Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis
Abstract Objectives To develop and validate the performance of CT-based radiomics models for predicting the prognosis of acute pancreatitis. Methods All 344 patients (51 ± 15 years, 171 men) in a first episode of acute pancreatitis (AP) were retrospectively enrolled and randomly divided into trainin...
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SpringerOpen
2025-01-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-024-01887-2 |
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| author | Hang Chen Yao Wen Xinya Li Xia Li Liping Su Xinglan Wang Fang Wang Dan Liu |
| author_facet | Hang Chen Yao Wen Xinya Li Xia Li Liping Su Xinglan Wang Fang Wang Dan Liu |
| author_sort | Hang Chen |
| collection | DOAJ |
| description | Abstract Objectives To develop and validate the performance of CT-based radiomics models for predicting the prognosis of acute pancreatitis. Methods All 344 patients (51 ± 15 years, 171 men) in a first episode of acute pancreatitis (AP) were retrospectively enrolled and randomly divided into training (n = 206), validation (n = 69), and test (n = 69) sets with the ratio of 6:2:2. The patients were dichotomized into good and poor prognosis subgroups based on follow-up CT and clinical data. The radiomics features were extracted from contrast-enhanced CT. Logistic regression analysis was applied to analyze clinical-radiological features for developing clinical and radiomics-derived models. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results Eight pancreatic and six peripancreatic radiomics features were identified after reduction and selection. In the training set, the AUCs of clinical, pancreatic, peripancreatic, radiomics, and combined models were 0.859, 0.800, 0.823, 0.852, and 0.899, respectively. In the validation set, the AUCs were 0.848, 0.720, 0.746, 0.773, and 0.877, respectively. The combined model exhibited the highest AUC among radiomics-based models (pancreatic, peripancreatic, and radiomics models) in both the training (0.899) and validation (0.877) sets (all p < 0.05). Further, the AUC of the combined model was 0.735 in the test set. The calibration curve and DCA indicated the combined model had favorable predictive performance. Conclusions CT-based radiomics incorporating clinical features was superior to other models in predicting AP prognosis, which may offer additional information for AP patients at higher risk of developing poor prognosis. Critical relevance statement Integrating CT radiomics-based analysis of pancreatic and peripancreatic features with clinical risk factors enhances the assessment of AP prognosis, allowing for optimal clinical decision-making in individuals at risk of severe AP. Key Points Radiomics analysis provides help to accurately assess acute pancreatitis (AP). CT radiomics-based models are superior to the clinical model in the prediction of AP prognosis. A CT radiomics-based nomogram integrated with clinical features allows a more comprehensive assessment of AP prognosis. Graphical Abstract |
| format | Article |
| id | doaj-art-faf39ccc00094d3e9ef542b7ff1392c1 |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
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| series | Insights into Imaging |
| spelling | doaj-art-faf39ccc00094d3e9ef542b7ff1392c12025-01-12T12:26:33ZengSpringerOpenInsights into Imaging1869-41012025-01-0116111110.1186/s13244-024-01887-2Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitisHang Chen0Yao Wen1Xinya Li2Xia Li3Liping Su4Xinglan Wang5Fang Wang6Dan Liu7Department of Radiology, Yongchuan Hospital of Chongqing Medical UniversityDepartment of Radiology, Chongqing Beibei District Hospital of Traditional Chinese MedicineDepartment of Radiology, Yongchuan Hospital of Chongqing Medical UniversityDepartment of Radiology, Yongchuan Hospital of Chongqing Medical UniversityDepartment of Radiology, Yongchuan Hospital of Chongqing Medical UniversityDepartment of Radiology, Yongchuan Hospital of Chongqing Medical UniversityShanghai United Imaging IntelligenceDepartment of Radiology, Yongchuan Hospital of Chongqing Medical UniversityAbstract Objectives To develop and validate the performance of CT-based radiomics models for predicting the prognosis of acute pancreatitis. Methods All 344 patients (51 ± 15 years, 171 men) in a first episode of acute pancreatitis (AP) were retrospectively enrolled and randomly divided into training (n = 206), validation (n = 69), and test (n = 69) sets with the ratio of 6:2:2. The patients were dichotomized into good and poor prognosis subgroups based on follow-up CT and clinical data. The radiomics features were extracted from contrast-enhanced CT. Logistic regression analysis was applied to analyze clinical-radiological features for developing clinical and radiomics-derived models. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). Results Eight pancreatic and six peripancreatic radiomics features were identified after reduction and selection. In the training set, the AUCs of clinical, pancreatic, peripancreatic, radiomics, and combined models were 0.859, 0.800, 0.823, 0.852, and 0.899, respectively. In the validation set, the AUCs were 0.848, 0.720, 0.746, 0.773, and 0.877, respectively. The combined model exhibited the highest AUC among radiomics-based models (pancreatic, peripancreatic, and radiomics models) in both the training (0.899) and validation (0.877) sets (all p < 0.05). Further, the AUC of the combined model was 0.735 in the test set. The calibration curve and DCA indicated the combined model had favorable predictive performance. Conclusions CT-based radiomics incorporating clinical features was superior to other models in predicting AP prognosis, which may offer additional information for AP patients at higher risk of developing poor prognosis. Critical relevance statement Integrating CT radiomics-based analysis of pancreatic and peripancreatic features with clinical risk factors enhances the assessment of AP prognosis, allowing for optimal clinical decision-making in individuals at risk of severe AP. Key Points Radiomics analysis provides help to accurately assess acute pancreatitis (AP). CT radiomics-based models are superior to the clinical model in the prediction of AP prognosis. A CT radiomics-based nomogram integrated with clinical features allows a more comprehensive assessment of AP prognosis. Graphical Abstracthttps://doi.org/10.1186/s13244-024-01887-2Computed tomographyAcute pancreatitisRadiomicsPrognosis |
| spellingShingle | Hang Chen Yao Wen Xinya Li Xia Li Liping Su Xinglan Wang Fang Wang Dan Liu Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis Insights into Imaging Computed tomography Acute pancreatitis Radiomics Prognosis |
| title | Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis |
| title_full | Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis |
| title_fullStr | Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis |
| title_full_unstemmed | Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis |
| title_short | Integrating CT-based radiomics and clinical features to better predict the prognosis of acute pancreatitis |
| title_sort | integrating ct based radiomics and clinical features to better predict the prognosis of acute pancreatitis |
| topic | Computed tomography Acute pancreatitis Radiomics Prognosis |
| url | https://doi.org/10.1186/s13244-024-01887-2 |
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