As an adjunct to magnetic resonance imaging (MRI), amino acid positron emission tomography (PET) imaging provides additional diagnostic and prognostic information in newly diagnosed gliomas (1). Amino acid PET is particularly helpful for gliomas exhibiting no contrast enhancement on MRI (2–4), which can, in some cases, conceal more aggressive gliomas (5). The most commonly used radiolabels, i.e., amino acids O-(2-[¹⁸F]fluoroethyl)-l-tyrosine (¹⁸F-FET) (6), ¹¹C-methionine (¹¹C-MET) (7), and 6-[¹⁸F]fluoro-l-DOPA (¹⁸F-FDOPA), were able to noninvasively characterize gliomas at the initial diagnosis (8). Dynamic acquisitions are started at the time of tracer injection, to follow the full kinetic path, and are most commonly performed with ¹⁸F-FET and ¹⁸F-FDOPA. Dynamic ¹¹C-MET acquisitions are rarely performed because of the limitations of the short (20 min) half-life of this radiolabeled amino acid (9).

Dynamic ¹⁸F-FET and ¹⁸F-FDOPA acquisitions have recently shown encouraging predictive performances for the noninvasive characterization of the mutation status of isocitrate dehydrogenase (IDH) in newly diagnosed gliomas (6, 8). This is considered to be one of the most important molecular parameters in gliomas according to the 2021 classification of the World Health Organization (WHO) (10). These results were obtained using a semiquantitative model, initially developed for dynamic ¹⁸F-FET PET imaging, that can be easily transposed to the clinic (11). The model relies on the extraction of two parameters directly from the time–activity curve (TAC) without requiring any modeling of the underlying metabolism. These parameters are the time-to-peak (TTP), which is the time from the beginning of the dynamic acquisition to the maximal TAC value, and the late phase slope of the TAC. This simple model is currently recommended for dynamic ¹⁸F-FET PET analyses (12) and has been reported to predict IDH mutation with an accuracy of 72% (6). The same dynamic analysis has also been successfully transposed to ¹⁸F-FDOPA dynamic images, with the time-to-peak parameter demonstrating a promising 74% accuracy for predicting the presence of IDH mutations in newly diagnosed gliomas (8).

The simple dynamic ¹⁸F-FET PET modeling approach can be applied because, unlike other radiolabeled amino acids, ¹⁸F-FET is not metabolized by cells (13). Indeed, several studies have shown that ¹⁸F-FET kinetics are best modeled using a one-tissue reversible compartmental model with reliable fit and stable kinetic parameters (14–16). The ¹⁸F-FDOPA kinetics in glioma, however, involve a much more complex model that is, to date, only partially characterized (17, 18) because the initial models were developed in the context of assessing Parkinson’s disease (19). The complexities of the ¹⁸F-FDOPA kinetic model arise from its different peripheral and intracellular metabolic pathways. ¹⁸F-FDOPA is metabolized into ¹⁸F-labeled metabolites (METS) and ¹⁸F-labeled l-3,4-dihydroxy-6-fluoro-3-O-methylphenylalanine (OMFD) in the periphery, with OMFD able to bidirectionally cross the blood–brain barrier. In addition, and unlike ¹⁸F-FET, ¹⁸F-FDOPA is metabolized via the dopaminergic pathway, even though it is unclear whether it is metabolized in the same way by tumor cells. A two-tissue compartmental model, only using one input function, has nevertheless been previously proven useful for the ¹⁸F-FDOPA imaging of gliomas (17, 18). However, to the best of our knowledge, there are currently no studies that compare the semiquantitative dynamic model to other dynamic models, such as the compartmental ¹⁸F-FDOPA PET imaging model in gliomas (17, 18).

Our current study assesses whether an ¹⁸F-FDOPA semiquantitative dynamic analysis indeed captures all the relevant information contained in time–activity curves to predict the presence of IDH mutations compared to the more sophisticated graphical and compartmental models.

The current study compared different ¹⁸F-FDOPA kinetic models to assess whether the SQ model, which is currently recommended for amino acid PET in clinical routine practice, is specifically suited to a ¹⁸F-FDOPA PET dynamic analysis (12). This comparison was performed on newly diagnosed gliomas for which the amino acid PET dynamic analysis had previously been shown to successfully predict the status of IDH mutation (6, 8, 24). The SQ model was found to have a similar performance to the other models, including the 2TCM, as previously reported for the dynamic ¹⁸F-FDOPA PET imaging of gliomas (17, 18) (Table 1 and Figure 2). Our study therefore provides supportive evidence for the application of the SQ model, to predict IDH mutations by ¹⁸F-FDOPA PET imaging in newly diagnosed gliomas, in the clinic.

The SQ model has the advantage of being easily integrated into clinical routine practice with no need for heavy processing, a reference region, or an input function. Although compartmental analysis, which is considered the current gold standard, requires an input function, input functions are notoriously difficult to obtain in clinical routine practice because they either require blood sampling or, if extracted directly from images, heavy post-processing and many approximations (see Figure 1), which limits their use in the clinic.

There has been renewed interest in dynamic PET acquisitions because of the significant technological advances in the field, such as digital signal (38) and/or the longer field of view of PET devices. These improvements make dynamic acquisitions accessible to clinical routine practice (39). A simple dynamic model is thus preferable to harmonize protocols across different centers and to promote a wider acceptance in the scientific medical community, not only among nuclear physicians but also clinicians requiring access to easily understandable and interpretable data.

Our study confirms the high diagnostic performance of dynamic ¹⁸F-FDOPA PET imaging using the SQ model to predict IDH mutations, with the TTP analysis yielding an accuracy of 75.7%. TACs with shorter TTPs may, in addition, identify more aggressive IDH-wild-type gliomas, which may correspond not only to tumors that express high concentrations of LAT transporters (40) but also those that are characterized by more extensive tracer perfusion, as shown for ¹⁸F-FET (11) and discussed in our previous publications (8, 24). Such performances are within the range of previously published studies that reported accuracies of 74% for TTP alone (8) and 75% when associated with other specific radiomics features of ¹⁸F-FDOPA PET imaging (24). It should nevertheless be noted that these latter two studies used a reference region for the SQ model to account for the population heterogeneity with regard to the Carbidopa pre-medication (8, 24, 34). In the current study, we report similar overall performances of the Ref SQ model and the SQ model (Ref SQ: AUC = 0.814, 95% CI = 0.671–0.956, p = 0.21, for the pairwise test with the SQ model). This is consistent with results from our previous dynamic ¹⁸F-FDOPA PET studies (8, 24, 34). Even though no significant differences were observed between the different models studied, the 2TCM provided better performance (AUC = 0.863, range = 0.734–0.993, p = 0.11, for the pairwise test with the SQ model) with parameters that reflect different kinetic information compared to those from the other models (Figure 3). However, as explained above, adapting the 2TCM to clinical routine practice presents serious challenges and would make it more difficult to harmonize data sourced from different centers.

Among the radiolabeled amino acids recommended for the assessment of gliomas, ¹⁸F-FET and ¹⁸F-FDOPA have been demonstrated to have similar semiquantitative static parameters (41) but different kinetic parameters for tumor grading using compartmental modeling (28). When applying the WHO 2016 classification of gliomas, dynamic parameters for both radiotracers extracted from the SQ or the Ref SQ model, in the literature, showed similar performances in predicting IDH mutations. An accuracy of 72% was reported for the ¹⁸F-FET TTP (6), while an accuracy of 74% was described for the ¹⁸F-FDOPA TTP_ratio (8). These results were also confirmed in the Lohmann et al. radiomics study, which reported a model accuracy of 80% (42). The study of Lohmann et al. nevertheless showed that dynamic parameters were particularly pertinent in determining diagnostic performance. Since the underlying kinetic model of ¹⁸F-FDOPA is much more complex than that of ¹⁸F-FET, there was no evidence that a simple model such as the SQ model would suffice. Our current study showed that the SQ model is appropriate for interpreting ¹⁸F-FDOPA kinetics and that it might be considered in future guidelines, as is currently the case for ¹⁸F-FET imaging (12).

The main limitations of our study are related to the assumptions that underpin how the input function was determined. Since no blood sampling was performed for any of our patients, we extrapolated the input function from an image-derived blood TAC that was converted to a ¹⁸F-FDOPA plasma TAC using a previously published method (18). This method is based on hematocrit data and the proportions of individual metabolites previously reported in the literature (19, 33). Since this assumption is not adapted to each patient, it may have a negative impact on the results from the model that are dependent on this input function. Moreover, our study used a digital PET device with high count sensitivity and improved dynamic image quality. The values of the different parameters may therefore not be directly comparable to those extracted from noisier dynamic images, captured with older PET devices. This may significantly affect the fitting process that is used for each dynamic model and, thus, affect the stability and the ability to directly extrapolate our results to other PET devices. The number of patients in this study was also limited, even though the diagnostic performances reported were similar to those of other amino acid dynamic PET studies. Although our population comprised a highly selected group of patients, it included representative proportions of the different types of gliomas (43).

Despite the complexities of the ¹⁸F-FDOPA kinetic model, our study confirms that a simple semiquantitative analysis, which is currently recommended for amino acid PET imaging in clinical routine practice, captures all the relevant information contained in TACs to predict the presence of IDH mutations when compared to the more sophisticated graphical and compartmental models. Although dynamic parameters play important roles in the interpretation of radiolabeled amino acid PET imaging in gliomas, our current study shows that the application of this easily transposable method can be extended to ¹⁸F-FDOPA PET imaging for the noninvasive characterization of newly diagnosed gliomas.

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

The studies involving human participants were reviewed and approved by Comité d’Ethique du CHRU de Nancy - FRANCE. The patients/participants provided their written informed consent to participate in this study.

TZ, MD, and AV conceived and designed the study and analyzed the data. FR, MB, LT, and LI participated either in the study conception and design or the analysis and interpretation of the data. All authors helped with the drafting of the manuscript or revising it critically for important intellectual content. FR, MB, LT, LI, and AV approved the submitted version. All authors contributed to the article and approved the submitted version.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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