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Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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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 . 0.653, p = 0.000) and test cohort (0.910 . 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 . 0.993, p = 0.357) and the test cohort (0.925 . 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.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-06-17

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/content/journals/cmir/10.2174/0115734056307393240623140851
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Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography
Curr. Med. Imaging 20, e15734056307393 (2024); https://doi.org/10.2174/0115734056307393240623140851
/content/journals/cmir/10.2174/0115734056307393240623140851
/content/journals/cmir/10.2174/0115734056307393240623140851
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  • Article Type:     Research Article
Keyword(s): Acute pancreatitis; Contrast-enhanced computed tomography; Liver injury; Pancreatitis enzymes; Radiomics; Radiomics model

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