## 4 Discussion  
To the best of our knowledge, the present study is one of the first to explore [18]FFDG PET/CT textural features analysis after first adjuvant therapy to potentially predict disease progression and survival outcome as an indirect predictive parameter of second-line therapy responses in metastatic colon cancer patients, using an innovative mixed descriptive-inferential sequential approach for features reduction and selection, and by using DA as a predictive model [17]. Radiomics literature in CRC is highly heterogeneous, but it holds promise for the prediction of outcomes. Most evidence is available for MRI-based radiomics in rectal cancer [23]. A few studies on textural features derived from [18]FFDG PET images at baseline for locally advanced colorectal cancer and before or after starting any neoadjuvant treatments may enable detailed stratification of prognosis in patients with CRC [24; 25; 26; 27]. Our study focused on the potential usefulness to extract PET radiomics features and also low-dose CT radiomic features for a \"hybrid\" textural PET/CT analysis mimicking the qualitative assessment in the clinical routine evaluation of PET/CT images. The scope of this design study was to confirm the feasibility of our ML methods and to analyze how all the information related to restaging PET/CT imaging after first-line treatments might be able to predict the disease status outcomes after further treatments. Radiomics features were extracted from each lesion (including all the sites of metastasis), divided into three feature subsets: 105 features from PET images, 66 features from CT, and 171 features from both PET and CT. Features were automatically extracted using the well-known IBSI compliant LifeX software to perform a totally objective and reproducible study. Prediction of outcome in patients with CRC is challenging because of the lack of a robust biomarker and heterogeneity between and within tumors to modulate treatment strategies. In this scenario, a study conducted on third-line treatment patients with metastatic colorectal cancer showed that high tumor heterogeneity, volume, and low sphericity on baseline [18]FDG PET were related to reduced survival [28]. Similarly, textural parameters as the coefficient of variation, kurtosis of the absolute gradient (GrKurtosis), and other features on [18]FDG PET images have been proposed in other papers as predictive and prognostic factors in the assessment of therapy response and survival outcomes in patients with rectal cancer [29; 30]. Our study results, differently from others studies for study design and ML models adopted, demonstrate the potential predictive value of radiomics features derived from an innovative machine learning model adapted by using the disease status at follow-up as the gold standard for the performances analysis. This approach was proposed, as in other studies conducted by our group [31; 32], to define the real value of PET/CT as a predictive tool for the stratification of patients with different diseases (prostate and primary brain tumors) that for specific characteristics are more susceptible to have a scarce sensitivity to therapies and a poor disease outcome. For this reason, the apparent sub-optimal results obtained in the present study need to be interpreted with caution because we are not presenting the performance on the identification of disease but the capability of some radiomics features to predict the disease status outcome of the patients with metastatic colon cancer after the standard first adjuvant therapy. Underlining the results on the PET radiomics analysis in the whole patient group, among per-lesion analysis, the feature selected as the most accurate for the DA classifier was GLZLM_GLNU, while three features (GLZLM_ZLNU and GLRLM_SRHGE between CT features and GLZLM_GLNU between PET features) were selected from PET/CT images obtaining slight enhancement of accuracy when CT analysis was merged with PET performances (AUROC 65.22%).