[18F]FDG PET-Based Radiomics and Machine Learning for the Assessment of Gliomas and Glioblastomas: A Systematic Review - Francesco Dondi; Roberto Gatta; Maria Gazzilli; Pietro Bellini; Gian Luca Viganò; Cristina Ferrari; Antonio Rosario Pisani; Giuseppe Rubini; Francesco Bertagna (Information)
Background: Some evidence of the value of 18F-fluorodesoxyglucose ([18F]FDG) positron emission tomography (PET) imaging for the assessment of gliomas and glioblastomas (GBMs) is emerging. The aim of this systematic review was to assess the role of [18F]FDG PET-based radiomics and machine learning (ML) in the evaluation of these neoplasms. Methods: A wide literature search of the PubMed/MEDLINE, Scopus, and Cochrane Library databases was made to find relevant published articles on the role of [18F]FDG PET-based radiomics and ML for the assessment of gliomas and GBMs. Results: Eight studies were included in the systematic review. Signatures, including radiomics analysis and ML, generally demonstrated a possible diagnostic value to assess different characteristics of gliomas and GBMs, such as the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter, the isocitrate dehydrogenase (IDH) genotype, alpha thalassemia/mental retardation X-linked (ATRX) mutation status, proliferative activity, differential diagnosis with solitary brain metastases or primary central nervous system lymphoma, and prognosis of these patients. Conclusion: Despite some intrinsic limitations of radiomics and ML affecting the studies included in the review, some initial insights on the promising role of these technologies for the assessment of gliomas and GBMs are emerging. Validation of these preliminary findings in multicentric studies is needed to translate radiomics and ML approaches in the clinical setting.