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.
Keywords: Computed tomography; Kidney neoplasms; Machine learning; Radiomics; Risk assessment.
© 2021 S. Karger AG, Basel.