The purpose of this study was to evaluate the possibility of extracting relevant information from radiomic features even in apparently [18F]FET-negative gliomas. A total of 46 patients with a newly diagnosed, histologically verified glioma that was visually classified as [18F]FET-negative were included. Tumor volumes were defined using routine T2/FLAIR MRI data and applied to extract information from dynamic [18F]FET PET data, i.e., early and late tumor-to-background (TBR5-15, TBR20-40) and time-to-peak (TTP) images. Radiomic features of healthy background were calculated from the tumor volume of interest mirrored in the contralateral hemisphere. The ability to distinguish tumors from healthy tissue was assessed using the Wilcoxon test and logistic regression. A total of 5, 15, and 69% of features derived from TBR20-40, TBR5-15, and TTP images, respectively, were significantly different. A high number of significantly different TTP features was even found in isometabolic gliomas (after exclusion of photopenic gliomas) with visually normal [18F]FET uptake in static images. However, the differences did not reach satisfactory predictability for machine-learning-based identification of tumor tissue. In conclusion, radiomic features derived from dynamic [18F]FET PET data may extract additional information even in [18F]FET-negative gliomas, which should be investigated in larger cohorts and correlated with histological and outcome features in future studies.
Keywords: FET PET; FET negative; amino acid PET; glioma; photopenic; radiomics.