Table 2.
Selected radiomics features.
| Model | No | Radiomics feature | Radiomics class | Filter |
|---|---|---|---|---|
| Method 1 | 1 | Skewness | First order | Wavelet-HLL∗ |
| 2 | Maximum | First order | Wavelet-HLL∗ | |
| 3 | High gray level zone emphasis | GLSZM | Wavelet-HLH∗ | |
| 4 | Gray level nonuniformity | GLSZM | Wavelet-LHL∗ | |
| Method 2 | 1 | Skewness | First order | Wavelet-HLL∗ |
| 2 | High gray level zone emphasis | GLSZM | Wavelet-LHL∗ | |
| 3 | Skewness | First order | Wavelet-LHL∗ | |
| 4 | High gray level run emphasis | GLRLM | Original | |
| 5 | High gray level run emphasis | GLRLM | Logarithm | |
| 6 | High gray level run emphasis | GLRLM | Square root | |
| 7 | High gray level run emphasis | GLRLM | Wavelet-LLL∗ |
GLSZM: gray level size zone matrix; GLRLM: gray level run length matrix. ∗The wavelet transform decomposes the tumor area image into low-frequency components (L) or high-frequency components (H) in the x, y, and z axes. Method 1: minimum delineation method; Method 2: maximum delineation method.