Dynamic 18F-FDopa PET Imaging for Newly Diagnosed Gliomas: Is a Semiquantitative Model Sufficient?

Purpose: Dynamic amino-acid Positron Emission Tomography (PET) has become essential in neuro-oncology, most notably for its prognostic value to non-invasively predict isocitrate dehydrogenase (IDH) mutations in newly diagnosed gliomas. The 6-[18F]fluoro-L-DOPA (18F-FDOPA) kinetic model has an underlying complexity while previous studies have predominantly used a semi-quantitative dynamic analysis. Our study addresses whether a semi quantitative analysis can capture all the relevant information contained in time-activity curves for predicting the presence of IDH mutations, compared to the more sophisticated graphical and compartmental models. Methods: Thirty-seven tumour time-activity curves from 18F-FDOPA PET dynamic acquisitions of newly diagnosed gliomas (median age of 58.3 [20.3; 79.9] years, 16 women, 16 IDH-wildtype) were analysed with a semi quantitative model based on classical parameters, with (SQ) or without reference region (Ref SQ), or on parameters of a fit function (SQ Fit), a graphical Logan model with input function (Logan) or reference region (Ref Logan), and a two-tissue compartmental model previously reported for 18F-FDOPA PET imaging of gliomas (2TCM). The overall predictive performance of each model was assessed, by an area under the curve (AUC) comparison using a multivariate analysis of all parameters included in the model. Moreover, each extracted parameter was assessed in an univariate analysis by a receiver operating characteristic curve analysis. Results: The SQ model with an AUC of 0.733 for predicting IDH mutations showed comparable performances to other models with AUCs of 0.752, 0.814, 0.693, 0.786, 0.863, respectively corresponding to SQ Fit, Ref SQ, Logan, Ref Logan and 2 TCM (p≥0.10 for the pairwise comparisons with other models). In the univariate analysis, the SQ time-to-peak parameter had the best diagnostic performance (75.7% accuracy) compared to all other individual parameters considered. Conclusions: The SQ model circumvents the complexities of the 18F-FDOPA kinetic model and yields similar performances for predicting IDH mutations when compared to other models, most notably the compartmental model. Our study provides supportive evidence for the routine clinical application of the SQ model for the dynamic analysis of 18F-FDOPA PET images in newly diagnosed gliomas.