Title:Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography
Volume: 20
Author(s): Lu Liu, Ningjun Yu, Tingting Liu, Shujun Chen, Yu Pu, Xiaoming Zhang*Xinghui Li*
Affiliation:
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South
Maoyuan Street, Nanchong 637001, China
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South
Maoyuan Street, Nanchong 637001, China
Keywords:
Radiomics, Acute pancreatitis, Liver injury, Contrast-enhanced computed tomography, Pancreatitis enzymes, Radiomics model.
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.
