Table 2.
The number of Radiomics features of CT images extracted with Pyradiomics2.2.0.
| Filters | Category | Total | ||||||
|---|---|---|---|---|---|---|---|---|
| FOS | GLCM | GLRLM | GLSZM | NGTDM | GLDM | Shape# | ||
| Original image | 18 | 24 | 16 | 16 | 5 | 14 | 14 | 107 |
| Wavelet* | 144 | 192 | 128 | 128 | 40 | 112 | 0 | 744 |
| Square | 18 | 24 | 16 | 16 | 5 | 14 | 0 | 93 |
| Square Root | 18 | 24 | 16 | 16 | 5 | 14 | 0 | 93 |
| Logarithm | 18 | 24 | 16 | 16 | 5 | 14 | 0 | 93 |
| Exponential | 18 | 24 | 16 | 16 | 5 | 14 | 0 | 93 |
| Gradient | 18 | 24 | 16 | 16 | 5 | 14 | 0 | 93 |
| LocalBinaryPattern2D | 18 | 24 | 16 | 16 | 5 | 14 | 0 | 93 |
| LocalBinaryPattern3D* | 54 | 72 | 48 | 48 | 15 | 72 | 0 | 279 |
| Total | 324 | 432 | 288 | 288 | 90 | 252 | 14 | 1688 |
Note: #Category” Shape” features were only extracted from the original image, were not included in the group analysis because of the obvious difference between the study group and the control group;
*Filter “Wavelet” uses 8 levels of LLL, LLH, LHL, LHH, HLL, HHL, HLH, HHH to process images;
*Filter “LocalBinaryPattern3D” uses 3 levels of m1, m2, and k to process images.