Diagnostic and interventional radiology (Ankara, Turkey) · 2025
Multi-organ non-contrast computed tomography radiomics model to predict hepatic encephalopathy in patients with cirrhosis and hepatorenal failure.
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Cao Jin-Ming, Zhang Ming-Ya, Ding Xue-Mei, Zhou Hai-Ying, Zhang Xiao-Ming, Chen Tian-Wu
Аннотация
PURPOSE: To develop and validate a model by incorporating abdominal multi-organ non-contrast computed tomography (CT) radiomics and clinical features to predict the feasibility of hepatic encephalopathy (HE) occurrence in patients with cirrhosis and hepatorenal failure.
METHODS: In total, 351 consecutive patients with cirrhosis and hepatorenal failure undergoing non-contrast abdominal CT scans at Centers 1 and 2 were enrolled. Patients from Center 1 were randomly allocated to training (n = 191) and internal test (n = 81) groups, and those from Center 2 were assigned to the external test group (n = 79). The nnU-Net framework was used for automated three-dimensional (3D) segmentation of abdominal organs-the liver, spleen, portal and splenic vein, inferior vena cava, esophagogastric junction, stomach, liver-adjacent small bowel, and colon. Segmented multi-organ radiomics features were extracted using 3D Slicer, with R software used for feature processing and model construction. Model performance in predicting HE occurrence was evaluated using receiver operating characteristic (ROC) analysis in the training, internal test, and external test cohorts. Decision curve analysis (DCA) was used to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) tool was used to provide a basis for model interpretability analysis.
RESULTS: In total, 351 patients (mean age, 61.3 ± 10.7 years; 231 men) were enrolled in this study. Esophageal variceal bleeding, peritonitis, and ascites were independent clinical predictors of HE. Twenty discriminative radiomics features, selected from the abovementioned multi-organs through intraclass correlation coefficient and least absolute shrinkage and selection operator analysis, were used to construct the radiomics model. The integrated model, incorporating both radiomics and clinical features, obtained higher areas under the ROC curve than the radiomics and clinical models in the training (0.87 vs. 0.83 vs. 0.68), internal test (0.85 vs. 0.81 vs. 0.66), and external test (0.83 vs. 0.78 vs. 0.72) cohorts, as evidenced by favorable integrated discrimination improvement values (P < 0.05 for all). The integrated model demonstrated superior clinical utility in DCA. Moreover, SHAP feature contribution analysis revealed that the top five features in terms of contribution were all extracted from the digestive tract.
CONCLUSION: The integrated model can effectively predict HE occurrence in patients with cirrhosis and hepatorenal failure.
CLINICAL SIGNIFICANCE: This novel model, developed by integrating abdominal multi-organ non-contrast CT radiomics and clinical features, demonstrates robust performance in predicting the occurrence of cirrhosis-related HE in patients with cirrhosis and hepatorenal failure. It thus provides a valuable tool for clinical decision-making, facilitating the prevention of this complication.
METHODS: In total, 351 consecutive patients with cirrhosis and hepatorenal failure undergoing non-contrast abdominal CT scans at Centers 1 and 2 were enrolled. Patients from Center 1 were randomly allocated to training (n = 191) and internal test (n = 81) groups, and those from Center 2 were assigned to the external test group (n = 79). The nnU-Net framework was used for automated three-dimensional (3D) segmentation of abdominal organs-the liver, spleen, portal and splenic vein, inferior vena cava, esophagogastric junction, stomach, liver-adjacent small bowel, and colon. Segmented multi-organ radiomics features were extracted using 3D Slicer, with R software used for feature processing and model construction. Model performance in predicting HE occurrence was evaluated using receiver operating characteristic (ROC) analysis in the training, internal test, and external test cohorts. Decision curve analysis (DCA) was used to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) tool was used to provide a basis for model interpretability analysis.
RESULTS: In total, 351 patients (mean age, 61.3 ± 10.7 years; 231 men) were enrolled in this study. Esophageal variceal bleeding, peritonitis, and ascites were independent clinical predictors of HE. Twenty discriminative radiomics features, selected from the abovementioned multi-organs through intraclass correlation coefficient and least absolute shrinkage and selection operator analysis, were used to construct the radiomics model. The integrated model, incorporating both radiomics and clinical features, obtained higher areas under the ROC curve than the radiomics and clinical models in the training (0.87 vs. 0.83 vs. 0.68), internal test (0.85 vs. 0.81 vs. 0.66), and external test (0.83 vs. 0.78 vs. 0.72) cohorts, as evidenced by favorable integrated discrimination improvement values (P < 0.05 for all). The integrated model demonstrated superior clinical utility in DCA. Moreover, SHAP feature contribution analysis revealed that the top five features in terms of contribution were all extracted from the digestive tract.
CONCLUSION: The integrated model can effectively predict HE occurrence in patients with cirrhosis and hepatorenal failure.
CLINICAL SIGNIFICANCE: This novel model, developed by integrating abdominal multi-organ non-contrast CT radiomics and clinical features, demonstrates robust performance in predicting the occurrence of cirrhosis-related HE in patients with cirrhosis and hepatorenal failure. It thus provides a valuable tool for clinical decision-making, facilitating the prevention of this complication.