<figure><img src="image_56.png" /><figcaption>**Fig. 1** ROC curves illustrating the sensitivity and specificity of TTP and slope in predicting IDH mutation status. Optimal thresholds and its associated accuracies are indicated</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>**Fig. 2** ROC curves illustrating the sensitivity and specificity of TTP in predicting 1p/19q co-deletion status. Optimal threshold and its associated accuracy are indicated</figcaption></figure>

significant univariate PET predictor of this 3-group comparison discriminating IDH– gliomas from IDH+/1p19q– gliomas ($p = 0.007$, AUC = 0.766, cut-off 5.1 min, accuracy 74%, sensitivity 76%, specificity 71%) as well as IDH+/1p19q+ gliomas ($p = 0.003$, AUC = 0.818, cut-off 6.2 min, accuracy 78%, sensitivity 75%, specificity 81%).

## Discussion

The present study shows that the prediction of the molecular features according to the WHO 2016 classification of newly diagnosed gliomas with ¹⁸F-FDopa PET imaging and especially the assessment of IDH mutation is achieved owing exclusively to dynamic parameters, and especially TTP. Dynamic ¹⁸F-FDopa PET parameters were able to discriminate IDH wildtype gliomas from the IDH mutant gliomas associated or not with a 1p/19q co-deletion, underlying the more general information that IDH mutation can be predicted non-invasively to a certain level by ¹⁸F-FDopa PET. The dynamic PET TTP parameter was additionally found to be a predictor of the 1p/19q co-deletion status, although at a lower level of significance.

To date, the characterization of gliomas with dynamic analyses of ¹⁸F-FDopa PET has been reported in only a few studies, which were furthermore performed with compartmental models and involved limited numbers of patients (16 to 37), all of which had inconclusive or poorly conclusive results [16–18]. These previous studies were moreover based on the 2007 WHO classification of gliomas [32], whereas the WHO 2016 classification, including molecular parameters, provides a much more accurate characterization with regard to prognostic and therapeutic outcomes [19]. This recent upgrade in the WHO classification is mainly related to the introduction of molecular parameters of high prognostic and therapeutic significance, especially the presence or absence of IDH mutation and 1p/19q co-deletion [19]. In addition, the use of dynamic analysis with ¹⁸F-FDopa PET is not yet proposed in the recent EANM guidelines for brain tumor imaging [28].

The present study proposes a simple approach for the dynamic analysis of ¹⁸F-FDopa PET, easily transposable to clinical practice and comparable with that previously validated in a similar setting for ¹⁸F-FET, another amino acid PET tracer [14, 15].

As a result, the use of the dynamic TTP parameter enabled a non-invasive prediction of the IDH mutation status independently of the presence or absence of an additional 1p/19q co-deletion. The IDH wildtype gliomas exhibited a particularly short TTP, a feature currently considered to characterize those gliomas associated with a particularly poor outcome [33]. From a clinical standpoint, differentiating the group of IDH wildtype gliomas from IDH mutant gliomas with a non-invasive method is particularly useful, thereby favoring the
a. IDH-mutant astrocytoma

<figure><img src="image_58.png" /></figure>

b. IDH-wildtype astrocytoma

<figure><img src="image_59.png" /></figure>

c. Anaplastic IDH-mutant and 1p/19q co-deleted oligodendroglioma

<figure><img src="image_60.png" /></figure>

d. IDH-wildtype glioblastoma

<figure><img src="image_61.png" /></figure>
<table><caption>**Table 3** Median (interquartile range) of all considered static or dynamic PET parameters in each of the three glioma groups with the results of intergroup comparisons</caption><thead><tr><th>Parameter</th><th>IDH+/1p19q<sup>-</sup> (n = 21)</th><th>IDH+/1p19q+ (n = 16)</th><th>IDH-<br>(n = 21)</th><th>p value</th></tr></thead><tbody><tr><td>TSR<sub>mean</sub></td><td>1.04 (0.88, 1.29)</td><td>1.30 (0.95, 1.55)</td><td>1.16 (0.88, 1.49)</td><td>0.687</td></tr><tr><td>TSR<sub>max</sub></td><td>1.91 (1.30,2.42)</td><td>2.16 (1.45, 3.06)</td><td>2.38 (1.33, 3.32)</td><td>0.766</td></tr><tr><td>TBR<sub>mean</sub></td><td>1.35 (1.24, 1.73)</td><td>1.58 (1.24, 2.01)</td><td>1.63 (1.23, 2.23)</td><td>0.832</td></tr><tr><td>TBR<sub>max</sub></td><td>2.54 (1.66, 3.39)</td><td>2.94 (1.88, 4.08)</td><td>3.33 (1.73, 4.81)</td><td>0.742</td></tr><tr><td>MTV (mL)</td><td>16.60 (2.34, 37.24)</td><td>25.97 (12.98, 42.40)</td><td>17.88 (2.58; 62.23)</td><td>0.642</td></tr><tr><td>TTP (min)</td><td>7.87 (4.81;30.00)<sup>†</sup></td><td>10.85 (5.09, 30)<sup>†</sup></td><td>4.09 (2.64, 5.69)</td><td>0.001*</td></tr><tr><td>Slope (h<sup>-1</sup>)</td><td>-0.12 (-0.85, 0.13)</td><td>-0.29 (-1.27, 0.29)</td><td>-0.65 (-2.20, -0.14)</td><td>0.05</td></tr></tbody></table>

IDH+/1p19q-, IDH mutant without 1p/19q co-deletion gliomas; IDH+/1p19+, IDH mutant with 1p/19q co-deletion gliomas; IDH-, IDH wildtype gliomas; MTV, metabolic tumor volume; TBR, tumor-to-brain ratio; TSR, tumor-to-striatum ratio; TTP, time-to-peak

identification of patients for whom initiating treatment cannot be delayed [34]. It is even more important when dealing with IDH wildtype diffuse gliomas where searching for MRI contrast enhancement is unfortunately weakly contributive (only one patient with IDH wildtype astrocytoma exhibited contrast enhancement on MRI in the present study (see Table 1)). On the other hand, predicting the IDH mutant gliomas conferring a better prognosis [33] could benefit elderly patients presenting high rates of clinical deficit at initial diagnosis and for whom biopsy or surgery is particularly at risk [35].

Moreover, our results suggest that TTP may also provide a certain degree of prediction of the 1p/19q co-deletion status. This prediction may be obtained with the criterion of a particularly long TTP, of at least 6.9 min, in contrast to the most aggressive IDH wildtype entities. This additional observation is also of particular importance since gliomas with both IDH mutation and a 1p/19q co-deletion are currently characterized

by high amino acid uptakes, notably with ¹⁸F-FET and ¹¹C-methionine PET, which may lead to a difficult differentiation from glioblastoma in static analyses [36–38].

In addition, one PET study performed with another amino acid, ¹¹C-methionine, had proposed to discriminate the oligodendroglial component of gliomas through the criterion of a lower slope at the late phase [39]. These results are clearly different from those of the present ¹⁸F-FDopa study, confirming that the kinetics information provided by the different amino acid radiotracers is not comparable in this clinical setting [40].

The mechanisms leading to the tumor uptake of amino acid radiotracers have mostly been analyzed for ¹⁸F-FET and still remain to be fully understood, particularly for ¹⁸F-FDopa. While it is generally considered that the uptake of ¹⁸F-FDopa is carried out with the L-system amino acid transporters and in particular, with the LAT1 transporters which are over-expressed in gliomas, the intensity of ¹⁸F-FDopa uptake is nonetheless not correlated with the level of LAT1 expression [41]. Indeed, the tumor uptake of amino acid tracers is also correlated with cell proliferation and microvessel density, with a disruption of the blood brain barrier likely facilitating the initial tumor uptake of the tracers, as well as their subsequent passive back diffusion [42]. It must nevertheless be pointed out that the faster kinetics observed herein for certain tumors is not solely explained by the breakdown of the blood brain barrier. This is particularly the case for our eleven IDH wildtype astrocytomas for which the kinetics were in most of cases particularly fast, with short TTP and negative slope, whereas the MRI contrast enhancement, which accompanies the breakdown of the blood brain barrier, was observed in only one of these IDH wildtype astrocytomas (Table 1).

In high-grade tumors, the breakdown of the blood brain barrier, but also microvessel density and LAT1 expression, are particularly manifest [42], thereby explaining our observations of a faster uptake and a faster washout of ¹⁸F-FDopa within the IDH wildtype gliomas. This consideration is

► **Fig. 3** Representative examples of metabolic tumor volume delineation for each glioma group, performed on an axial slice of ¹⁸F-FDopa PET (left column) with the dynamic TBR<sub>mean</sub> curve (middle column) providing the time-to-peak delay-time (light blue line) and the 10 to 30 min slope (dark blue line), along with, for illustration purposes, the same slice location recorded on contrast-enhanced T1-weighted MRI (right column). **a** 27-year-old woman with an IDH mutant astrocytoma with contrast enhancement on T1-weighted gadolinium-injected MRI (TBR<sub>mean</sub> of 1.4, TBR<sub>max</sub> of 2.7, TSR<sub>mean</sub> of 1, TSR<sub>max</sub> of 2, MTV of 16.6 ml, TTP of 30 min, and slope of 0.21 h<sup>-1</sup>). **b** 79-year-old woman with an IDH wildtype astrocytoma without contrast enhancement on T1-weighted gadolinium-injected MRI (TBR<sub>mean</sub> of 1.3, TBR<sub>max</sub> of 1.6, TSR<sub>mean</sub> of 1, TSR<sub>max</sub> of 1.2, MTV of 12.3 mL, TTP of 4.9 min, and slope of -0.07 h<sup>-1</sup>). **c** 57-year-old woman with an anaplastic IDH mutant and 1p/19q co-deleted oligodendroglioma without contrast enhancement on T1-weighted gadolinium-injected MRI (TBR<sub>mean</sub> of 1.7, TBR<sub>max</sub> of 2.8, TSR<sub>mean</sub> of 1.3, TSR<sub>max</sub> of 2.2, MTV of 27.5 mL, TTP of 30 min, and slope of 0.86 h<sup>-1</sup>). **d** 22-year-old woman with an IDH wildtype glioblastoma with contrast enhancement on T1-weighted gadolinium-injected MRI (TBR<sub>mean</sub> of 2.7, TBR<sub>max</sub> of 5.3, TSR<sub>mean</sub> of 2.2, TSR<sub>max</sub> of 4.2, MTV of 73 ml, TTP of 4.1 min, and slope of -10.34 h<sup>-1</sup>)

<footnote>*p < 0.05 for intergroup comparison between 3 groups</footnote>

<footnote>†p < 0.05 for comparison with IDH- gliomas</footnote>
supported by previous kinetic modeling studies where high-
grade tumors, representing the most aggressive entities, were
found exhibiting the highest transport rate constant [16].

Up to now, no previous ¹⁸F-FDopa PET imaging study had been conducted to discriminate the molecular features of gliomas according to the WHO 2016 classification in a population including both diffuse gliomas and glioblastomas and using dynamic parameters. A few studies [24–26] had already attempted to characterize these molecular features with static parameters and in populations including (i) both diffuse gliomas and glioblastomas but with inconclusive results with regard to the discrimination of IDH mutation or 1p/19q co-deletion [24] and (ii) only diffuse gliomas and with results showing a significantly higher ¹⁸F-FDopa uptake in IDH mutant gliomas comparatively with the IDH wildtype gliomas [25, 26]. These findings are in accordance with those from the present study in which no static parameter was able to discriminate the molecular features of gliomas in our admixed population of diffuse gliomas and glioblastomas. In addition, we also found that the IDH wildtype diffuse gliomas (IDH wildtype astrocytomas) showed a significantly lower MTV than IDH mutant diffuse gliomas (IDH mutant astrocytomas and IDH mutant and 1p/19q co-deleted oligodendrogliomas) (see additional results in Supplemental Fig. 2). It should also be pointed out that glioblastomas (grade IV gliomas) were able to be discriminated herein from all other gliomas with the dual criterion of a high MTV and short TTP (p < 0.01, accuracy of 95%, see Supplemental Fig. 2).

Our retrospectively selected study population represents a particular and highly selected group, even if the proportions of the different glioma types fit well with data from epidemiological studies [43]. Nevertheless, extrapolation to more general populations should be carried out with caution, particularly for the diagnostic thresholds and performances documented herein for the prediction of each glioma type with ¹⁸F-FDopa PET. It should additionally be pointed out that the TAC ratios used in the present study likely allow overcoming a possible interference of Carbidopa premedication taken by certain patients [27], thereby rendering our results as independent as possible of this premedication. Notwithstanding, close results were documented when SUV values were used instead of TBR for TAC in the present study, with dynamic parameters being the sole predictors, namely (1) TTP and slope for IDH mutation and (2) slope for 1p/19q co-deletion (results not shown). Finally, it should also be kept in mind that dynamic analyses could only be obtained with a dual analysis of MRI images in instances of a very low tumor uptake of ¹⁸F-FDopa PET. This was the case of 17% of our patients, a proportion comparable with those previously documented in other amino acid PET studies [44, 45].

Altogether, the present study highlights the significant
added value of including dynamic parameters for the routine
analysis of the ¹⁸F-FDopa PET imaging of gliomas. The latter

is indeed associated with a marked improvement in the char-
acterization of molecular features of newly diagnosed gliomas
according to the WHO 2016 classification, especially for the
non-invasive identification of IDH mutation status. These
findings need to be further confirmed in larger scale prospec-
tive trials involving additional clinical and MRI variables of
interest, in order to improve the non-invasive prediction of
glioma types as well as to optimize patient management.
Further clinical questions involving ¹⁸F-FDopa PET imaging
in gliomas such as recurrence or treatment monitoring could
also be assessed by integrating these dynamic parameters.

**Acknowledgments** The authors thank Pierre Pothier for his critical review of the manuscript.

Compliance with ethical standards

**Conflict of interest** The authors declare that they have no conflict of interest.

**Ethical approval** All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

**Informed consent** Informed written consent was obtained from all in-
dividual participants included in the study.

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ORIGINAL RESEARCH

Open Access

# Use of static and dynamic [¹⁸F]-F-DOPA PET parameters for detecting patients with glioma recurrence or progression

<figure><img src="image_62.png" /></figure>

Check for
updates

Timothée Zaragori<fnref n="1" />,<fnref n="2" />†, Merwan Ginet<fnref n="1" />†, Pierre-Yves Marie<fnref n="1" />,³, Véronique Roch<fnref n="1" />, Rachel Grignon<fnref n="1" />, Guillaume Gauchotte⁴,⁵, Fabien Rech⁶,⁷, Marie Blonski⁷,⁸, Zohra Lamiral³, Luc Taillandier⁷,⁸, Laëtitia Imbert<fnref n="1" />,<fnref n="2" /> and Antoine Verger<fnref n="1" />,<fnref n="2" />* ID

## Abstract

**Background:** Static [¹⁸F]-F-DOPA PET images are currently used for identifying patients with glioma recurrence/ progression after treatment, although the additional diagnostic value of dynamic parameters remains unknown in this setting. The aim of this study was to evaluate the performances of static and dynamic [¹⁸F]-F-DOPA PET parameters for detecting patients with glioma recurrence/progression as well as assess further relationships with patient outcome.

**Methods:** Fifty-one consecutive patients who underwent an [¹⁸F]-F-DOPA PET for a suspected glioma recurrence/ progression at post-resection MRI, were retrospectively included. Static parameters, including mean and maximum tumor-to-normal-brain (TBR) ratios, tumor-to-striatum (TSR) ratios, and metabolic tumor volume (MTV), as well as dynamic parameters with time-to-peak (TTP) values and curve slope, were tested for predicting the following: (1) glioma recurrence/progression at 6 months after the PET exam and (2) survival on longer follow-up.

**Results:** All static parameters were significant predictors of glioma recurrence/progression (accuracy ≥ 94%) with all parameters also associated with mean progression-free survival (PFS) in the overall population (all *p* < 0.001, 29.7 vs. 0.4 months for TBRmax, TSRmax, and MTV). The curve slope was the sole dynamic PET predictor of glioma recurrence/ progression (accuracy = 76.5%) and was also associated with mean PFS (*p* < 0.001, 18.0 vs. 0.4 months). However, no additional information was provided relative to static parameters in multivariate analysis.

**Conclusion:** Although patients with glioma recurrence/progression can be detected by both static and dynamic [¹⁸F]-F-DOPA PET parameters, most of this diagnostic information can be achieved by conventional static parameters.

**Keywords:** [¹⁸F]-F-DOPA, Glioma, Recurrence, Dynamic analysis, Amino-acid PET

<figure><img src="image_63.png" /></figure>

<footnote>* Correspondence: a.verger@chru-nancy.fr</footnote>

<footnote>†Timothée Zaragori and Merwan Ginet contributed equally to this work.</footnote>

<footnote n="1">¹Department of Nuclear Medicine & Nancyyclotep Imaging platform, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France</footnote>

<footnote n="2">²IADI, INSERM, UMR 1254, Université de Lorraine, F-54000 Nancy, France</footnote>

<footnote>Full list of author information is available at the end of the article</footnote>
## Background

Gliomas represent approximately 80% of malignant tumors of the central nervous system (CNS) [1]. These tumors have a poor prognosis with a median overall survival of 15 months for glioblastomas, the most common of glioma entities. This particular poor prognosis partly results from a high risk of recurrence/progression with a median progression-free survival (PFS) of only 8 to 11 weeks for high-grade gliomas [2]. Even though magnetic resonance imaging (MRI) remains the gold standard for imaging these tumors, MRI is nonetheless dependent on the disruption of the blood-brain barrier and may hence be limited with regard to the differential diagnosis between residual tumors and post-therapeutic changes in tumors with suspected recurrence [3].

Amino-acid PET has been proposed as a criterion for detecting glioma recurrence by the Response Assessment in Neuro-Oncology (RANO) group [3] and has become a current indication for differentiating glioma recurrence from treatment-induced changes, as stated in the recent joint European Association of Nuclear Medicine (EANM)/European Association of Neuro-Oncology (EANO)/RANO practice guidelines [4].

[¹⁸F]-F-DOPA is an amino acid PET tracer for which the ability to diagnose glioma recurrence has been previously established [5-7]. In particular, [¹⁸F]-F-DOPA PET was found to detect glioma recurrence with an accuracy as high as 82% in a retrospective study of 110 patients, in which the lesion-to-normal-tissue ratio was additionally predictive of PFS [8]. More recently, in a study in which glioma recurrence was proven histologically, [¹⁸F]-F-DOPA PET accuracy was reported to reach 84%, using maximum [¹⁸F]-F-DOPA uptake as diagnostic parameter [9]. This accuracy for detecting glioma recurrence was even higher, reaching 96%, in a prospective, albeit smaller study of 28 patients [10].

However, all of the aforementioned [¹⁸F]-F-DOPA PET studies [5-10] were conducted while solely taking into account static [¹⁸F]-F-DOPA PET parameters, whereas information provided by dynamic parameters was not considered. It should be noted that such dynamic parameter-derived information has previously been studied in the assessment of glioma recurrence/progression with another widely used amino-acid radiotracer, O-(2-[¹⁸F]fluoroethyl)-L-tyrosine ([¹⁸F]-FET) [11-14], [¹⁸F]-FET, and [¹⁸F]-F-DOPA being known to present relatively equal performances [15]. Dynamic [¹⁸F]-F-DOPA parameters have also been used to distinguish high grade from low-grade recurrent gliomas with regard to the World Health Organization (WHO) 2007 classification [16]. Moreover, such parameters were recently found to allow characterizing the molecular features of gliomas according to the WHO 2016 classification [17]. However, it remains unknown whether dynamic parameters may also enhance

the diagnostic accuracy of [¹⁸F]-F-DOPA PET prescribed for detecting glioma recurrence/progression.

The aim of the present study was thus to assess the performances of [¹⁸F]-F-DOPA PET with both static and dynamic parameters in detecting patients with progressive or recurrent glioma and for assessing further relationships with patient outcome.

## Materials and methods

### Patients

From October 2012 to October 2017, 245 patients referred to the Department of Nuclear Medicine at the CHRU Nancy for brain tumor assessment were investigated by [¹⁸F]-F-DOPA PET. Fifty-one of these patients were retrospectively selected on the basis that they had an initial history of surgically resected glioma and that the considered [¹⁸F]-F-DOPA PET had been prescribed for differentiating recurrence/tumor progression from post-therapeutic effects after a non-contributive MRI. As usual in our department, a minimum delay of 2 months is always respected between the surgery or end of radiotherapy and the performing of [¹⁸F]-F-DOPA PET in order to reduce the risk of [¹⁸F]-F-DOPA PET false positives [18].

In such patients, a clinical follow-up and MRI are systematically performed at least every 3 months or at shorter intervals as clinically indicated after surgery [8]. For the present study, the final diagnosis of glioma recurrence/progression at 6 months or for further assessment of survival was blinded from the [¹⁸F]-F-DOPA PET results and was based on current guidelines for which a recurrence/progression is the result of any new tumor or brain lesion at MRI and/or clear increase in tumor size or in contrast enhancement, and/or significant clinical deterioration, with all of these criteria not being attributable to other non-tumor causes and not due to steroid tapering [19-21]. For 4 patients having undergone stereotactic biopsy or open resection, the diagnosis of recurrence/progression was assessed histologically.

The assessment of [¹⁸F]-F-DOPA PET parameters for differentiating recurrence/progression from post-therapeutic effects was based on the evaluation of the previous criteria during a 6-month follow-up period. However, PFS and overall survival (OS) were calculated from the date of the PET exam to the date of definite diagnosis of progression and of death, respectively, with a minimum delay of 19 months of observation. The final date for reporting any event for PFS or OS was June 1, 2019.

The local ethics committee (Comité d'Ethique du CHRU de Nancy) approved the retrospective data evaluation on June 7, 2018, and authorization from the CNIL (National Commission on Information Technology and
Civil Liberties) was delivered on June 25, 2018 (authorization n° R2018-11). This study complied with the principles of the Declaration of Helsinki. Informed consent was obtained from all individuals included in the study.

## Initial pathological grading of the gliomas

All cases were reviewed and classified according to the WHO 2016 classification from tumor samples provided by surgery or stereotactic biopsy [22]. IDH mutation status was assessed by immunohistochemistry with IDH1 R132H protein expression (Dianova, clone H09), or by Sanger sequencing in case of ATRX immunohistochemical loss without IDH1 R132H staining [23]. Tumors presenting oligodendroglial morphology or showing IDH mutation without ATRX loss were additionally tested for 1p/19qco-deletion using multiplex PCR fragment analysis (loss of heterozygosity), or comparative genomic hybridization [24].

## PET recordings and image reconstruction

[¹⁸F]-F-DOPA PET-computed tomography (CT) scans were obtained on a Biograph hybrid system involving a six-detector CT for attenuation correction (Biograph 6 True Point, SIEMENS, Erlangen, Germany). Patients were instructed to fast for at least 4 h with some patients also receiving Carbidopa administration 1 h prior to their exam (n = 17). The CT scan was recorded first and immediately followed by a 30-min 3D list-mode PET recording initiated during the bolus injection of 3 MBq of [¹⁸F]-F-DOPA per kilogram of body weight. The static PET images were reconstructed from the list mode data ranging from 10 to 30 min post-injection [4, 7], while the PET images reconstructed for dynamic parameters encompassed 6 consecutive frames of 20 s each followed by 28 frames of 1 min each. The choice of this acquisition time frame was based on previous studies performed with [¹⁸F]-FET PET (from 0 to 40 min post-injection with a reconstructed 20- to 40-min static image [25]) and on the maximum observed uptake of [¹⁸F]-F-DOPA in a PET study involving high-grade and low-grade gliomas (respectively 8 and 10 min post-injection) [26].

All static and dynamic images were reconstructed with an OSEM 2D algorithm (2 iterations, 21 subsets, 4-mm Gaussian post-reconstruction filter), corrected for attenuation, scatter, and radioactive decay, and displayed in a 256 × 256 matrix with 2.7 × 2.7 × 3.0 mm³ voxels.

## Analyses of PET images

Regions of interest (ROIs) were placed on the static PET images using a dedicated software (DOSIsoft, Cachan, France). A spherical ROI of 2 cm diameter, centered on the maximum lesion uptake, was used for determining

maximum and mean standardized uptake values (SUV-max and SUV-mean, respectively). Tumor-to-striatum (TSR) and tumor-to-normal-brain (TBR) ratios were computed as SUV-mean or SUV-max of the lesion uptake divided by the SUV-mean of the striatum (TSR-mean and TSR-max) or of normal brain (TBR-mean and TBR-max). The SUV-mean from the striatum was obtained from the contralateral basal ganglia, which was segmented using a threshold of 65% of maximal uptake, while the normal reference brain SUV-mean was obtained with a crescent shape ROI (2 × 8 cm) positioned on the semi-oval center of the unaffected contralateral hemisphere, including white and gray matter [4].

When no abnormal [¹⁸F]-F-DOPA uptake was detected, the ROIs of the potential residual tumor were placed at the site of maximal MRI abnormalities with a fused display of PET and fluid attenuation inversion recovery (FLAIR) MRI images [7].

As previously described [27], the metabolic tumor volume (MTV) was obtained through a 3D auto-contouring process with a threshold corresponding to the SUV-mean of the contralateral striatum.

In addition, time-activity curves, representing the evolution of the TBR-mean as a function of time (TAC-ratio), were extracted with the PLANET® Dose software (DOSIsoft, Cachan, France) and with the ROIs previously placed on static images (see above). Each dynamic frame was previously registered on the CT images in order to take into account potential patient movements during acquisitions [28]. Two dynamic parameters were determined from fitted curves to overcome noise effects, using a method already validated for [¹⁸F]-FET in the same setting [29], namely, (i) time-to-peak (TTP), corresponding to the delay between the onset of the dynamic acquisition (time of tracer injection) and the time-point of the maximal TBR-mean value and (ii) slope, calculated with a linear regression applied from the 10th to 30th minute.

## Statistical analysis

Categorical variables are expressed as percentages and continuous variables as median and interquartile range due to the non-normality of variable distributions.

## Recurrence/progression at 6 months follow-up

Univariate analysis was performed using Mann-Whitney tests applied between patients with glioma recurrence/progression and the remaining patients. In order to calculate diagnostic performances, the optimal threshold for each static and dynamic PET parameter was determined from ROC curves using the maximal value of the product of sensitivity and specificity. Thereafter, a multi-variable logistic regression model with forward selection
was performed for predicting patients with glioma recurrence/progression.

### Progression-free survival (PFS) and overall survival (OS)

The dichotomized parameters, which were determined according to previously mentioned optimal thresholds, were used in survival analyses. For this purpose, univariate survival probabilities according to the Kaplan-Meier method with the log-rank test used and the hazard ratio interval of each parameter with its 95% confidence interval were calculated to compare survival curves.

p values obtained in univariate analysis as well as in survival analysis were adjusted using Benjamini-Hochberg correction in order to reduce the risk of false discovery [30]. p values lower than 0.05 were considered as significant.

Analyses were performed with SPSS (SPSS Statistics for Windows, Version 20.0. Armonk, NY: IBM Corp) and R (R Foundation for Statistical Computing, Vienna, Austria).

## Results

### Patient characteristics and follow-up data

The study population included 51 patients with a median age of 50.8 [44.4–59.0] years, 23 of whom were women. According to the 2016 WHO classification for gliomas [22], 8 gliomas (16%) had been initially classified as IDH-mutant astrocytomas (including 2 with anaplastic

component), 6 (12%) as IDH-wildtype astrocytomas (including 2 with anaplastic component), 12 (24%) as IDH-mutant and 1p/19q co-deleted oligodendrogliomas (including 4 with anaplastic component), 22 (43%) as IDH-wildtype glioblastomas (GBM), and 3 (6%) as IDH-mutant GBM. Median delay times from the date of surgery to the [¹⁸F]-F-DOPA PET exam and from the date of the non-contributive MRI to the PET exam were 12.7 [5.9–23.5] months and 16 [7–30] days, respectively.

After the subsequent 6-month follow-up period, 34 patients were ultimately considered as having a recurrent or progressive glioma (3 IDH-mutant astrocytomas, 6 IDH-wildtype astrocytomas, 6 IDH-mutant and 1p/19q co-deleted oligodendrogliomas, 17 IDH-wildtype GBM and 2 IDH-mutant GBM) with 4 cases exhibiting a second recurrence and 2 cases a third recurrence. The remaining 17 patients were thus considered to exhibit only treatment-related changes at MRI. Patient characteristics are detailed in Table 1 as well as in the supplemental Table.

With regard to the location of tumor recurrence, 94% (32/34) were observed in the area of the resection cavity, whereas the remaining 6% (2/34) were located remotely.

During an observation period of 41 [23–50] months following the PET exam, 22 patients (43%) died and 43 (84%) had evidence of tumor progression. In the overall population, median survival was 24 [14–43] months.

<table><caption>**Table 1** Patient characteristics</caption><thead><tr><th>n = 51</th><th>Value</th></tr></thead><tbody><tr><td>Age (years)</td><td></td></tr><tr><td>Median [range]</td><td>50.8 [21.2; 75.3]</td></tr><tr><td>Female gender n (%)</td><td>23 (45)</td></tr><tr><td>Primary histopathological type n (%)</td><td></td></tr><tr><td>IDH-mutant astrocytoma</td><td>8 (16)</td></tr><tr><td>IDH-wildtype astrocytoma</td><td>6 (12)</td></tr><tr><td>IDH-mutant and 1p/19q co-deleted oligodendroglioma</td><td>12 (24)</td></tr><tr><td>IDH-wildtype glioblastoma</td><td>22 (43)</td></tr><tr><td>IDH-mutant glioblastoma</td><td>3 (6)</td></tr><tr><td>Histopathological WHO grade of the primary tumor n (%)</td><td></td></tr><tr><td>II</td><td>18 (35)</td></tr><tr><td>III</td><td>8 (16)</td></tr><tr><td>IV</td><td>25 (49)</td></tr><tr><td>Primary treatment n (%)</td><td></td></tr><tr><td>Surgery</td><td>15 (29)</td></tr><tr><td>Surgery + radiotherapy</td><td>2 (4)</td></tr><tr><td>Surgery + chemotherapy</td><td>9 (18)</td></tr><tr><td>Surgery + radiotherapy + chemotherapy</td><td>25 (49)</td></tr></tbody></table>
<table><caption>**Table 2** Median [interquartile range] of PET parameters in the overall population as well as comparatively between the 2 groups of patients with or without glioma recurrence or progression</caption><thead><tr><th>Parameter</th><th>Overall</th><th>No recurrence/progression (n = 17)</th><th>Recurrence/progression (n = 34)</th><th>Adjusted p value</th></tr></thead><tbody><tr><td>Static</td><td></td><td></td><td></td><td></td></tr><tr><td>TBR<sub>max</sub></td><td>2.22 [1.36; 3.12]</td><td>1.26 [1.12; 1.37]</td><td>2.71 [2.20; 3.69]</td><td>&lt; 0.001</td></tr><tr><td>TBR<sub>mean</sub></td><td>1.57 [1.02; 2.13]</td><td>0.84 [0.75; 1.04]</td><td>1.95 [1.57; 2.70]</td><td>&lt; 0.001</td></tr><tr><td>TSR<sub>max</sub></td><td>1.31 [0.82; 1.90]</td><td>0.77 [0.65; 0.82]</td><td>1.64 [1.31; 2.28]</td><td>&lt; 0.001</td></tr><tr><td>TSR<sub>mean</sub></td><td>0.92 [0.65; 1.43]</td><td>0.51 [0.46; 0.65]</td><td>1.19 [0.92; 1.69]</td><td>&lt; 0.001</td></tr><tr><td>MTV</td><td>1.49 [0.00; 8.22]</td><td>0.00 [0.00; 0.00]</td><td>5.35 [1.49; 15.75]</td><td>&lt; 0.001</td></tr><tr><td>Dynamic</td><td></td><td></td><td></td><td></td></tr><tr><td>TTP</td><td>7.70 [3.35; 18.65]</td><td>14.53 [1.55; 30.00]</td><td>7.67 [4.13; 14.62]</td><td>1</td></tr><tr><td>Slope</td><td>- 0.14 [- 0.82; 0.13]</td><td>0.07 [- 0.07; 0.31]</td><td>- 0.59 [- 0.94; - 0.08]</td><td>&lt; 0.001</td></tr></tbody></table>

MTV metabolic tumor volume, TBR tumor-to-normal brain ratio, TSR tumor-to-striatum ratio

## PET prediction of glioma recurrence/progression

As detailed in Tables 2 and 3, all studied PET parameters, except TTP, were significant univariate predictors of glioma recurrence/progression (all adjusted *p* < 0.001), with a global diagnostic accuracy of 96% being reached with TBR<sub>max</sub>, TSR<sub>max</sub>, and MTV. Meanwhile, the curve slope was the sole significant dynamic PET predictor, although its predictive value was somewhat lower than that obtained with the other PET predictors (i.e., with a lower area under the ROC curve and with a global diagnostic accuracy of only 76.5%, as detailed in Table 3).

On multivariate analysis, TSR<sub>max</sub> was the only parameter selected by the model to predict glioma recurrence/progression, with no other dynamic or static PET parameter able to provide any significant additional predictive information (Table 4).

When separating the gliomas into two groups according to their respective grade at initial diagnosis [22], all of the static parameters as well as the curve slope were able to discriminate recurrent or progressive gliomas in initially classified high-grade gliomas (*n* = 33, *p* ≤ 0.001), whereas the slope parameter was not discriminative in low-grade gliomas (*n* = 18, *p* = 0.13) (Fig. 1).

## PET parameters for predicting PFS and OS

All PET parameters, except TTP, were also significant predictors of PFS on Kaplan-Meier analyses, although none were predictive of OS (adjusted *p* value = 1). More precisely, PFS times were much longer in patients with vs. those without a TBR<sub>max</sub> ≤ 1.61 (29.7 vs. 0.4 months, log-rank test adjusted *p* < 0.001, hazard ratio (HR) = 7.45 [2.39; 23.21], *p* < 0.01), a TBR<sub>mean</sub> ≤ 1.3 (27.8 vs. 0.5 months, log-rank test adjusted *p* < 0.001, HR = 5.81 [2.04; 16.53], *p* < 0.01), a TSR<sub>max</sub> ≤ 1.0 (29.7 vs. 0.4 months, log-rank test adjusted *p* < 0.001, HR = 7.45 [2.39; 23.21], *p* < 0.01), a TSR<sub>mean</sub> ≤ 0.83 (25.6 vs. 0.0 months, log-rank test adjusted *p* < 0.001, HR = 6.0 [1.83; 19.63], *p* = 0.01), a MTV ≤ 0.045 mL (29.7 vs. 0.4 months, log-rank test adjusted *p* < 0.001, HR = 7.45 [2.39; 23.21], *p* < 0.01), or a curve slope ≥ -0.26 h<sup>-1</sup> (18.0 vs. 0.4 months, log-rank test adjusted *p* < 0.001, HR = 2.45 [1.18; 5.07], *p* = 0.03). The corresponding survival curves are depicted in Fig. 2.

Representative examples of patients with or without glioma recurrence/progression investigated with [<sup>18</sup>F]-F-DOPA PET imaging are provided in Fig. 3.

## Discussion

In the present population of patients with suspected glioma recurrence/progression at post-resection MRI,

<table><caption>**Table 3** Results provided by ROC curve analyses for the PET identification of patients with glioma recurrence or progression</caption><thead><tr><th></th><th>AUC</th><th>CI (95%) AUC</th><th>Threshold</th><th>Sensitivity</th><th>Specificity</th><th>Accuracy</th></tr></thead><tbody><tr><td>TBR<sub>max</sub></td><td>0.969</td><td>(0.923-1.0)</td><td>1.61</td><td>97.1%</td><td>94.1%</td><td>96.0%</td></tr><tr><td>TBR<sub>mean</sub></td><td>0.983</td><td>(0.956-1.0)</td><td>1.3</td><td>94.1%</td><td>94.1%</td><td>94.1%</td></tr><tr><td>TSR<sub>max</sub></td><td>0.976</td><td>(0.939-1.0)</td><td>1.0</td><td>97.1%</td><td>94.1%</td><td>96.0%</td></tr><tr><td>TSR<sub>mean</sub></td><td>0.986</td><td>(0.964-1.0)</td><td>0.83</td><td>91.2%</td><td>100%</td><td>94.1%</td></tr><tr><td>MTV</td><td>0.978</td><td>(0.939-1.0)</td><td>0.045 mL</td><td>97.1%</td><td>94.1%</td><td>96.0%</td></tr><tr><td>Slope</td><td>0.818</td><td>(0.702-0.935)</td><td>- 0.26 h<sup>-1</sup></td><td>67.6%</td><td>94.1%</td><td>76.5%</td></tr></tbody></table>

AUC area under the curve, CI confidence interval, MTV metabolic tumor volume, TBR tumor-to-normal brain ratio, TSR tumor-to-striatum ratio
<table><caption>**Table 4** Results of logistic regression for the prediction of recurrence/progression at 6 months after [¹⁸F]-F-DOPA PET</caption><thead><tr><td>Parameter</td><td>Coefficient</td><td>p value</td></tr></thead><tbody><tr><td>Intercept</td><td>– 10.179</td><td>0.001*</td></tr><tr><td>TBR<sub>mean</sub></td><td>–</td><td>0.244</td></tr><tr><td>TBR<sub>max</sub></td><td>–</td><td>0.605</td></tr><tr><td>TSR<sub>mean</sub></td><td>–</td><td>0.116</td></tr><tr><td>TSR<sub>max</sub></td><td>10.039</td><td>0.002*</td></tr><tr><td>MTV</td><td>–</td><td>0.729</td></tr><tr><td>Slope</td><td>–</td><td>0.380</td></tr></tbody></table>

both static and dynamic [¹⁸F]-F-DOPA PET parameters were significant predictors of a glioma recurrence/progression at 6 months, as well as of progression-free survival on the longer term. This diagnostic information was however mostly achieved with conventional static parameters, with limited additional diagnostic information provided by dynamic parameters, contrary to that previously reported in the very different clinical setting of newly diagnosed gliomas [17].

The diagnostic performances of [¹⁸F]-F-DOPA PET imaging in the present study were very high, in keeping with those reported in a previous study [10], reaching a global accuracy of 96% for predicting patients with glioma recurrence/progression at 6 months after the [¹⁸F]-F-DOPA PET exam. This global accuracy is moreover higher when compared with the 82% level previously reported for the [¹⁸F]-F-DOPA PET detection of glioblastoma recurrence in a population of 110 patients [8].

Of particular note, upon multivariate analysis, none of the dynamic parameters were shown to provide any

additional diagnostic information, comparatively to that obtained with static parameters. This statement may be explained by the high performance values obtained with the static parameters in this setting of suspicion of glioma recurrence/progression (accuracy ≥ 94%).

These results partially support previous results documented for [¹⁸F]-FET and in which the univariate predictive values of the dynamic parameters were consistently lower than that of static parameters [11–14]. In particular, in a large study cohort including 124 patients, dynamic [¹⁸F]-FET PET parameters (TTP, curve pattern) were less predictive than static parameters (TBR<sub>max</sub>, TBR<sub>mean</sub>) for glioma recurrence/progression [11].

Furthermore, in our study, the curve slope was predictive of outcome only in the subgroup of high-grade gliomas, similarly to what has been reported for dynamic parameters in a [¹⁸F]-FET PET study involving only high-grade gliomas [31]. Despite the fact that the number of patients was relatively limited, particularly for low-grade gliomas, this finding further strengthens the fact that dynamic parameters are ostensibly more relevant in instances of high expected uptake, i.e., in high-grade gliomas [5–7], in which a clear decreasing slope in kinetics is typically reported in these most aggressive glioma entities [16, 17]. The washout observed in high-grade gliomas, even if still to be well defined, could be related to microvessel density and LAT1 expression but also to the disruption of the blood brain barrier which likely facilitates the initial tumor uptake of the tracers, as well as their subsequent passive backdiffusion [32]. By contrast, lower-grade gliomas preferentially exhibit consistently increasing curves [17, 33, 34], which are very similar to the [¹⁸F]-F-DOPA time-activity curves expected in normal brain tissue or reactive tissue

<figure><img src="image_64.png" /><figcaption>**Fig. 1** Receiver operating characteristic (ROC) curves for TBR<sub>max</sub>, TBR<sub>mean</sub>, TSR<sub>max</sub>, TSR<sub>mean</sub>, MTV, and slope parameters for differentiating between recurrent or progressive gliomas and treatment-related changes in high-grade gliomas (n = 33, left panel) and in low-grade gliomas (n = 18, right panel). Significant ROC curves are represented in solid lines</figcaption></figure>

<footnote>*Indicates parameters ultimately included in the final multivariable model *MTV* metabolic tumor volume, *TBR* tumor-to-normal brain ratio, *TSR* tumor-to-striatum ratio</footnote>
<figure><img src="image_65.png" /><figcaption>**Fig. 2** Kaplan-Meier survival plots for the prediction of progression-free survival using maximal tumor-to-background ratios (upper left panel), mean tumor-to-background ratios (middle left panel), metabolic tumor volume (lower left panel), maximal tumor-to-striatum ratio (upper right panel), mean tumor-to-striatum ratio (middle right panel), and slope (lower right panel) as discriminators. Corresponding log-rank test adjusted *p* values are < 0.0001 for all presented curves</figcaption></figure>

changes [5]. Thus, the use of dynamic parameters in this setting of low-grade gliomas could hence be more challenging. Another potential rationale is that a sufficiently high tumor uptake is required for an accurate determination of dynamic parameters, whereas this uptake was too low in the present study in cases of an absence of any glioma recurrence/progression (median TBR<sub>mean</sub> = 0.84, see Table 2). In contrast to the previous hypotheses, the potential confounding influence of reactive tissue changes, in line with the inflammatory and healing processes induced by surgery and/or radiotherapy and/or chemotherapy [35, 36] cannot be used to explain the lower diagnostic performances from dynamic parameters. Both static and dynamic parameters should

normally be affected in the same manner by the complex histology of the treated sites, involving varying levels of reactive gliosis, inflammatory cells, and radiation-induced changes. Moreover, in our clinical practice, [¹⁸F]-F-DOPA PET for detecting glioma recurrence/progression is performed at least 2 months after the surgery or at the end of radiotherapy, limiting the risk of the aforementioned confounding factors.

[¹⁸F]-F-DOPA PET imaging was also predictive of PFS in the present series of suspected recurrent gliomas, with a lower uptake and an increasing slope being associated with a longer PFS (Fig. 1). Several studies have observed similar relationships between static amino-acid PET parameters and PFS in recurrent gliomas [8, 37–40].
<figure><img src="image_66.png" /><figcaption>**Fig. 3** Representative examples of patients with or without glioma recurrence/progression investigated with [¹⁸F]-F-DOPA PET imaging, with axial slices of [¹⁸F]-F-DOPA PET (left column), dynamic TBR<sub>mean</sub> curves (middle column) providing the time-to-peak delay-time (light blue dotted line), and the 10-to-30 min slope (dark blue dotted line), along with, for illustrative purposes, the same slice location recorded on a FLAIR MRI sequence (right column). **a** 51-year-old woman with no recurrent or progressive glioma (TBR<sub>mean</sub> = 0.8, TBR<sub>max</sub> = 1.1, TSR<sub>mean</sub> = 0.5, TSR<sub>max</sub> = 0.7, MTV = 0 mL, TTP = 30 min, and slope = 0.31 h<sup>-1</sup>). **b** 46-year-old woman with a progressive IDH-wildtype glioblastoma (TBR<sub>mean</sub> = 1.9, TBR<sub>max</sub> = 2.5, TSR<sub>mean</sub> = 1.2, TSR<sub>max</sub> = 1.6, MTV = 6.13 mL, TTP = 7.6 min, and slope = -1.22 h<sup>-1</sup>)</figcaption></figure>

Irrespectively, the data provided herein by [¹⁸F]-F-DOPA PET imaging was furthermore predictive of patient outcome well beyond the 6-month period, reaching up to 18 and 29 months of mean PFS according to dynamic and static parameters, respectively.

The main limitations inherent to this study are that results were obtained retrospectively in a single center. In addition, our sample size was too limited for providing separate analyses according to the different molecular features of the gliomas involved in the present study, whereas it is possible that [¹⁸F]-F-DOPA uptake may vary according to these features [41–44]. Furthermore, the relative low number of histologically verified cases should lead to consider with caution the very high accuracy reported in this study. The time activity curves of tumors were expressed through ratios with tracer activity from normal brain, a method successfully tested by our team for the detection of newly diagnosed gliomas [17], along with the necessity to lower the possible interference of Carbidopa premedication [45]. Lastly, the calculation of the PFS and OS may be questioned given

that all included gliomas, mixing several entities and especially low- and high-grade gliomas, may have benefited from different therapeutic strategies. Notwithstanding, all applied treatments were performed in keeping with general standards [20, 21].

## Conclusion

In summary, this novel study, assessing the relevance of [¹⁸F]-F-DOPA kinetics in the diagnosis of recurrent gliomas shows that patients with a glioma recurrence/progression, occurring remotely after surgery, may be detected by both static and dynamic [¹⁸F]-F-DOPA PET parameters. However, in this population mixing both low- and high-grade gliomas, much of this diagnostic information is achievable by conventional static parameters, contrary to that previously documented for the [¹⁸F]-F-DOPA PET detection of newly diagnosed gliomas. Further studies are warranted to investigate the relevance of such [¹⁸F]-F-DOPA kinetics in populations involving only high-grade gliomas.
**Supplementary information**

**Supplementary information** accompanies this paper at https://doi.org/10.1186/s13550-020-00645-x.

**Additional file 1.** Supplemental Table.

**Abbreviations**

PFS: Progression-free survival; RANO: Response Assessment in Neuro-Oncology; EANM: European Association of Nuclear Medicine; EANO: European Association of Neuro-Oncology; WHO: World Health Organization; OS: Overall survival; CT: Computed tomography; ROI: Region of interest; SUV: Standardized uptake values; TBR: Tumor-to-normal-brain ratio; TSR: Tumor-to-striatum ratio; FLAIR: Fluid attenuation inversion recovery; MTV: Metabolic tumor volume; TTP: Time-to-peak; GBM: Glioblastomas; MET: ¹¹C-methyl-methionine

**Acknowledgements**

Not applicable.

**Authors' contributions**

All authors participated either to the following: (1) study conception and design or analysis and interpretation of the data, or both (TZ, MG, VR, ZL, AV); (2) the drafting of the manuscript or its critical revision for important intellectual content (TZ, MG, FR, MB, GG, PYM, LI, AV); or (3) final approval of the submitted manuscript (FR, MB, GG, LT, LI, AV). The author(s) read and approved the final manuscript.

**Funding**

None

**Availability of data and materials**

All data generated or analyzed during this study are included in this published article and its supplementary information files.

**Ethics approval and consent to participate**

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Written informed consent was obtained from all individual participants included in the study. The local ethics committee (Comité d'Ethique du CHRU de Nancy) approved the retrospective data evaluation on June 7, 2018, and authorization from the CNIL (National Commission on Information Technology and Civil Liberties) was delivered on June 25, 2018 (authorization n° R2018-11).

**Consent for publication**

Not applicable.

**Competing interests**

All authors declare that they have no competing interests.

**Author details**

¹Department of Nuclear Medicine & Nancycloptep Imaging platform, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France. ²IADI, INSERM, UMR 1254, Université de Lorraine, F-54000 Nancy, France. ³INSERM, U1116, Université de Lorraine, F-54000 Nancy, France. ⁴Department of Pathology, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France. ⁵INSERM U1256, Université de Lorraine, F-54000 Nancy, France. ⁶Department of Neurosurgery, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France. ⁷Centre de Recherche en Automatique de Nancy CRAN, CNRS UMR 7039, Université de Lorraine, F-54000 Nancy, France. ⁸Department of Neuro-oncology, Université de Lorraine, CHRU-Nancy, F-54000 Nancy, France.

Received: 6 March 2020 Accepted: 13 May 2020

Published online: 29 May 2020

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