The aim of this study was to investigate the application of [¹⁸F]FDG PET/CT images-based textural features analysis to propose radiomics models able to early predict disease progression (PD) and survival outcome in metastatic colorectal cancer (MCC) patients after first adjuvant therapy. For this purpose, 52 MCC patients who underwent [¹⁸F]FDGPET/CT during the disease restaging process after the first adjuvant therapy were analyzed. Follow-up data were recorded for a minimum of 12 months after PET/CT. Radiomics features from each avid lesion in PET and low-dose CT images were extracted. A hybrid descriptive-inferential method and the discriminant analysis (DA) were used for feature selection and for predictive model implementation, respectively. The performance of the features in predicting PD was performed for per-lesion analysis, per-patient analysis, and liver lesions analysis. All lesions were again considered to assess the diagnostic performance of the features in discriminating liver lesions. In predicting PD in the whole group of patients, on PET features radiomics analysis, among per-lesion analysis, only the GLZLM_GLNU feature was selected, while three features were selected from PET/CT images data set. The same features resulted more accurately by associating CT features with PET features (AUROC 65.22%). In per-patient analysis, three features for stand-alone PET images and one feature (i.e., HUKurtosis) for the PET/CT data set were selected. Focusing on liver metastasis, in per-lesion analysis, the same analysis recognized one PET feature (GLZLM_GLNU) from PET images and three features from PET/CT data set. Similarly, in liver lesions per-patient analysis, we found three PET features and a PET/CT feature (HUKurtosis). In discrimination of liver metastasis from the rest of the other lesions, optimal results of stand-alone PET imaging were found for one feature (SUVbwmin; AUROC 88.91%) and two features for merged PET/CT features analysis (AUROC 95.33%). In conclusion, our machine learning model on restaging [¹⁸F]FDGPET/CT was demonstrated to be feasible and potentially useful in the predictive evaluation of disease progression in MCC.

Objective: This review aims to provide a summary of the clinical indications and limitations of PET imaging with different radiotracers, including 18F-fluorodeoxyglucose (18F-FDG) and other radiopharmaceuticals, in pediatric neuro-oncology, discussing both supratentorial and infratentorial tumors, based on recent literature (from 2010 to present). Methods: A literature search of the PubMed/MEDLINE database was carried out searching for articles on the use of PET in pediatric brain tumors. The search was updated until December 2020 and limited to original studies published in English after 1 January 2010. Results: 18F-FDG PET continues to be successfully employed in different settings in pediatric neuro-oncology, including diagnosis, grading and delineation of the target for stereotactic biopsy, estimation of prognosis, evaluation of recurrence, treatment planning and assessment of treatment response. Nevertheless, non-18F-FDG tracers, especially amino acid analogues seem to show a better performance in each clinical setting. Conclusions: PET imaging adds important information in the diagnostic work-up of pediatric brain tumors. International or national multicentric studies are encouraged in order to collect larger amount of data.