## 3 Results  
Table 1 lists all features that were reproducible, with high correlation, for RIDER Scans 1 and 2 based on the [PERSON]'s correlation coefficient with threshold (blue) and without threshold (red) using the two quantizers GUQ and IUQ. Features with _rs_\(\geq\) 0.75 were classified as highly correlated. This was a subjective decision as no published guidance was available. A total of 34 features were found to be reproducible: 29 and 21 with and without intensity threshold applied, respectively, and features were found to be reproducible for all data. This suggests that including intensity threshold around the region of interest as a pre-processing step tends to increase the stability of some radiomics features.  
Figure 1 shows a Venn diagram plotting the overlap in highly correlated features quantized with GUQ versus IUQ for 43 features that are commonly available in the MATLAB toolkit and Pyradiomics. Panels A and B plot the correlation of rank ordering quantized between GUQ and IUQ without and with the use of an intensity threshold. The correlation value for most shape, first order, GLCM, GLRLM, GLZSM, and NGTDM texture features were high (>0.8) for the RIDER Scan 1, with 9 and 14 features with _rs_ > 0.9 without and with intensity threshold. The number of features that overlapped between the MATLAB toolkit and Pyradiomics was higher when the intensity threshold was applied. A similar trend was seen for RIDER scan 2 data.  
Figure 2 presents the results for features that were uniquely available in Pyradiomics. Figure 2A shows the correlation of rank order between features extracted with and without an intensity threshold with the use of GUQ and IUQ quantization. Similarly, Figure 2B shows the correlation of ranks quantized between GUQ and IUQ with and without intensity threshold being applied. Shape features were found to be invariant to intensity thresholds or quantization techniques. For all other classes, the features showed more variance based on the application of an intensity threshold and less so on the quantization used. A few features showed negative correlation with the choice of quantization used, namely GLCM Inverse Variance, GLDM Large Dependence Low Gray Level Variance, GLRLM Run Entropy, and GLZSM Size Zone Nonuniformity Normalized. This negative correlation was seen only in the GLCM Maximum Probability when an intensity threshold was applied.  
Figure 1: Venn diagrams with a visual representation of features that are reproducible between MATLAB and Pyradiomics feature extraction toolkits for GUQ versus IUQ with 128 quantizer levels (Fixed Bin Width in Pyradiomics). (**A**) without threshold; (**B**) with threshold applied. Reproducibility is measured by an _rs_ value greater than or equal to 0.8. Features highlighted in bold had _rs_ value greater than 0.9.  
Figure 3 plots a Venn diagram with the overlap in the highly correlated features quantized between GUQ and IUQ and with an intensity threshold applied for datasets RIDER Lung 1 and Head and Neck HN1.The features were extracted using Pyradiomics;  
Figure 2: The Spearman correlation ranks of features extracted using (**A**) threshold versus no threshold and comparing the impact of the quantization techniques (GUQ (bin count) vs. IUQ (bin width)) and (**B**) correlation between quantization methods (GUQ and IUQ) and comparing the impact of thresholding. Each color-paired block represents a separate feature class.  
18/43 features were agnostic of the disease site and 4/5 from HN1 and 8/11 from RIDER lung had rank correlations above 0.9, respectively.