Frontiers in nutrition · 2026
CT-based radiomics-clinical model for risk assessment of parenteral nutrition-associated hepatic steatosis in chronic intestinal failure and its metabolomic interpretation.
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Xia Yufei, Li Ruochen, Liu Sirui, Zhou Pinwen, Wang Jiaqi, Qi Xin, Zhu Minyi, Sun Guangming, Gao Xuejin, Zhang Li, Maitiabula Gulisudumu, Wang Xinying
Аннотация
BACKGROUND: Patients with chronic intestinal failure (CIF) dependent on parenteral nutrition (PN) are at risk of developing parenteral nutrition-associated hepatic steatosis (PNAHS), a condition that can progress to hepatic fibrosis. Effective methods for predicting PNAHS risk are lacking.
METHODS: This retrospective study included 307 CIF patients. Patients were divided into training (n = 219) and testing (n = 88) sets. Radiomic features (n = 1,037 per patient) were extracted from non-contrast abdominal CT scans obtained within 1 week before PN initiation. Clinical characteristics (e.g., demographics, laboratory values) were collected. Multiple predictive models were developed: radiomics, clinical, combined radiomics-clinical, and various deep learning models (e.g., DenseNet121, ResNet18, Unet, etc.). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis and Log-rank test. Serum metabolomics analysis was performed to explore biological implications of the model-derived risk scores.
RESULTS: The combined model demonstrated the highest AUC of 0.862 (95% CI: 0.782-0.942) in the testing set, significantly outperforming most other models (p < 0.05). Key predictors in the combined model included the radiomics score, total cholesterol level, urea level, PN frequency, and L3-intermuscular fat area. The combined score also demonstrated considerable capability in stratifying patients by duration of PN dependency. The distinct PNAHS risk groups identified by the combined model exhibited significant differences in their metabolic profiles.
CONCLUSION: We developed a novel predictive model that integrates CT radiomics features with clinical characteristics to effectively predict the risk of PNAHS in patients with CIF and reflects underlying metabolic disturbance.
METHODS: This retrospective study included 307 CIF patients. Patients were divided into training (n = 219) and testing (n = 88) sets. Radiomic features (n = 1,037 per patient) were extracted from non-contrast abdominal CT scans obtained within 1 week before PN initiation. Clinical characteristics (e.g., demographics, laboratory values) were collected. Multiple predictive models were developed: radiomics, clinical, combined radiomics-clinical, and various deep learning models (e.g., DenseNet121, ResNet18, Unet, etc.). Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis and Log-rank test. Serum metabolomics analysis was performed to explore biological implications of the model-derived risk scores.
RESULTS: The combined model demonstrated the highest AUC of 0.862 (95% CI: 0.782-0.942) in the testing set, significantly outperforming most other models (p < 0.05). Key predictors in the combined model included the radiomics score, total cholesterol level, urea level, PN frequency, and L3-intermuscular fat area. The combined score also demonstrated considerable capability in stratifying patients by duration of PN dependency. The distinct PNAHS risk groups identified by the combined model exhibited significant differences in their metabolic profiles.
CONCLUSION: We developed a novel predictive model that integrates CT radiomics features with clinical characteristics to effectively predict the risk of PNAHS in patients with CIF and reflects underlying metabolic disturbance.