## 4 Discussion
In per-patient analysis, 3 PET features (GLZLM_ZLNU, GLZLM_HGZ, RIM_SUV bwstdev2), and 1 PET/CT feature (HUKurtosis) were selected by DA classifier (AUROC 61%). Considering these first two analysis groups, three features belonging to the GLZLM class were identified as the most accurate for the DA classifier. The GLZLM, also called gray-level size-zone matrix (GLSZM), is the texture class that provides information on thesize of homogeneous zones for each gray-level. Consequently, it is indirectly linked to the heterogeneity of the lesions, which, reflecting biological characteristics, has a potential value in predicting the progression of the disease [9]. In regard to colorectal liver metastasis, the presence of metastasis in this site is widely considered as one of the unfavorable prognosis parameters. However, commonly employed SUV metrics (\(\text{SUV}_{\text{max}}\), \(\text{SUV}_{\text{peak}}\), \(\text{SUV}_{\text{mean}}\)) from [18]FFDG PET images perform relatively poorly in outcome prediction tasks (OS, PFS, EFS). In contrast, the use of liver metastasis number and volumetric measurements of MTV and TLG appears to be capable of providing significant performance [33]. Our radiomics model results, similarly, showed sub-optimal performances in the prediction of disease outcome by defining, at per-lesion analysis, one PET feature (GLZLM_GLNU with AUROC 39.94%) and three PET/CT features (GLZLM_ZLNU and GLRLM_SRHGE between CT features and GLZLM_GLNU between PET features with AUROC 55.26%). Similarly, in liver lesions per-patient analysis, we found 3 PET features (GLZLM_ZLNU, GLZLM_HGZ, RIM_SUVbwstdev2 with AUROC 60.11%) and one PET/CT feature (HUKurtosis with AUROC 43.48%). Furthermore, to quantify the influence of liver metastasis over all PET/CT findings, one only feature considering PET imaging (i.e., DISCRETIZED_SUVbwmin) and two features considering PET/CT imaging (DISCRETIZED_HISTO_Energy between the CT features and DISCRETIZED_SUVbwmin between the PET features) was able to discriminate liver metastasis from the rest of the other lesions (AUROC = 88.91% and 95.33%, respectively). These results, confirmed after further investigations, may be interpreted as crucial in the diagnostic and prognostic impact of liver lesions in patients affected by metastatic colorectal cancer. Potential limitations of the study must be considered. First, some intrinsic biases as the well-known sub-optimal accuracy of PET in some conditions due to FDG uptake variability, depending on the histology, size, location (particularly relevant for primary lesion in terms of prognosis: right vs. left colorectal cancer), pH, and possible overestimation of metabolic activity due to associate inflammation, could have affected the results. Furthermore, this is a retrospective single-center study, with a relatively small number of patients and a design study limited by data available. All patients who underwent [18]FDG PET/CT after the first adjuvant therapy were at different disease stages, treated with different chemotherapy combinations following Italian oncological guidelines (5 FU or oral capecitabine in combination with either oxaliplatin or irinotecan in various schedules) and a different number of cycles based on patients clinical conditions. All these variables might affect the patient's outcome. In addition, radiomics features were extracted only from the [18]FDG-positive tumor to construct the model, and the remaining normal tissue in the image may still contain invisible but useful data. To properly analyze the entire images, 3D deep learning methods will be necessary. Our study results could benefit from validation in a prospective multi-center study. Nevertheless, our preliminary experience suggests that PET texture analysis is feasible and could carefully be used as an independent indicator for the prognosis of patients with a high risk of disease progression and supporting clinicians for a more accurate selection of patients that may benefit from tailored therapies.