Computational and Mathematical Methods in Medicine / 2022 / Article / Tab 1 / Research Article
[Retracted] Feasibility of Constructing an Automatic Meniscus Injury Detection Model Based on Dual-Mode Magnetic Resonance Imaging (MRI) Radiomics of the Knee Joint Table 1 ICCs of the remaining eight features after Model 1 and Model 2 redundancy analysis.
Groups Radiomics signatures Intraobserver Interobserver Model 1 logsigma50mm3D_glcm_InverseVariance_sag 0.994 0.982 square_glszm_ZoneEntropy_sag 0.995 0.998 logsigma50mm3D_glcm_Correlation_sag 0.983 0.916 logsigma50mm3D_firstorder_Skewness_sag 0.832 0.814 logarithm_gldm_LargeDependenceEmphasis_sag 0.891 0.923 logarithm_glcm_DifferenceAverage_sag 0.962 0.971 logsigma40mm3D_glszm_SmallAreaEmphasis_sag 0.891 0.845 original_gldm_LowGrayLevelEmphasis_sag 0.990 0.911 Model 2 square_glcm_Imc2_cor 0.936 0.901 waveletHHH_gldm_LowGrayLevelEmphasis_cor 0.877 0.921 logsigma50mm3D_glcm_InverseVariance_cor 0.964 0.991 waveletHHH_gldm_LargeDependenceLowGrayLevelEmphasis_cor 0.881 0.934 logsigma50mm3D_firstorder_Maximum_cor 0.827 0.869 waveletLLL_firstorder_Kurtosis_cor 0.937 0.892 waveletHHH_glrlm_ShortRunEmphasis_cor 0.998 0.987 logsigma50mm3D_glcm_Idmn_cor 0.995 0.998
