Table 3.

Summary of the best radiomics for predicting invasive procedural management of renal calculi, presence of hydronephrosis, presence of renal calculi, and median volume of renal calculi

	Best features from multiple logistic regression	AUC	95% CI
Patient management (in all 202 patients)	Imc2 (GLCM) + Imc1 (GLCM) + Minimum (1st Order) + Short-run low gray-level emphasis (GLRLM)	0.91	0.85–0.92
Hydronephrosis (in all 202 patients)	Dependence non-uniformity (GLDM) + Small area emphasis (GLSZM) + Size zone non-uniformity (GLSZM)	0.89	0.8–0.89
Hydronephrosis (in 123 patients with calculi)	Dependence non-uniformity (GLDM) + Small area emphasis (GLSZM) + Dependence non-uniformity normalized (GLDM) + Size zone non-uniformity (GLSZM)	0.85	0.77–0.87
Renal calculi detection (in all 202 patients)	Short-run low gray-level emphasis (GLRLM) + Run variance (GLRLM) + Run entropy (GLRLM) + MCC (GLCM)	0.84	0.78–0.89
Renal calculi detection (in all 404 kidneys)	Idmn (GLCM) + Coarseness (NGTDM) + Short-run low gray-level emphasis (GLRLM)	0.9	0.85–0.93
Median volume of calculi (in all 404 kidneys)	Idn (GLCM) + Sum entropy (GLCM)	0.93	0.9–0.95

AUC area under the curve, CI confidence interval, GLCM gray-level co-occurrence matrix, GLRLM gray-level run length matrix, GLSZM gray-level size zone matrix, GLDM gray-level dependence matrix, NGTDM neighboring gray-tone difference matrix, Idmn inverse difference moment normalized, Imc1 informational measure of correlation 1, Imc2 informational measure of correlation 2, Idn inverse difference normalized