Purpose: To assess the role of SUVs, MTV, TLG and other FDG PET metric data in predicting the prognosis of patients with newly diagnosed BC. Materials and methods: A systematic review was conducted by using three different databases: PubMed, Web of Science and EMBASE, in a period between January 2011 and May 2021. Studies on the use of FDG PET in BC patients, concerning the utility of metric PET data and the survival were retrieved. The following keywords were used in diverse combinations: “breast cancer”, “18F-FDG”, “FDG”, “PET”, “PET/CT”, “FDG PET”, “volumetric parameters”, “metabolic tumor volume”, “MTV”, “total lesion glycolysis”, “TLG”, “prognosis”, “prognostic”. No limits were applied. The quality of selected papers was assessed by using specific criteria. Results: Totally 123 articles were retrieved, but only 14 studies were selected. In the selected papers, overall, the number of patients was 1850. Overall survival (OS) was the main outcome in three studies, while both OS and disease-free survival (DFS) were considered in the remainder of most papers. PET/CT was performed in patients with BC, before surgery or neoadjuvant chemotherapy in 6 studies and in metastatic BC in 8. At multivariable analyses, diverse PET metrics, such as SUVmax, MTV and TLG were correlated to recurrence or OS. However, a large heterogeneity for the proposal cut-off, able to discriminate between poor and good prognosis, was found. Conclusion: PET metrics are helpful for the prognosis stratification in patients with locally advanced or metastatic BC. However, no specific cut-off values for these variables are now available in this setting of patients.

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.

Background: 18F-FDG PET/CT imaging represents the most important functional imaging method in oncology. European Society of Medical Oncology and the National Comprehensive Cancer Network guidelines defined a crucial role of 18F-FDG PET/CT imaging for local/locally advanced breast cancer. The application of artificial intelligence on PET images might potentially contributes in the field of precision medicine. Objective: This review aims to summarize the clinical indications and limitations of PET imaging for comprehensive artificial intelligence in relation to breast cancer subtype, hormone receptor status, proliferation rate, and lymphonodal (LN)/distant metastatic spread, based on recent literature. Methods: A literature search of the Pubmed/Scopus/Google Scholar/Cochrane/EMBASE databases was carried out, searching for articles on the use of artificial intelligence and PET in breast tumors. The search was updated from January 2010 to October 2021 and was limited to original articles published in English and about humans. A combination of the search terms "artificial intelligence", “breast cancer”, “breast tumor”, “PET”, “Positron emission tomography”, “PET/CT”, “PET/MRI”, “radiomic”,"texture analysis", “machine learning”, “deep learning” was used. Results: Twenty-three articles were selected following the PRISMA criteria from 139 records obtained from the Pubmed/Scopus/Google Scholar/Cochrane/EMBASE databases according to our research strategy. The QUADAS of 30 full-text articles assessed reported seven articles that were excluded for not being relevant to population and outcomes and/or for lower level of evidence. The majority of papers were at low risk of bias and applicability. The articles were divided per topic, such as the value of PET in the staging and re-staging of breast cancer patients, including new radiopharmaceuticals and simultaneous PET/MRI. Conclusion: Despite the current role of AI in this field remains still undefined, several applications for PET/CT imaging are under development, with some preliminary interesting results particularly focused on the staging phase that might be clinically translated after further validation studies.

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