## 3 Results
### Radiomics Features Analysis  
A total of 63 lesions out of 52 patients included in the study were selected. The analysis of the classification model has been divided into three parts, as explained in the \"_Radiomics features extraction and Machine-learning features classification\"_ section, for a total of 10 different radiomics models (Figure 1). In the first case, the prediction disease outcome for each lesion was analyzed (per-lesion analysis) considering the features extracted from PET and PET/CT images, respectively; then, the same analysis was developed of considering each patient (per-patient analysis) considering all the features extracted from the same images for a total of four different radiomics models. The results were as follow:  
* For lesion analysis, GLRLM-based feature gray-level non-uniformity (GLZLM_GLNU) was selected [15; 16] considering the only PET data set obtaining a Sensitivity 90.11%, Specificity 36.78%, Accuracy 66.72%, and AUROC 56.52% for the predictive DA classifier, while three features (GLZLM_ Zone Length Non-Uniformity--GLZLM_ ZLNU, and GLRLM_Short Run High Gray-Level Emphasis--GLRLM_SRHGE--between the CT features and GLZLM_GLNU between the PET features) were selected considering the PET/CT data set with Sensitivity 78.22%, Specificity 51.75%, Accuracy 66.63%, and AUROC 65.22%;
* For patient analysis, three features (GLZLM_ZLNU, GLZLM_High Gray-level Zone -GLZLM_HGZ-, Conventional Radial Intensity Mean Standardized Uptake Value body weight standard deviation squared -CONVENTIONAL_RIM_SUVbwstdev2-) were selected considering the PET-only data set with Sensitivity 32.07%, Specificity 92.11%, Accuracy 73.95% and AUROC 47.97%, and one feature (Conventional Hounsfield Unit Kurtosis -CONVENTIONAL_HUKurtosis-) was selected considering the PET/CT data set with Sensitivity 33.81%, Specificity 83.76%, Accuracy 68.70%, and AUROC 61%. Figure 2 shows the ROCs for the four implemented models, while Table 2 shows all obtained performances. Figure 1: Radiomics flow chart applied in this study. Figure 2: ROCs for the four radiomics models obtained per lesion (first row) and per-patient analyses (second row) for PET-only (first column) and PET/CT images (second column), with an AUROC of 56.52%, 65.22%, 47.97%, and 61%, respectively. In addition, the study was similarly repeated, focusing only on liver lesions, for a total of two different radiomics models, with the following results:  
* For lesion analysis, one PET feature (GLZLM_GLNU) with Sensitivity 70.15%, Specificity 23.48%, Accuracy 54.21%, and AUROC 39.94%, and three PET/CT features (GLZLM_ZLNU, and GLRLM_SRHGE between the CT features and GLZLM_GLNU between the PET features) with Sensitivity 64.39%, Specificity 76.71%, Accuracy 68.69%, and AUROC 55.26%;
* For patient analysis, three PET features (GLZLM_ZLNU, GLZLM_HGZ, CONVENTIONAL_RIM_SUVbwstdev2) with Sensitivity 44.42%, Specificity 84.37%, Accuracy 59.03%, and AUROC 60.11%, and one PET/CT feature (CONVENTIONAL_HUKurtosis) with Sensitivity 33.12%, Specificity 73.74%, Accuracy 47.88%, and AUROC 43.48%. Figure 3 shows the ROCs for the four implemented models, while Table 3 shows all obtained performances. Figure 3: Liver subset: ROCs for the four radiomics models obtained per lesion (first row) and per-patient analyses (second row) for PET (first column) and PET/CT images (second column), with an AUROC of 39.94%, 55.26%, 60.11%, and 43.48%, respectively. Finally, all lesions were again considered to assess the diagnostic performance of the features in discriminating liver metastasis:  
* For PET images, one feature (Discretized SUVbw minimum--DISCRETIZED_SUV bwmin-) was extracted with Sensitivity 73.78%, Specificity 83.02%, Accuracy 76.91%, and AUROC 88.91%;
* For PET/CT images, two features (Discretized histogram energy--DISCRETIZED_HISTO_Energy--between the CT features and DISCRETIZED_SUVbwmin between the PET features) were extracted with Sensitivity 89.46%, Specificity 93.63%, Accuracy 91.02%, and AUROC 95.33%.