Tumor segmentation and extraction of radiomics features
tumor
TUMOR, RENAL TUMORS
Figure 
Schematic diagram of a radiomics study of renal tumors
Manual three-dimensional (3D) of the tumor. a, b and c are the unenhanced phase (UP), the corticomedullary phase (CMP) and the nephrographic phase (NP), respectively. d is 3D volumetric reconstructionThe extraction of features was performed using the Artificial Intelligence Kit software (A.K. Software, version 3.3.0.R). To minimize variability in the radiomics features, prior to extraction, the following image preprocessing techniques were applied: gray-level discretization, intensity normalization and voxel resampling. Subsequently, 1595 features were extracted from the UP, CMP and NP CT images by the open-source PyRadiomics library, respectively.The intraclass correlation coefficient (ICC) was calculated to evaluate the consistency and reproducibility of the features. Features with ICC greater than 0.75 in both intra- and inter-observer agreement analyses were included in further analysis. Reader 1 and Reader 2 randomly segmented CT images of 20 patients (8 high-grade small ccRCC and 12 low-grade small ccRCC). Two weeks later, Reader 1 segmented these 20 patients once again.