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  • Research
  • Open Access

Prognostic value of subventricular zone involvement in relation to tumor volumes defined by fused MRI and O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET imaging in glioblastoma multiforme

Radiation Oncology201914:37

https://doi.org/10.1186/s13014-019-1241-0

  • Received: 19 September 2018
  • Accepted: 21 February 2019
  • Published:

Abstract

Background

Subventricular zone (SVZ) involvement is associated with a dismal prognosis in patients with glioblastoma multiforme (GBM). Dual-time point (dtp) O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET/CT (PET) may be a time- and cost-effective alternative to dynamic FET PET, but its prognostic value, particularly with respect to SVZ involvement, is unknown.

Methods

Thirty-five patients had two scans 5–15 and 50–60 min after i.v. FET injection to define tumor volumes and SVZ involvement before starting radiotherapy. Associations between clinical progression markers, MRI- and dtp FET PET-based tumor volumes, or SVZ involvement and progression-free (PFS) and overall survival (OS) were assessed in univariable and multivariable analyses.

Results

The extent of resection was not related to outcomes. Albeit non-significant, dtp FET PET detected more SVZ infiltration than MRI (60% vs. 51%, p = 0.25) and was significantly associated with poor survival (p < 0.03), but PET-T1-Gad volumes were larger in this group (p < 0.002). Survival was shorter in patients with larger MRI tumor volumes, larger PET tumor volumes, and worse Karnofsky performance status (KPS), with fused PET-T1-Gad and KPS significant in multivariable analysis (p < 0.03). Uptake kinetics was not associated with treatment outcomes.

Conclusions

FET PET-based tumor volumes may be useful for predicting worse prognosis glioblastoma. Although the presence of SVZ infiltration is linked to higher PET/MRI-based tumor volumes, the independent value of dtp FET PET parameters and SVZ infiltration as prognostic markers pre-irradiation has not been confirmed.

Keywords

  • Glioblastoma multiforme
  • FET PET
  • Subventricular zone
  • Prognosis
  • Imaging biomarker

Background

Glioblastoma multiforme (GBM) is the most aggressive malignant primary central nervous system tumor. While the majority of GBMs have similar pre-treatment magnetic resonance imaging (MRI) characteristics, subgroups exist with distinct clinical behaviors, genetic alterations, and outcomes. According to grading prognostic assessment (GPA) scoring, patients with newly diagnosed GBM qualifying for chemoradiotherapy have a two-year overall survival (OS) of between 5 and 35%, but 5–10% of GBM patients experience long-term survival [1]. Identifying prognostic groups who would benefit from different, personalized treatment remains challenging.

Age, Karnofsky performance status (KPS), and extent of surgery are all prognostic in GBM [2, 3], and more recently prognostic biomarkers have been described including O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation [4, 5], isocitrate dehydrogenase 1 or 2 gene mutations [6, 7], and subventricular zone (SVZ) involvement [8]. However, the relationship between pre-irradiation MRI contrast enhancement-based tumor volume and clinical outcome remains controversial [9, 10].

Therefore, accurately predicting tumor behavior in individual patients based on imaging parameters remains challenging, especially when molecular-genetic factors are not available. Imaging may be especially important given that mutations show intratumoral heterogeneity in non-operable, sub-totally operated, or MGMT promoter status-undefined patients [1113].

SVZ infiltration defined by MRI is known to be associated with treatment outcomes and progression and is thought to arise from neural stem cells [14, 15]. Extensive peritumoral edema on imaging may also be associated with survival [16, 17], since edema defined by MRI-T2 sequences may represent a mixture of neoplastic cells as well as vasogenic edema [18]. However, imaging parameters that more accurately define prognosis are still urgently needed to individualize treatment.

Positron-emission tomography/CT (PET) using O-(2-[18F]fluoroethyl)-L-tyrosine (FET) has been widely used for static and dynamic imaging in patients with brain tumors [19, 20]. Dynamic FET PET is helpful for defining aggression in WHO III astrocytomas [21] and low-grade gliomas (LGGs) [22, 23]. Moreover, WHO I-II gliomas show increased uptake kinetics compared to WHO III-IV high-grade gliomas (HGGs) [24, 25]. Dynamic acquisition more accurately differentiates LGGs from HGGs than standard static scans (20–40 min post-injection (p.i.)), mainly due to the characteristic high FET uptake in HGGs in the initial phase [26]. However, many institutions do not have routine access to dynamic PET imaging techniques. When dynamic PET cannot be performed, FET PET acquisition at a few selected time points may be a cost- and time-effective alternative as demonstrated using relatively early and very late time points (20–40 min p.i. and 70–90 min p.i.) [27]. However, experience with dtp FET and other amino acid PET tracers in patients with gliomas remains limited. Biological tumor volume defined by dtp FET PET correlates with progression site [28], but to the best of our knowledge, the prognostic impact of dtp FET PET parameters in GBM patients has yet to be determined. We hypothesized that dtp FET PET imaging in combination with SVZ infiltration would accurately select subgroups of patients with different chemoradiotherapy outcomes.

Methods

Study and patient details

This was a post-hoc analysis of a prospective study approved by the Ethics Committee of Collegium Medicum of Nicolaus Copernicus University (procedure nr KB257/2012), and all subjects signed written informed consent. Thirty-five consecutive patients with newly diagnosed GBM referred for radiotherapy planning between December 2012 and October 2014 and fulfilling pre-specified criteria were included. Inclusion criteria were: (i) KPS > 50 with normal mental status; age 18 years or greater; histopathological confirmation of GBM; previously untreated with radiation and/or chemotherapy; and time between PET examination and start of chemoradiotherapy no longer than 2 weeks. Patients underwent dtp FET PET scans at the Department of Nuclear Medicine, the Franciszek Lukaszczyk Oncology Centre in Bydgoszcz. Radiotherapy was performed in the Department of Radiotherapy, the Franciszek Lukaszczyk Oncology Centre in Bydgoszcz.

The maximum follow-up was 48 months. Progression-free survival (PFS) was measured from the start of radiotherapy to the date of tumor growth on conventional MRI according to Modified Response Assessment in Neuro-Oncology criteria [29]. All progressions were stratified into whether they occurred locally (within 2 cm of the primary tumor defined by MRI) or distantly (outside this margin). For survival analysis, family members were contacted to confirm the exact date of death. OS was defined as the time from the start of radiotherapy until death.

MRI and 18F-FET PET/CT

All radiotherapy planning MRI studies were carried out using a Philips camera (3 Tesla; Achieva 3.0 T X-series, Philips Medical Systems, Crawley, UK) and a standard head coil up to 7 days prior to radiotherapy in two stages: (i) standard head MRI, taking the area containing tumor into account in the spin-echo or turbo spin-echo sequence in T1-, T2-, and PD-dependent images at three levels: frontal, sagittal, and transverse; and (ii) patients received intravenous contrast (gadolinium diethylenetriamine pentaacetate; Magnevist, Bayer Schering Pharma, Berlin, Germany) at a dose of 0.2 ml/kg body weight with 180 s of imaging using the spin-echo sequence in T1-weighted images in three dimensions. The scan thickness was 2 mm in a 512 × 512 pixel matrix. Tumor was defined as the area of contrast-induced signal enhancement in the T1 sequence. Hypointense areas without contrast enhancement on T1 images were regarded as the postoperative bed.

All PET/CT scans were performed using a mCT128 Biograph (Siemens Medical Solutions, Erlangen, Germany) using locally produced FET radiotracer. The amino acid 18F-FET was produced and applied as described previously [30].

Patients were fasted for 4 h prior to data collection. Radiotracer uptake was assessed after 5–15 min and 50–60 min after i.v. administration of 350 ± 10 MBq FET. Image acquisition was performed in the supine position after head immobilization with an individual thermoplastic mask fixed to the scanner table. CT scans were performed as follows: CARE Dose 4D, 120 kV, and pit 0.7 recorded every 2.7 min per 1 position of the bed. The TrueX+TOF (UltraHD-PET) three-dimensional algorithm was used for image reconstruction.

FET tissue uptake was recorded as a standardized uptake value (SUV) defined as the ratio of radioactivity (MBq/ml) of the tissue marker to the initial radioactivity of the marker administered i.v. according to the patient’s weight [31]. The tumor was assessed using the Leonardo™ diagnostic station (Siemens Medical Solutions/CTI).

To measure FET uptake, volumes of interest (VOI) were defined in similarly sized symmetrical areas defined by the tumor on one side and normal tissue in the other (normal) hemisphere. In the semi-quantitative analysis, 5–15 and 50–60 min after administering radiotracer, the maximum SUV (SUVMAX) and the mean SUV (SUVMEAN) were specified for each VOI on PET scans with CT images used as reference images. The SUVMEAN and SUVMAX ratios in the VOI of the tumor to healthy brain were determined (tumor-to-brain ratio, TBRMAX and TBRMEAN). Tumors were contoured semi-automatically as areas corresponding to radiotracer uptake above 1.6 x SUVMEAN in the VOI of normal brain (threshold) corrected to areas of physiological activity in the basal ganglia, thalamus, cerebellum, skull bones, sphenoidal sinus, sagittal sinus, pituitary, and vessels [31, 32]. The tumor area was defined this way 5–15 (PETVOL 10) and 50–60 min (PETVOL 60) after radiotracer administration. Fused volumes of the larger of PET and MRI volumes with (PET-T1-Gad) and without tumor bed (PET-T1-Gad without tumor bed) were assessed. A nuclear medicine specialist and radiation oncologist jointly evaluated each case.

FET uptake values analysis

The differences between TBRMEAN10 and TBRMEAN60 (TBRMEAN diff), TBRMAX10 and TBRMAX60 (TBRMAX diff), SUVMEAN10 and SUVMEAN60 (SUVMEAN diff), SUVMAX10 and SUVMAX60 (SUVMAX diff) were calculated in each case. The difference between PET tumor volumes (PETVOL diff) was also defined.

Subventricular zone invasion and extensive peritumoral edema

SVZ was defined as contrast-enhanced lesions and/or dtp FET PET-positive uptake involving the wall of the lateral ventricle. Patients without SVZ involvement on MRI but infiltrated in PET were defined (Fig. 1).
Fig. 1
Fig. 1

Comparison of pre- and post-irradiation dtp FET PET and MRI images of glioblastoma long-term survival in the right frontal lobe. Dtp FET PET volume < 40 cm3. The large pathological uptake volume extends into the SVZ over a substantial area (a). Contrast-enhanced T1-weighted MRI performed for radiotherapy planning with no SVZ infiltration in the axial, sagittal, and coronal planes (b). The patient had a favorable outcome, being alive at the end of the observation period (OS 47 months) without progression. Twelve months post-treatment dtp FET PET with complete response: TBR below 1.6 (c) and MRI with residual contrast enhancement (d)

Peritumoral edema was observed as hyperintense areas in T2-weighted or FLAIR MRI or hypointense areas in T1-weighted images. Extensive peritumoral edema (EPE) was defined when edema extended 2 cm from the tumor border as in [33]. SVZ and EPE were analyzed in relation to OS or PFS. Moreover, the OS and PFS of tumors involved SVZ (+) and not involved SVZ (−) in combination with all clinical and imaging parameters were analyzed. The median was used as the threshold for tumor volumes and imaging quantitative parameters.

Statistical analysis

Calculations were performed in STATISTICA v13.0 (Statsoft, Poland). Quantitative parameters are presented as minimum and maximum values (min and max) and mean (□) and median values. Distributions were assessed using the Shapiro-Wilk test; parameters without a normal distribution were analyzed using the Mann-Whitney rank sum test. Spearman’s correlations were used to compare two quantitative parameters. For univariate analyses, Cox regression was used to assess the significance of individual variables using log-rank tests. OS and PFS were analyzed with Kaplan–Meier survival curves. The median was used as the threshold for dichotomizing parameters. To examine relative effects, multivariate regression analyses and log-rank (Mantel-Cox) testing were performed. P-values < 0.05 were considered significant.

Results

Overall characteristics

Thirty-five patients were eligible for study. During a mean observation period of 36 months, 32 patients (91%) died. The mean OS was 16 ± 2 months (range, 4–48 months), and the mean PFS was 10 ± 2 months (range, 2–47 months). The clinical parameters including MRI and PET tumor volumes are summarized in Table 1.
Table 1

Clinical and radiological parameters of the study population

 

n (range)

%

Gross total resection defined with MRI

24

68

KPS performance > 70

24

68

Age (mean)

53 (29–73)

Sex (male)

25

71

SVZ infiltration defined by MRI

18

51

SVZ infiltration defined by dtp FET PET

21

60

EPE

10

28

Distant progression

9

25

TBRMAX increased

9

25

TBRMEAN increased

4

11

SUVMAX increased

13

37

SUVMEAN increased

24

68

PETVOL 10 (mean)

39 (1–115)

PETVOL 60 (mean)

34 (1–101)

T1-Gad (mean)

30 (4–89)

PET-T1-Gad (mean)

50 (5–131)

PET-T1-Gad without tumor bed (mean)

45 (3–129)

Progression-free survival and overall survival

Better KPS performance status (> 70%) had a favorable impact on PFS (Kaplan-Meier test; HR 0.09, 95% CI 0.02–0.38, p = 0.001) and OS (Kaplan-Meier test; HR 0.03, 95% CI 0.007–0.11, p = 0.001; Fig. 2a and b and Additional file 1: Table S1), and was correlated with PFS (p = 0.007) and OS (p < 0.001) as assessed by Spearman’s rank correlations (Additional file 1: Table S2A). Gross total resection had no impact on PFS (p = 0.594) or OS (p = 0.22) (Additional file 1: Table S2B). Other significant parameters are presented in Additional file 1: Table S1.
Fig. 2
Fig. 2

a Kaplan-Meier plots of survival versus KPS showing significantly worse survival in patients with KPS values greater than the median (median OS 15 months versus 7 months). b Kaplan-Meier plots showing that greater KPS was associated with worse PFS (median PFS 8 versus 4). c and d PET-T1-Gad volume was associated with worse survival but not PFS, respectively

There were no statistically significant relationships between PFS and the quantitative imaging parameters in the univariate analysis. However, for OS, there were significant and negative Spearman’s correlations with age, PETVOL 10, PETVOL 60, T1-Gad, PET-T1-Gad, and PET-T1-Gad without tumor bed (Table 2). OS was significantly longer for the group without SVZ involvement (Mann-Whitney test; mean OS 14.1 vs. 18.8 months, median OS 10 vs. 15 months, p = 0.021, Table 3), but tumor volumes were significantly smaller in this group (PET-T1-Gad mean volume in SVZ- vs. SVZ+ 35.9 cm3 vs. 64.2cm3, p = 0.004).
Table 2

Univariate analysis of patient survival related to selected quantitative imaging factors (Spearman’s rank test)

Parameter

PFS

OS

R

p

R

p

Age

0.130

0.502

−0.375

0.032

PETVOL10

−0.145

0.454

−0.374

0.032

PETVOL60

−0.220

0.251

−0.423

0.014

PETVOLdiff

0.355

0.059

0.161

0.372

T1-Gad

−0.293

0.131

−0.441

0.011

PET T1-Gad

−0.198

0.312

−0.443

0.011

PET-T1 Gad w/o tumor bed

−0.205

0.295

−0.447

0.010

TBRMEAN DIFF

0.159

0.418

0.012

0.949

TBRMAX DIFF

0.237

0.225

0.304

0.091

SUVMEAN DIFF

−0.002

0.993

−0.317

0.073

SUVMAX DIFF

0.254

0.183

0.130

0.471

SUVMAX10

0.363

0.058

0.184

0.314

SUVMEAN 10

0.021

0.913

−0.163

0.364

TBRMAX10

0.300

0.121

0.300

0.095

TBRMEAN10

0.152

0.439

0.058

0.753

SUVMAX60

0.100

0.604

−0.045

0.803

SUVMEAN60

0.081

0.677

−0.069

0.701

TBRMAX60

0.140

0.477

0.049

0.790

TBRMEAN60

0.041

0.838

0.055

0.767

Table 3

Analysis of patient survival and imaging parameters in relation to SVZ involvement (Mann-Whitney test)

Parameter

SVZ involved

SVZ not involved

p-values

Mean

Median

Range

Mean

Median

Range

PFS

11.7

5.0

3–47

9.1

7.5

2–37

0.650

OS

14.1

10.0

4–48

18.8

15.0

9–41

0.021

Age

56.1

58.0

34–73

50.8

48.0

29–72

0.207

PETVOL10

49.7

51.1

3.07–115.63

27.0

25.7

1.24–67.93

0.007

PETVOL60

46.0

50.2

0.79–101.7

21.9

19.4

1.48–60.06

0.007

PET VOL DIFF

4.5

4.5

−16.06 - 27,61

5.2

4.4

−3.28 - 20.5

0.832

T1-Gad

38.6

33.5

11.2–89.31

21.5

16.3

4.23–53.48

0.024

PET T1-Gad

64.2

62.5

17.8–131.5

35.9

29.9

5.1–103.1

0.004

PET-T1Gad w/o tumor bed

59,4

58,6

11.2–129.2

29.7

26.2

2.8: - 86.6

0.001

TBRMEANDIFF

0.2

0.2

−0.07 - 0.69

0.3

0.2

−0.09 - 0.8

0.634

TBRMAXDIFF

0.3

0.2

−0.43 - 2.33

1.0

0.6

−0.4 - 5.45

0.067

SUVMEANDIFF

−0.1

−0.1

−1.14 - 1.01

0.0

−0.1

− 0.49 - 1.25

0.807

SUVMAXDIFF

0.6

0.0

−0.84 - 4.28

0.5

0.6

−1.5 - 3.5

0.832

SUVMAX10

3.7

3.3

1.03–10.16

3.1

3.4

1.26–4.79

0.646

SUVMEAN10

1.8

1.7

0.8–2.7

1.5

1.5

0.98–2.04

0.025

TBRMAX10

3.1

2.9

1.92–7.66

3.3

3.0

1.37–6.38

0.734

TBRMEAN10

2.5

2.3

2–3.95

2.4

2.3

1.85–3.92

0.518

SUVMAX60

3.8

3.4

1.48–8.21

3.0

3.0

1.78–5.36

0.103

SUVMEAN60

1.9

1.9

1.18–3.23

1.7

1.7

0.96–2.76

0.207

TBRMAX60

2.8

2.5

1.88–5.33

2.3

2.2

0.93–3.2

0.067

TBRMEAN60

2.3

2.2

1.89–3.39

2.1

2.0

1.65–3.12

0.079

Significant variables (KPS, SVZ, PET-T1-Gad) in univariable OS analysis and one parameter representing changes in PET uptake values (TBRMAXdiff) and previously studied by the same authors [34] were entered into a multivariable model (Table 4). KPS and PET-T1-Gad were associated with OS with a close but non-significant relationship for SVZ. For PFS, only KPS was associated in multivariable analysis.
Table 4

Multivariate linear regression analyses of OS or PFS versus age, KPS, SVZ, PET-T1-Gad, and TBRMAX DIFF

Parameter

OS

Coefficient

p-value

Age

−0.11 (0.50)

0.482

KPS

−14.38 (0.45)

0.006

SVZ

8.82 (0.25)

0.091

PET-T1-Gad

−0.142 (0.059)

0.047

TBRMAX DIFF

0.72 (0.13)

0.680

R2 = 0.406

Parameter

PFS

Coefficient

p

Age

0.16 (0.19)

0.414

KPS

−13.80 (5.86)

0.028

SVZ

10.27 (5.56)

0.078

PET T1 Gad

−0.112 (0.08)

0.194

TBRMAX DIFF

−0.96 (1.92)

0.62

R2 = 0.29

FET uptake values and kinetics measured in dual time-point assessments

Uptake and kinetic values for the whole group are listed in Table 5, and the kinetic data for patients are presented in Additional file 1: Table S3. In all cases, uptake was above the threshold of 1.6 x mean background. Kinetic analysis was available for 34/35 patients. The majority of GBMs had decreased kinetics measured according to TBRMEAN diff and TBRMAX diff parameters. Kinetic parameters measured quantitatively were not associated with survival (Table 2).
Table 5

dtp FET PET parameters in glioblastoma

Parameter

Mean

Median

Range

TBRMEAN DIFF

0.2

0.2

−0.09 – 0.8

TBRMAX DIFF

0.6

0.4

−0.43 – 5.45

SUVMEAN DIFF

−0.1

−0.1

−1.14 – 1.25

SUVMAX DIFF

0.6

0.4

−1.5 – 4.28

SUVMAX10

3.4

3.4

1.03–10.16

SUVMEAN10

1.6

1.6

0.8–2.7

TBRMAX10

3.2

2.9

1.37–7.66

TBRMEAN10

2.4

2.3

1.85–3.95

SUVMAX60

3.4

3.2

1.48–8.21

SUVMEAN60

1.8

1.8

0.96–3.23

TBRMAX60

2.6

2.4

0.93–5.33

TBRMEAN60

2.2

2.1

1.65–3.39

FET and SVZ infiltration

SVZ infiltration was present in MRI scans from 18 patients and, by adding dtp FET PET data, three further cases of SVZ infiltration could be defined (21/35; 60%, p = 0.25). MRI-based, PET-based, or fused volumes differed significantly when there was SVZ involvement (Additional file 1: Table S5). The most significant difference was for mean PET-T1-Gad without tumor bed (59 cm3 in SVZ-positive tumors and 29.7 cm3 in SVZ-negative tumors; p = 0.001). TBRMAX, TBRMEAN, and kinetic parameters were nearly identical in both groups. EPE and quantitative parameters above median were not additional negative factors when combined with SVZ (Additional file 1: Table S4).

Discussion

Here we show that pre-irradiation tumor volumes have a prognostic impact in GBM. Of the analyzed volumes, fused dtp FET PET for T1-Gad-based volume without the tumor bed was the most powerful predictor and may therefore be of value for radiation treatment planning. SVZ involvement, KPS performance status, and age but not the tumor-to-brain uptake ratios or FET kinetics measured by dtp PET/CT were prognostic in univariate analysis. However, the presence or absence of SVZ was associated with higher PET/MR tumor volumes, so this association was no longer significant in multivariate analysis.

Tumors are known to differ in shape and size when defined 10 and 60 min post FET injection and corresponded with the site of recurrence [28]. The current study shows that dtp FET PET parameters does not provides additional information as a prognostic imaging biomarker, although we note that FET PET performed after irradiation treatment response has previously been shown to be a marker of both PFS and OS [9].

Current PET tracers provide additional prognostic value in GBM [9, 3538]. However, amino acids and FET specifically are most commonly used for PET due to low uptake into inflammatory tissues, high stability, and longer half-life of 18F-FET [39].

Tumor volumes are vulnerable to the FET PET acquisition method. Tumor-to-brain ratios of 1.6 or greater determine the FET tumor volume and depend on the time of measurement, spatial resolution of the PET scans, and image processing [20]. The most commonly used method represents a summation of dynamic PET scans and a single static scan 20–40 min post FET injection. Pre-irradiation tumor volumes defined on static PET have been shown to be prognostic [9]. However, in high-grade gliomas, there can be increased tracer uptake at earlier time frames [26, 40], so tumor volume might be underestimated in the standard 20–40-min scan frequently taken in static PET-based radiation treatment planning [26]. However, in our group, 25% of patients had different uptake kinetics that may underestimate tumor volumes when categorized only on early (5–15 min p.i.) acquisition. The largest study to date on the topic reported a correlation between PET volumes and OS based on dynamic PET results [38]. However, dynamic FET PET is more time-consuming and costly, requiring 40–50 min of scanning time [20], which may be too long to patients to tolerate the thermoplastic mask.

Grosu et al. [41] reported that gross tumor volumes were not significantly different when measured by L-[methyl-11C]-methionine (MET) PET or FET PET by static acquisition. However, FET PET-based volumes depend on the time of uptake measurement, which may have limited this comparison. Moreover, the different uptake kinetics (also known as the time-activity curve (TAC)) is a feature of FET not observed with MET [20].

Uptake kinetics have been shown to be prognostic in more aggressive low-grade [22] and WHO III [21] gliomas. Further, in a study of selected patients, uptake kinetics had an impact on prognosis [23]. We could not confirm this finding here, perhaps due to the smaller group size, non-selected cohort, or different method of PET acquisition.

With respect to the impact of SVZ invasion on GBM prognosis, our results are consistent with other studies [8, 14, 33]. However, to our knowledge, this is the first report that FET-PET-detected infiltration of the surrounding brain is larger in SVZ areas then in other locations. These large FET uptake volumes may, to some extent, explain worse outcomes for patients with SVZ invasion, which is typically explained in pre-clinical studies by the “neural stem cell niche” concept. Moreover, tumor not defined as infiltrating the SVZ on MRI may actually extend into this area when defined by dtp FET PET. Further, the frequency of SVZ involvement not shown on MRI but present in dtp FET PET is unknown. Here, this frequency was not significantly increased, but this was simply due to the larger volumes defined by dtp FET PET. It could be of value to target SVZ areas with modified or higher than routine radiation doses in future research studies.

The maximal safe resection of contrast-enhancing tumors is the mainstay of treatment for newly diagnosed GBM. Although extensively studied, the prognostic value of partial resection remains controversial, but the benefit of gross total resection in association with survival has been established [42]. This, however, was not seen in our study. Other factors such as MGMT methylation have a substantial impact on prognosis and, in combination with the small number of events, could impact on the lack of effect of gross total removal on survival. Here, 24 patients underwent gross total resection as defined by MRI; interestingly, all 35 patients had pathological uptake values in the surrounding tumor bed. This suggests that when the aim is to remove a contrast enhanced portion of a tumor, partial rather than gross tumor removal is the actual result. A recent large retrospective analysis showed that the additional removal of a significant portion of the FLAIR-abnormal region was associated with better survival [43]. Further, PET-based tumor removal may prolong survival in patients with high-grade gliomas [10]. Our study supports the concept that pre-radiation tumor volumes, especially when defined by PET rather than contrast-enhanced tumor removal, influences prognosis [9, 44]. The prognostic value of PET-based volume was also recently reported in a case of re-irradiation [45]. Nevertheless, performance status post-surgery remains the most important clinical marker of treatment outcome.

The dtp FET PET methodology used here and that published by Lohmann et al. [27] are different (20–40 p.i. and 70–90 p.i. in [27] and 5–15 p.i. and 50–60 p.i. here). The frequently recommended acquisition (20–40 p.i.) is not one of the two time points, which might have influenced the results (the threshold of 1.6 of the background has been validated on 20–40 p.i. static images). However, this might explain why 25% of GBMs showed decreased uptake (the maximal uptake could have been obtained in the period 20–40 min p.i., and thus missed by the choice of timepoints).

The main limitations of this study are the post hoc analysis of SVZ infiltration, manual measurement of tumor volumes that may influence results, and the relatively small number of patients. The lack of known MGMT promoter methylation status may also be regarded as limiting, but this was not standard care during recruitment. However, it has been shown that the FET PET volumes are independent of MGMT methylation status [38]. This study is also strengthened by its prospective nature, no pre-selection of patients, and confirmation of the exact date of death.

Conclusions

FET PET-based tumor volumes may be useful for predicting a worse prognosis in glioblastoma patients. Although the presence of SVZ infiltration is linked to higher PET/MRI-based tumor volumes, the independent value of dtp FET PET parameters and SVZ infiltration as prognostic markers pre-irradiation has not been confirmed.

Abbreviations

dtp: 

Dual-time point

FET: 

O-(2-[18F]fluoroethyl)-L-tyrosine

GBM: 

Glioblastoma multiforme

GPA: 

Grading prognostic assessment

HGGs: 

High-grade gliomas

KPS: 

Karnofsky performance status

LGGs: 

Low-grade gliomas

MRI: 

Magnetic resonance imaging

OS: 

Overall survival

PET: 

Positron-emission tomography

PFS: 

Progression-free survival

SUV: 

Standardized uptake value

SVZ: 

Subventricular zone

TBR: 

Tumor-to-brain ratio

VOI: 

Volumes of interest

Declarations

Acknowledgements

We gratefully acknowledge Nextgenediting (http://www.nextgenediting.com) for editorial assistance.

Funding

None.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

MH and BM conceived and designed the experiments; MH and BM performed the experiments; MH analyzed the data; MH contributed reagents/materials/analysis tools; MH wrote the paper. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The Ethics Committee of Collegium Medicum of Nicolaus Copernicus University approved this prospective longitudinal study (procedure nr KB257/2012), and all subjects signed written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Oncology and Brachytherapy, Nicolaus Copernicus University, Ludwik Rydygier Collegium Medicum, Romanowskiej 2 St, ,85-796 Bydgoszcz, Poland
(2)
Department of Positron Emission Tomography and Molecular Imaging, Nicolaus Copernicus University, Ludwik Rydygier Collegium Medicum, Bydgoszcz, Poland
(3)
Department of Oncology, Radiotherapy and Gynecologic Oncology, Faculty of Health Sciences, Nicolaus Copernicus University Toruń, Bydgoszcz, Poland
(4)
Department of Radiotherapy, Unit of Radiosurgery and Radiotherapy of CNS, Franciszek Lukaszczyk Oncology Center, Bydgoszcz, Poland

References

  1. Krex D, Klink B, Hartmann C, von Deimling A, Pietsch T, Simon M, et al. Long-term survival with glioblastoma multiforme. Brain. 2007;130(10):2596–606. https://doi.org/10.1093/brain/awm204.View ArticlePubMedGoogle Scholar
  2. Li J, Wang M, Won M, Shaw EG, Coughlin C, Curran WJ, et al. Validation and simplification of the radiation therapy oncology group recursive partitioning analysis classification for glioblastoma. Int J Rad Oncol Biol Phys. 2011;81(3):623–30. https://doi.org/10.1016/j.ijrobp.2010.06.012.View ArticleGoogle Scholar
  3. Tsien C, Gomez-Hassan D, Chenevert TL, Lee J, Lawrence T, Ten Haken RK, et al. Predicting outcome of patients with high-grade gliomas after radiotherapy using quantitative analysis of T1-weighted magnetic resonance imaging. Int J Radiat Oncol Biol Phys. 2007;67(5):1476–83. https://doi.org/10.1016/j.ijrobp.2006.11.020.View ArticlePubMedGoogle Scholar
  4. Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, et al. MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med. 2005;352(10):997–1003. https://doi.org/10.1056/NEJMoa043331.View ArticlePubMedGoogle Scholar
  5. Wick W, Weller M, van den Bent M, Sanson M, Weiler M, von Deimling A, et al. MGMT testing-the challenges for biomarker-based glioma treatment. Nat Rev Neurol. 2014;10(7):372–85. https://doi.org/10.1038/nrneurol.2014.100.View ArticlePubMedGoogle Scholar
  6. Molenaar RJ, Verbaan D, Lamba S, Zanon C, Jeuken JW, Boots-Sprenger SH, et al. The combination of IDH1 mutations and MGMT methylation status predicts survival in glioblastoma better than either IDH1 or MGMT alone. Neuro-Oncology. 2014 Sep;16(9):1263–73. https://doi.org/10.1093/neuonc/nou005.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W, et al. IDH1 and IDH2 mutations in gliomas. N Engl J Med. 2009 Feb 19;360(8):765–73. https://doi.org/10.1056/NEJMoa0808710.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Jafri NF, Clarke JL, Weinberg V, Barani IJ, Cha S. Relationship of glioblastoma multiforme to the subventricular zone is associated with survival. Neuro-Oncology. 2013 Jan;15(1):91–6. https://doi.org/10.1093/neuonc/nos268.View ArticlePubMedGoogle Scholar
  9. Piroth MD, Holy R, Pinkawa M, Stoffels G, Kaiser HJ, Galldiks N, et al. Prognostic impact of postoperative, pre-irradiation (18)F-fluoroethyl-l-tyrosine uptake in glioblastoma patients treated with radiochemotherapy. Radiother Oncol. 2011;99(2):218–24. https://doi.org/10.1016/j.radonc.2011.03.006.View ArticlePubMedGoogle Scholar
  10. Pirotte BJ, Levivier M, Goldman S, Massager N, Wikler D, Dewitte O, et al. Positron emission tomography-guided volumetric resection of supratentorial high-grade gliomas: a survival analysis in 66 consecutive patients. Neurosurgery. 2009;64(3):471–81. https://doi.org/10.1227/01.NEU.0000338949.94496.85.View ArticlePubMedGoogle Scholar
  11. Sottoriva A, Spiteri I, Piccirillo SG, Touloumis A, Collins VP, Marioni JC, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A. 2013;110(10):4009–14. https://doi.org/10.1073/pnas.1219747110.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Parkinson JF, Wheeler HR, Clarkson A, McKenzie CA, Biggs MT, Little NS, et al. Variation of O(6)-methylguanine-DNA methyltransferase (MGMT) promoter methylation in serial samples in glioblastoma. J Neuro-Oncol. 2008;87(1):71–8.View ArticleGoogle Scholar
  13. Parker NR, Hudson AL, Khong P, Parkinson JF, Dwight T, Ikin RJ, et al. Intratumoral heterogeneity identified at the epigenetic, genetic and transcriptional level in glioblastoma. Sci Rep. 2016;6:22477. https://doi.org/10.1038/srep22477.doi:10.1007/s11060-007-9486-0.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Lim DA, Cha S, Mayo MC, Chen MH, Keles E, VandenBerg S, et al. Relationship of glioblastoma multiforme to neural stem cell regions predicts invasive and multifocal tumor phenotype. Neuro-Oncology. 2007;9(4):424–9. https://doi.org/10.1215/15228517-2007-023.View ArticlePubMedPubMed CentralGoogle Scholar
  15. Adeberg S, König L, Bostel T, Harrabi S, Welzel T, Debus J, et al. Glioblastoma recurrence patterns after radiation therapy with regard to the subventricular zone. Int J Radiat Oncol Biol Phys. 2014;90(4):886–93. https://doi.org/10.1016/j.ijrobp.2014.07.027.View ArticlePubMedGoogle Scholar
  16. Wangaryattawanich P, Hatami M, Wang J, Thomas G, Flanders A, Kirby J, et al. Multicenter imaging outcomes study of the Cancer genome atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival. Neuro-Oncology. 2015;17(11):1525–37. https://doi.org/10.1093/neuonc/nov117.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Wang K, Wang Y, Fan X, Wang J, Li G, Ma J, et al. Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients. Neuro-Oncology. 2016;18(4):589–97. https://doi.org/10.1093/neuonc/nov239.View ArticlePubMedGoogle Scholar
  18. Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ. Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg. 1987;66(6):865–74. https://doi.org/10.3171/jns.1987.66.6.0865.View ArticlePubMedGoogle Scholar
  19. Grosu AL, Weber WA. PET for radiation treatment planning of brain tumours. Radiother Oncol. 2010;96(3):325–7. https://doi.org/10.1016/j.radonc.2010.08.001.View ArticlePubMedGoogle Scholar
  20. Langen KJ, Stoffels G, Filss C, Heinzel A, Stegmayr C, Lohmann P, et al. Imaging of amino acid transport in brain tumours: positron emission tomography with O-(2-[18F]fluoroethyl)-L-tyrosine (FET). Methods. 2017;130:124–34. https://doi.org/10.1016/j.ymeth.2017.05.019.View ArticlePubMedGoogle Scholar
  21. Jansen NL, Suchorska B, Wenter V, Schmid-Tannwald C, Todica A, Eigenbrod S, et al. Prognostic significance of dynamic 18F-FET PET in newly diagnosed astrocytic high-grade glioma. J Nucl Med. 2015;56(1):9–15. https://doi.org/10.2967/jnumed.114.144675.View ArticlePubMedGoogle Scholar
  22. Jansen NL, Suchorska B, Wenter V, Eigenbrod S, Schmid-Tannwald C, Zwergal A, et al. Dynamic 18F-FET PET in newly diagnosed astrocytic low-grade glioma identifies high-risk patients. J Nucl Med. 2014;55(2):198–203. https://doi.org/10.2967/jnumed.113.122333.View ArticlePubMedGoogle Scholar
  23. Suchorska B, Giese A, Biczok A, Unterrainer M, Weller M, Drexler M, et al. Identification of time-to-peak on dynamic 18F-FET-PET as a prognostic marker specifically in IDH1/2 mutant diffuse astrocytoma. Neuro-Oncology. 2017. https://doi.org/10.1093/neuonc/nox153.
  24. Calcagni ML, Galli G, Giordano A, Taralli S, Anile C, Niesen A, et al. Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (F-18 FET) PET for glioma grading: assessment of individual probability of malignancy. Clin Nucl Med. 2011;36(10):841–7. https://doi.org/10.1097/RLU.0b013e3182291b40.View ArticlePubMedGoogle Scholar
  25. Pöpperl G, Kreth FW, Mehrkens JH, Herms J, Seelos K, Koch W, et al. FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading. Eur J Nucl Med Mol Imaging. 2007;34(12):1933–42.View ArticleGoogle Scholar
  26. Albert NL, Winkelmann I, Suchorska B, Wenter V, Schmid-Tannwald C, Mille E, et al. Early static (18)F-FET-PET scans have a higher accuracy for glioma grading than the standard 20-40 min scans. Eur J Nucl Med Mol Imaging. 2016;43(6):1105–14. https://doi.org/10.1007/s00259-015-3276-2.View ArticlePubMedGoogle Scholar
  27. Lohmann P, Herzog H, Rota Kops E, Stoffels G, Judov N, Filss C. E al. Dual-time-point O-(2-[(18)F]fluoroethyl)-L-tyrosine PET for grading of cerebral gliomas. Eur Radiol. 2015;25(10):3017–24. https://doi.org/10.1007/s00330-015-3691-6.View ArticlePubMedGoogle Scholar
  28. Harat M, Małkowski B, Makarewicz R. Pre-irradiation tumour volumes defined by MRI and dual time-point FET-PET for the prediction of glioblastoma multiforme recurrence: a prospective study. Radiother Oncol. 2016;120(2):241–7. https://doi.org/10.1016/j.radonc.2016.06.004.View ArticlePubMedGoogle Scholar
  29. Ellingson BM, Wen PY, Cloughesy TF. Modified criteria for radiographic response assessment in glioblastoma clinical trials. Neurotherapeutics. 2017;14(2):307–20.View ArticleGoogle Scholar
  30. Hamacher K, Coenen HH. Efficient routine production of the 18F-labelled amino acid O-2-18F fluoroethyl-L-tyrosine. Appl Radiat Isot. 2002;57:853–6.View ArticleGoogle Scholar
  31. Langen K-J, Stoffels G, Filß C, Heinzel A, Stegmayr C, Lohmann P, Willuweit A, Neumaier B, Mottaghy FM, Galldiks N. Imaging of amino acid transport in brain tumours: positron emission tomography with O-(2-[(18)F]fluoroethyl)-l-tyrosine (FET) Methods San Diego Calif; 2017.Google Scholar
  32. Pauleit D, Floeth F, Hamacher K, Riemenschneider MJ, Reifenberger G, Müller HW, et al. O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas. Brain. 2005;128:678–87. https://doi.org/10.1093/brain/awh399.View ArticlePubMedGoogle Scholar
  33. Liang HT, Chen WY, Lai SF, Su MY, You SL, Chen LH, et al. The extent of edema and tumor synchronous invasion into the subventricular zone and corpus callosum classify outcomes and radiotherapy strategies of glioblastomas. Radiother Oncol. 2017;125(2):248–57. https://doi.org/10.1016/j.radonc.2017.09.024.View ArticlePubMedGoogle Scholar
  34. Malkowski B, Harat M, Zyromska A, et al. The Sum of Tumour-to-Brain Ratios Improves the Accuracy of Diagnosing Gliomas Using 18F-FET PET. PLoS One. 2015;10(10):e0140917. Published 2015 Oct 15. https://doi.org/10.1371/journal.pone.0140917.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Galldiks N, Dunkl V, Kracht LW, Vollmar S, Jacobs AH, Fink GR, et al. Volumetry of [11C]-methionine positron emission tomographic uptake as a prognostic marker before treatment of patients with malignant glioma. Mol Imaging. 2012;11(6):516–27.View ArticleGoogle Scholar
  36. Colavolpe C, Metellus P, Mancini J, Barrie M, Béquet-Boucard C, Figarella-Branger D, et al. Independent prognostic value of pre-treatment 18-FDG-PET in high-grade gliomas. J Neuro-Oncol. 2012;107(3):527–35. https://doi.org/10.1007/s11060-011-0771-6.View ArticleGoogle Scholar
  37. Spence AM, Muzi M, Swanson KR, O'Sullivan F, Rockhill JK, Rajendran JG, et al. Regional hypoxia in glioblastoma multiforme quantified with [18F]fluoromisonidazole positron emission tomography before radiotherapy: correlation with time to progression and survival. Clin Cancer Res. 2008;14(9):2623–30. https://doi.org/10.1158/1078-0432.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Suchorska B, Jansen NL, Linn J, Kretzschmar H, Janssen H, Eigenbrod S, et al. Biological tumor volume in 18FET-PET before radiochemotherapy correlates with survival in GBM. Neurology. 2015;84(7):710–9. https://doi.org/10.1212/WNL.0000000000001262.View ArticlePubMedGoogle Scholar
  39. Wang L, Lieberman BP, Ploessl K, Kung HF. Synthesis and evaluation of 18F labeled FET prodrugs for tumor imaging. Nucl Med Biol. 2014;41(1):58–67. https://doi.org/10.1016/j.nucmedbio.2013.09.011.View ArticlePubMedGoogle Scholar
  40. Galldiks N, Stoffels G, Filss C, Rapp M, Blau T, Tscherpel C, et al. The use of dynamic O-(2-18F-fluoroethyl)-l-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma. Neuro-Oncology. 2015;17(9):1293–300. https://doi.org/10.1093/neuonc/nov088.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Grosu AL, Astner ST, Riedel E, Nieder C, Wiedenmann N, Heinemann F, et al. An interindividual comparison of O-(2-[18F]fluoroethyl)-L-tyrosine (FET)- and L-[methyl-11C]methionine (MET)-PET in patients with brain gliomas and metastases. Int J Radiat Oncol Biol Phys. 2011;81(4):1049–58. https://doi.org/10.1016/j.ijrobp.2010.07.002.E.View ArticlePubMedGoogle Scholar
  42. Brown TJ, Brennan MC, Li M, Church EW, Brandmeir NJ, Rakszawski KL, et al. Association of the extent of resection with survival in glioblastoma: a systematic review and meta-analysis. JAMA Oncol. 2016;2(11):1460–9. https://doi.org/10.1001/jamaoncol.2016.1373.View ArticlePubMedGoogle Scholar
  43. Li YM, Suki D, Hess K, Sawaya R. The influence of maximum safe resection of glioblastoma on survival in 1229 patients: can we do better than gross-total resection? J Neurosurg. 2016;124(4):977–88. https://doi.org/10.3171/2015.5.JNS142087.View ArticlePubMedGoogle Scholar
  44. Grabowski MM, Recinos PF, Nowacki AS, Schroeder JL, Angelov L, Barnett GH, et al. Residual tumor volume versus extent of resection: predictors of survival after surgery for glioblastoma. J Neurosurg. 2014;121(5):1115–23. https://doi.org/10.3171/2014.7.JNS132449.View ArticlePubMedGoogle Scholar
  45. Moller S, Law I, Munck AF, Rosenschold P, Costa J, Poulsen HS, et al. Prognostic value of 18F-FET PET imaging in re-irradiation of high-grade glioma: results of a phase I clinical trial. Radiother Oncol. 2016;121(1):132–7. https://doi.org/10.1016/j.radonc.2016.08.014.View ArticlePubMedGoogle Scholar

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