Abstract
Objective: This study aimed to predict liver injury in AP patients by establishing a radiomics model based on CECT.
Methods: A total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by LASSO were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model, and combined model.
Results: Four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the AUC of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and the test cohort (0.925 vs. 0.910, p = 0.302).
Conclusion: The radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.
Keywords: Radiomics, Acute pancreatitis, Liver injury, Contrast-enhanced computed tomography, Pancreatitis enzymes, Radiomics model.
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