Table S1. Feature type and associated features.
| Feature type | Methods | Feature name |
|---|---|---|
| Shape-based | – | Maximum 3D diameter (M3D) |
| Maximum 2D Diameter Slice (M2DS) | ||
| Sphericity | ||
| Minor Axis (MA) | ||
| Elongation | ||
| Surface Volume Ratio (SVR) | ||
| Volume | ||
| Major Axis (MA1) | ||
| Surface Area (SA) | ||
| Flatness | ||
| Least Axis (LA) | ||
| Maximum 2D Diameter Column (M2DC) | ||
| Maximum 2D Diameter Row (M2DR) | ||
| First order-based | Histogram | Interquartile Range (IQR) |
| Skewness | ||
| Uniformity | ||
| Median | ||
| Energy | ||
| Robust Mean Absolute Deviation (RMAD) | ||
| Mean Absolute Deviation (MAD) | ||
| Total Energy (TE) | ||
| Maximum | ||
| Root Mean Squared (RMS) | ||
| 90 Percentile | ||
| Minimum | ||
| Entropy | ||
| Range | ||
| Variance | ||
| 10 Percentile | ||
| Kurtosis | ||
| Mean | ||
| Texture-based | GLCM | Joint Average (JA) |
| Sum Average (SA) | ||
| Joint Entropy (JE) | ||
| Cluster Shade (CS) | ||
| Maximum Probability (MP) | ||
| Idmn | ||
| Joint Energy (JE) | ||
| Contrast | ||
| Difference Entropy (DE) | ||
| Inverse Variance (IV) | ||
| Difference Variance (DV) | ||
| Idn | ||
| Idm | ||
| Correlation | ||
| Autocorrelation | ||
| Sum Entropy (SE) | ||
| Sum Squares (SS) | ||
| Cluster Prominence (CP) | ||
| Imc2 | ||
| Imc1 | ||
| Difference Average (DA) | ||
| Id | ||
| Cluster Tendency (CT) | ||
| GLSZM | Gray Level Variance (GLV) | |
| Zone Variance (ZV) | ||
| Gray Level Non-Uniformity Normalized (GLNUN) | ||
| Size Zone Non-Uniformity Normalized (SZNUN) | ||
| Size Zone Non-Uniformity (SZNU) | ||
| Gray Level Non-Uniformity (GLNU) | ||
| Large Area Emphasis (LAE) | ||
| Small Area High Gray Level Emphasis (SAHGLE) | ||
| Zone Percentage (ZP) | ||
| Large Area Low Gray Level Emphasis (LALGLE) | ||
| Large Area High Gray Level Emphasis (LAHGLE) | ||
| High Gray Level Zone Emphasis (HGLZE) | ||
| Small Area Emphasis (SAE) | ||
| Low Gray Level Zone Emphasis (LGLZE) | ||
| Zone Entropy (ZE) | ||
| Small Area Low Gray Level Emphasis (SALGLE) | ||
| GLRLM | Short Run Low Gray Level Emphasis (SRLGLE) | |
| Gray Level Variance (GLV) | ||
| Low Gray Level Run Emphasis (LGLRE) | ||
| Gray Level Non-Uniformity Normalized (GLNUN) | ||
| Run Variance (RV) | ||
| Gray Level Non-Uniformity (GLNU) | ||
| Long Run Emphasis (LRE) | ||
| Short Run High Gray Level Emphasis (SRHGLE) | ||
| Run Length Non-Uniformity (RLNU) | ||
| Short Run Emphasis (SRE) | ||
| Long Run High Gray Level Emphasis (LRHGLE) | ||
| Run Percentage (RP) | ||
| Long Run Low Gray Level Emphasis (LRLGLE) | ||
| Run Entropy (RE) | ||
| High Gray Level Run Emphasis (HGLRE) | ||
| Run Length Non-Uniformity Normalized (RLNUN) | ||
| NGTDM | Coarseness | |
| Complexity | ||
| Strength | ||
| Contrast | ||
| Busyness | ||
| GLDM | Gray Level Variance (GLV) | |
| High Gray Level Emphasis (HGLE) | ||
| Dependence Entropy (DE) | ||
| Dependence Non-Uniformity (DNU) | ||
| Gray Level Non-Uniformity (GLNU) | ||
| Small Dependence Emphasis (SDE) | ||
| Small Dependence High Gray Level Emphasis (SDHGLE) | ||
| Dependence Non-Uniformity Normalized (DNUN) | ||
| Large Dependence Emphasis (LDE) | ||
| Large Dependence Low Gray Level Emphasis (LDLGLE) | ||
| Dependence Variance (DV) | ||
| Large Dependence High Gray Level Emphasis (LDHGLE) | ||
| Small Dependence Low Gray Level Emphasis (SDLGLE) | ||
| Low Gray Level Emphasis (LGLE) | ||
| Wavelet-based | First-order statistic and texture of wavelet decomposition | First-order features |
| GLCM features | ||
| GLSZM features | ||
| Decomposition levels: LLL, LLH, LHL, LHH, HLL, HLH, HHL, HHH | GLRLM features | |
| NGTDM features | ||
| GLDM features |
GLCM, gray-level co-occurrence matrix, describe the second-order joint probability function of the voxel intensities within the contoured volume; GLSZM, gray-level size-zone matrix, quantify the number of connected voxels within the contoured volume that share the same gray level intensity; GLRLM, gray-level run-Length matrix, quantify the number of consecutive voxels that have the same gray level value; NGTDM, neighboring gray-tone difference matrix, quantify the difference between a gray value and the average gray value of its neighbors within 3×3×3 voxels neighborhood window; GLDM, gray-level dependence matrix, quantify the gray level dependencies in the contoured volume which is defined as the number of connected voxels within a specific distance that are dependent on the center voxel; Decomposition levels, i.e., LLH interpreted as the high-pass sub band, resulting from directional filtering of the volume with a low-pass filter along x-direction, a low pass filter along y-direction and a high-pass filter along z-direction