with recurrent AP (RAP) (n = 82). The 196 cases were randomly divided into a training set (n = 137) and a test set (n = 59) at a 7:3 ratio. The clinical characteristics of these patients were systematically compiled for further analysis. For each case, the pancreatic parenchyma was manually delineated slice by slice using 3D Slicer software, and the appropriate radiomics characteristics were retrieved. The K-best approach, the least absolute shrinkage and selection operator (LASSO) algorithm, and variance thresholding were all used in the feature selection process. The establishment of clinical, radiomics, and combined models for forecasting AP recurrence in patients with MetS was then done using a random forest classifier. Model performance was measured using the area under the receiver operating characteristic curve (AUC), and model comparison was done using the DeLong test. The clinical utility of these models was evaluated using decision curve analysis (DCA), and the optimal model was determined via a calibration curve.

In the training set, the clinical, radiomics, and combined models yielded respective AUCs of 0.651, 0.825, and 0.883, with corresponding test sets of AUCs of 0.606, 0.776, and 0.878. Both the radiomics and combined models exhibited superior predictive effectiveness compared to the clinical model in both the training (p = 0.001, p < 0.001) and test sets (p = 0.04, p < 0.001). The combined model outperformed the radiomics model (training set: p = 0.025, test set: p = 0.019). The DCA demonstrated that the radiomics and combined models had greater clinical efficacy than the clinical model. The calibration curve for the combined model demonstrated good agreement between the predicted probability of AP recurrence and the observed outcomes.

These findings highlight the superior predictive power of a T2WI-based radiomics model for predicting AP recurrence in patients with MetS, potentially supporting early interventions that can mitigate or alleviate RAP.