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

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.

Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis.

Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25-7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40-3.39, p < 0.001), 3.08 (95%-CI 2.09-4.06, p < 0.001) and 3.57 (95%-CI 2.69-4.45, p < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73-3.62, p < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.