Prediction of acute pancreatitis severity based on early CT radiomics
Abstract Background This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity. Methods A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for...
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2024-11-01
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| Online Access: | https://doi.org/10.1186/s12880-024-01509-9 |
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| author | Mingyao Qi Chao Lu Rao Dai Jiulou Zhang Hui Hu Xiuhong Shan |
| author_facet | Mingyao Qi Chao Lu Rao Dai Jiulou Zhang Hui Hu Xiuhong Shan |
| author_sort | Mingyao Qi |
| collection | DOAJ |
| description | Abstract Background This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity. Methods A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis. Results A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793–0.949) in the training cohort and 0.859 (95% CI, 0.751–0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756–0.910) and 0.810 (95% CI, 0.692–0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837–0.973) in the training cohort and 0.908 (95% CI, 0.824–0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone. Conclusion The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making. |
| format | Article |
| id | doaj-art-2888b4e62dc546c78ae2f4a7eebed37c |
| institution | OA Journals |
| issn | 1471-2342 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-2888b4e62dc546c78ae2f4a7eebed37c2025-08-20T02:38:40ZengBMCBMC Medical Imaging1471-23422024-11-0124111110.1186/s12880-024-01509-9Prediction of acute pancreatitis severity based on early CT radiomicsMingyao Qi0Chao Lu1Rao Dai2Jiulou Zhang3Hui Hu4Xiuhong Shan5Department of Radiology, Affiliated People’s Hospital of Jiangsu UniversityDepartment of Radiology, Affiliated People’s Hospital of Jiangsu UniversityDepartment of Radiology, Affiliated People’s Hospital of Jiangsu UniversityArtificial Intelligence Imaging Laboratory, Nanjing Medical UniversityDepartment of Radiology, Affiliated Hospital of Nanjing University of Chinese MedicineDepartment of Radiology, Affiliated People’s Hospital of Jiangsu UniversityAbstract Background This study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity. Methods A retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis. Results A total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793–0.949) in the training cohort and 0.859 (95% CI, 0.751–0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756–0.910) and 0.810 (95% CI, 0.692–0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837–0.973) in the training cohort and 0.908 (95% CI, 0.824–0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone. Conclusion The clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making.https://doi.org/10.1186/s12880-024-01509-9Acute pancreatitisComputed tomographyRadiomicsNomogram |
| spellingShingle | Mingyao Qi Chao Lu Rao Dai Jiulou Zhang Hui Hu Xiuhong Shan Prediction of acute pancreatitis severity based on early CT radiomics BMC Medical Imaging Acute pancreatitis Computed tomography Radiomics Nomogram |
| title | Prediction of acute pancreatitis severity based on early CT radiomics |
| title_full | Prediction of acute pancreatitis severity based on early CT radiomics |
| title_fullStr | Prediction of acute pancreatitis severity based on early CT radiomics |
| title_full_unstemmed | Prediction of acute pancreatitis severity based on early CT radiomics |
| title_short | Prediction of acute pancreatitis severity based on early CT radiomics |
| title_sort | prediction of acute pancreatitis severity based on early ct radiomics |
| topic | Acute pancreatitis Computed tomography Radiomics Nomogram |
| url | https://doi.org/10.1186/s12880-024-01509-9 |
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