Value of Radiomics of Perinephric Fat for Prediction of Intraoperative Complexity in Renal Tumor Surgery

Abstract

Introduction: The aim of this study was to assess the value of computed tomography (CT)-based radiomics of perinephric fat (PNF) for prediction of surgical complexity. Methods: Fifty-six patients who underwent renal tumor surgery were included. Radiomic features were extracted from contrast-enhanced CT. Machine learning models using radiomic features, the Mayo Adhesive Probability (MAP) score, and/or clinical variables (age, sex, and body mass index) were compared for the prediction of adherent PNF (APF), the occurrence of postoperative complications (Clavien-Dindo Classification ≥2), and surgery duration. Discrimination performance was assessed by the area under the receiver operating characteristic curve (AUC). In addition, the root mean square error (RMSE) and R² (fraction of explained variance) were used as additional evaluation metrics. Results: A single feature logit model containing “Wavelet-LHH-transformed GLCM Correlation” achieved the best discrimination (AUC 0.90, 95% confidence interval [CI]: 0.75–1.00) and lowest error (RMSE 0.32, 95% CI: 0.20–0.42) at prediction of APF. This model was superior to all other models containing all radiomic features, clinical variables, and/or the MAP score. The performance of uninformative benchmark models for prediction of postoperative complications and surgery duration were not improved by machine learning models. Conclusion: Radiomic features derived from PNF may provide valuable information for preoperative risk stratification of patients undergoing renal tumor surgery.