Abstract
Background:
Orthopedic patients, especially those with bone tumors, are prone to perioperative acute kidney injury (AKI). This study integrates ultrasound radiomics with machine learning to predict AKI risk.
Methods:
A retrospective cohort of 120 patients from this center with fractures or bone tumors was analyzed. Ultrasound images were preprocessed and manually segmented to define kidney regions of interest. Morphological, texture, intensity, and higher order features were extracted. Feature selection was performed using least absolute shrinkage and selection operator, random forest importance ranking, and support vector machine–recursive feature elimination. Ten machine learning algorithms were trained with internal cross validation, and their performance was assessed by area under the curve (AUC), accuracy, calibration, and decision curve analysis. The final optimized model was applied to real-world ultrasound images, and class activation mapping was used to visualize AKI-related regions through interpretable heatmaps.
Results:
Three radiomic features consistently associated with AKI were identified across all the selection methods. Among the tested algorithms, the extreme gradient boosting (XGBoost) model achieved the best performance, with AUCs of 0.932, 0.922, and 0.893 in the training, internal, and external validation sets, respectively. The model demonstrated good calibration and high clinical net benefit. An interpretable risk-scoring system visualized individualized postoperative AKI risk, revealing higher predicted risk in bone tumor and complex fracture patients.
Conclusion:
This study highlights three ultrasound-derived features as critical determinants of postoperative AKI. The XGBoost model built on these features provides accurate and interpretable prediction in orthopedic patients and holds promise for guiding individualized perioperative kidney protection.
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