Multimodal imaging in cerebral gliomas and its neuropathological correlation

European Journal of Radiology 83 (2014) 829–834

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European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad

Multimodal imaging in cerebral gliomas and its neuropathological correlation Jens Gempta,∗,1 , Eric Soehngenb,d,1 , Stefan Försterc , Yu-Mi Ryanga , Jürgen Schlegeld , Claus Zimmerb , Bernhard Meyera , Florian Ringela , Astrid E. Gramsb,e , Annette Förschlerb a

Neurochirurgische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 München, Germany Abteilung für Neuroradiologie, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 München, Germany c Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Ismaninger Str. 22, 81675 München, Germany d Abteilung für Neuropathologie des Instituts für Allgemeine Pathologie und Pathologische Anatomie, Technische Universität München, Ismaninger Str. 22, 81675 München, Germany e Universitätsklinik für Neuroradiologie, Department Radiologie, Medizinische Universität Innsbruck, Anichstraße 35, A-6020 Innsbruck, Austria b

a r t i c l e

i n f o

Article history: Received 21 January 2014 Accepted 4 February 2014 Keywords: Glioma FET-PET MR-S Proton magnetic resonance spectroscopic imaging Brain tumor MIB-1

a b s t r a c t Introduction: Concerning the preoperative clinical diagnostic work-up of glioma patients, tumor heterogeneity challenges the oncological therapy. The current study assesses the performance of a multimodal imaging approach to differentiate between areas in malignant gliomas and to investigate the extent to which such a combinatorial imaging approach might predict the underlying histology. Methods: Prior to surgical resection, patients harboring intracranial gliomas underwent MRIs (MR-S, PWI) and 18 F-FET-PETs. Intratumoral and peritumoral biopsy targets were defined, by MRI only, by FET-PET only, and by MRI and FET-PET combined, and biopsied prior to surgical resection and which then received separate histopathological examinations. Results: In total, 38 tissue samples were acquired (seven glioblastomas, one anaplastic astrocytoma, one anaplastic oligoastrocytoma, one diffuse astrocytoma, and one oligoastrocytoma) and underwent histopathological analysis. The highest mean values of Mib1 and CD31 were found in the target point “T’ defined by MRI and FET-PET combined. A significant correlation between NAA/Cr and PET tracer uptake (−0.845, p < 0.05) as well as Cho/Cr ratio and cell density (0.742, p < 0.05) and NAA/Cr ratio and MIB-1 (−0761, p < 0.05) was disclosed for this target point, though not for target points defined by MRI and FET-PET alone. Conclusion: Multimodal-imaging-guided stereotactic biopsy correlated more with histological malignancy indices, such as cell density and MIB-1 labeling, than targets that were based solely on the highest amino acid uptake or contrast enhancement on MRI. The results of our study indicate that a combined PET-MR multimodal imaging approach bears potential benefits in detecting glioma heterogeneity. © 2014 Elsevier Ireland Ltd. All rights reserved.

1. Introduction High-grade glial tumors characteristically exhibit extensive and aberrant microvessel proliferation, perifocal edema, widespread tissue invasion, and necrotic foci, thus giving rise to a pronounced heterogeneous appearance in imaging and microscopy [2]. Regarding the preoperative clinical diagnostic work-up of glioma patients, tumor heterogeneity challenges the planning of stereotactic biopsy and tumor resection. These diagnostics are constrained by the lack

∗ Corresponding author. Tel.: +49 89 4140 2151; fax: +49 89 4140 4889. E-mail address: [email protected] (J. Gempt). 1 Both authors contributed equally to this work. http://dx.doi.org/10.1016/j.ejrad.2014.02.006 0720-048X/© 2014 Elsevier Ireland Ltd. All rights reserved.

of precise information about actual tumor extension, proliferation capacity and underlying histology, which can strongly vary, even within a single tumor. Guiding neuronavigated biopsies toward regions with the highest malignancy grade is mandatory, to avoid under-grading and to enable adequate therapy and concise prognosis [3]. Postoperatively, resection control, planning, and assessment of radiotherapy and chemotherapy are considerably impeded by the difficulty in differentiating reliably between therapeutically caused tissue changes, tumor remnants, and disease recurrence, as necrotic and proliferative parts can be found in close proximity. Numerous studies have demonstrated that the employment of novel imaging techniques in magnetic resonance imaging (MRI), such as proton MRspectroscopy (MRS) and MR perfusion, as well

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as molecular imaging, such as 18 F-fluoro-etyhl-tyrosine positron emission tomography (18 F-FET-PET), can greatly increase diagnostic accuracy and tissue differentiability in uncertain cases [4–6]. The purpose of the current study was to assess the performance of a multimodal imaging approach, including conventional MRI, MRS, PWI and 18 F-FET-PET, in order to determine whether advanced MR sequences in combination with 18 F-FET-PET are more capable of differentiating between histologically different areas in malignant gliomas than the imaging modalities when used alone; and secondly, to investigate the extent to which such a combinatorial imaging approach predicts the underlying histology. 2. Methods The authors of this study wrote to the ethics committee, approval was granted and informed consent from all patients was obtained. All patients suffered from newly diagnosed brain lesions and had not had any prior surgery, radiotherapy or chemotherapy. Prior to stereotactic biopsy and surgical resection, patients underwent MRIs and 18 F-FET-PETs. Up to eight intratumoral and peritumoral biopsy targets were defined by an experienced, boardcertified neurosurgeon, a neuroradiologist, and a nuclear medicine specialist. 2.1. Magnetic resonance imaging acquisition All MRI examinations were performed on a 3 tesla MRI (Achieva, Philips Medical, the Netherlands). Patients received axial T2 FLAIR (TR: 1200 ms, TE: 140 ms, FOV: 230 (ap) × 230 (rl) mm × 134 (fh) mm, 30 slices, voxel size: 0.9 mm× 0.9 mm × 4 mm, acquisition time: 3:00 min) and post contrast T1w 3D MPRAGE (TR: 9 ms, TE: 4 ms, FOV 240 (ap) × 240 (rl) × 160 (fh) mm, voxel size: 1 mm3 , acquisition time: 5:56 min). For contrast, Magnograf® (MaRoTrast, Jena, Germany) was administered intravenously (0.2 ml/kg, 0.1 mmol/kg and 4 ml/s), using an MR-compatible contrast medium injection system (Spectris Solaris EP, Siemens Medical, Erlangen, Germany). 3D 1H-MR spectroscopy (PRESS, TR: 1669 ms, TE: 288 ms, FOV: 200 (ap) × 176 (rl) × 72 (fh) mm, 10 mm isovoxel, acquisition time: 15:55 min) covered the tumor, the peritumoral edema and the contralateral healthy hemisphere. Post-processing was performed on an Extended MR workspace workstation (Philips Medical, the Netherlands). Residual water subtraction, apodization filtering, zero filling, and fusion with the anatomical data were done with the software, SpektroView (Philips Medical Systems). All metabolite ratios were described as regional ratios in comparison with healthy contralateral hemispheres. For dynamic susceptibility-weighted perfusion MRI, we used a 3D EPI sequence (TR: 17 ms, TE: 8 ms, FOV: 230 (ap) mm × 184 (rl) mm × 120 mm, 30 slices, voxel size: 2.88 mm × 2.88 mm × 4 mm, acquisition time: 1:19 min). CBV maps were post-processed with the software, NeuroPerfusion (Philips Medical, the Netherlands). 2.2.

18 F-FET-PET

imaging acquisition

All 18 F-FET PET scans were obtained on an ECAT EXACT HR+ scanner (Siemens). To ensure standardized metabolic conditions, patients were asked to fast for at least 6 h before PET scanning. The PET scanner acquires 63 contiguous transaxial planes, simultaneously covering 15.5 cm of axial field of view. After a 15-min transmission scan (68Ge rod sources), a target dose of 185 MBq (±10%) 18F-FET was injected intravenously. PET emission scans in 3D-mode were obtained 30–40 min after injection (128 · 128 matrix). Then, one 10-min static frame was reconstructed with filtered back-projection using a Hann filter after correction for scatter and attenuation. For PET image analysis, reconstructed PET data

Fig. 1. MRI, PET-Overlay and HE stains, Upper row shows corresponding MRI, MRIPET Fusion and a histopathological stain of a target point “CI” in one illustrative case of a 66-year-old female patient harboring a glioblastoma. (A) T1 contrast enhanced MRI with peri-tumoral biopsy target, (B) corresponding MRI PWI, (C) corresponding MR Spectroscopy with for infiltration suspicious metabolite ratios: NAA/Cr (2,4) and Cho/Cr (1,4), (D) corresponding MR PET Fusion, (E) corresponding HE stain with increased glial density. Lower row (F–J) shows corresponding MRI, MRI-PET Fusion and a histopathological stain of a target point “T” in one illustrative case of a 69-year-old male patient with glioblastoma. (A): T1 contrast enhanced MRI with center-tumoral biopsy target, (B): corresponding MRI PWI, (C): corresponding MR Spectroscopy with tumor suspicious metabolite ratios: NAA/Cr (1,4) and Cho/Cr (2,4), (D): corresponding MR PET Fusion, (E): corresponding HE stain with markedly increased glial density, vascular proliferation and hemorrhage.

were imported in BrainLAB iPlan® Net Cranial 3.0.1 (BrainLAB AG, Feldkirchen, Germany). PET images were processed using BrainLab iPlan 3.0 cranial planning software. With the latter application, standardized uptake value (SUV) ratios, relating counts in a tumor VOI to the respective counts in a background VOI and derived from a cortical region in the opposite non–tumor-bearing hemisphere, were calculated in a manner that was similar to previous studies [7]. 2.3. Definition of biopsy targets Imaging data were transferred to BrainLAB iPlan® Net Cranial 3.0.1 (BrainLAB AG, Feldkirchen, Germany). Using iPlan® -Net, reconstructed PET images were coregistered to MR data and up to eight intratumoral and peritumoral biopsy targets were defined as follows (Table 1 and Fig. 1): a) MRI-only-defined targets were “edema” (“E.” T2 hyperintensity, no contrast enhancement, no elevation of Cho/Cr ratio and CBV), “contrast enhancement” (“CE,” T2 hyperintensity and pronounced hyperintensity in T1w contrast enhanced MRI), “necrosis” (“N,” non-enhancing center of the tumor with decreased rCBV), “cellular infiltration” (“CI,” T2 hyperintensity, no contrast enhancement, increased Cho/Cr ratios, unaltered rCBV), and “vascular proliferation” (“VP,” increased rCBV and T2 hyperintensity, neither contrast enhancement nor elevated Cho/NAA). b) 18 F-FET-PET-only-defined targets were the areas with maximal (“Pmax”) and minimal (“Pmin”) pathological amino acid uptake. Tumor-to-background (T/B) ratios higher than 1.6 were considered pathological. c) MRI and 18 F-FET-PET combined base target was “tumor” (“T,” T2 hyperintensity, contrast enhancement, increased Cho/Cr ratios, rCBV, and pathological T/B ratios in 18 F-FET-PET). 2.4. Stereotactic biopsy Heads were fixed with a standard 3-point head clamp (Doro cranial stabilization, pro med instruments GmbH, Freiburg im Breisgau, Germany), and the neuronavigation reference star was attached to the head clamp. Registration of navigation was conducted by laser-pointer surface registration (Z-touch, BrainLAB AG, Feldkirchen, Germany). After registration, the stereotactic biopsy arm was attached. The skin was incised followed by the creation

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Table 1 Imaging criteria for the identification of the different tumor components and for the selection of biopsy targets. Tumor component

T2 appearance

Extravasation of contrast

Cho/Cr

rCBV

18F-FET-PET

Edema (E) Cellular infiltration (CI) Vascular Proliferation (VP) Necrosis (N) Maximum Contrast enhancement (CE) Tumor (T) Area of maximal tracer uptake (Pmax) Area of tracer uptake just above threshold (Pmin)

Hyperintense Hyperintense Hyperintense Hyperintense Hyperintense Hyperintense – –

None No/slight No/slight None Marked Yes – –

Not altered Markedly increased Not altered Any Any Increased – –

Not altered Not altered Increased Decreased Any Increased – –

– – – – – Increased Markedly increased Slightly increased

of the burr hole necessary for the craniotomy. The burr hole was defined as the biopsy entrance point and trajectories to the predefined target points were created intraoperatively. The biopsy arm was then aligned to the respective trajectories, and biopsies of the target areas were conducted using the frameless stereotactic system as described previously [8]. Hereafter, tumor craniotomy and tumor resection were carried out.

For each biopsy target “T,” “Pmax,” and “CE,” the histological measures of cell density, “MIB-1” and CD31 labeling were correlated to the T/B, Cho/Cr, and NAA/Cr ratios.

2.5. Neuropathological and immunohistochemical analysis

2.7. Statistical analysis

All biopsy specimens were labeled with consecutive numbers to allow for blinded diagnostic work-up. Tumor grading was performed according to the WHO 2007 glioma classification. All specimens were fixated in 10% formalin, and afterwards embedded in paraffin. Hematoxylin and eosin staining was performed on paraffin-embedded specimens with HE stain (Mayers HE, AppliChem, Darmstadt, Germany). Samples that were subjected to immunohistochemical analysis were heated for 20 min to allow antigen-activation in a sodium citrate solution (0.15 mol/l) before staining. MIB-1 labeling was performed with an MIB-1 antibody (MIB-1 antibody anti-Ki67, 1:50, Dako, Hamburg, Germany). The ratio of labeled and unlabeled nuclei was represented as a percentage (MIB-1 labeling index). CD31 labeling was performed with JC70a (1:50, Dako, Hamburg, Germany). Nucleus and microvessel counting was performed on microscopic photos that were captured at 200× and 400× magnification, representing an area of 2 mm2 using a Zeiss Axioskop (Zeiss Microsystems, Jena, Germany) equipped with a phototube and digital color camera-mounting (Canon PowerShot, 5 megapixels). HE and immunostained nuclei were counted using a free ImageJ software plug-in (ImmunoRatio) and built-in ImageJ algorithms that allowed for semi-automatic and standardized nucleus counting [9]. Mean values of three processed fields of view were calculated. Microvessel density was measured using the same approach as described for nucleus counting, but instead of the total number of nuclei, the area of CD31-stained vessels was calculated and represented as a percentage.

All statistical analyses were performed with SPSS (Version 19.0, IBM, New York, U.S.A.). Pearson’ correlations were calculated and significance levels were tested for p = 0.05.

2.6. Data 1. In order to elucidate the underlying histology of both imaging characteristics of the biopsy targets (“T,” “Pmax,” “Pmin,” “CI,” “E,” “CE,” “N,” and “VP”), the mean values of MIB-1, cell density and CD31 measures were assessed. 2. The target points “CE,” “Pmax,” and “Pmin” were compared regarding their mean values of Cho/Cr. 3. All target areas (“T”, “Pmax,” “Pmin,” “CI,” “E,” “CE,” “N,” and “VP”) were compared regarding their mean NAA/Cr ratio. 4. In order to enable a direct comparison with the multimodal imaging approach, using MRS, PWI and 18 F-FET-PET in one group, the functional approach using 18 F-FET-PET in the other group, and the morphological approach using contrast enhancing MRI only, we compared the biopsy target point “T” that considered

findings from all involved modalities, the biopsy target point “Pmax” as a representation of the 18 F-FET-PET only approach, and the biopsy point “CE.”

2.8. Ethics Clinical Trial Registration Number: 2038/08. The present study has been approved by the appropriate ethics committee and has therefore been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. 3. Results The study included 11 patients (median age 59 years, range 23–82 years, five female, six male). Histologies revealed seven glioblastomas, one anaplastic astrocytoma, one anaplastic oligoastrocytoma, one diffuse astrocytoma, and one oligoastrocytoma. The mean time from the MRI scan to surgery was 11 days (±13.31); the mean time from the FET-PET scan to surgery was 11 days (±10.14). In total, 38 tissue samples were acquired and underwent histopathological analysis. For the biopsy target “Pmax,” specimens with corresponding histology could be obtained from 7 out of 11 patients (64%), for the target point “T,” and specimens could be acquired from 6 out of 11 patients (54%). 1. The results of the mean values of MIB-1, cell density, and CD31 in the different defined biopsy targets can be found in Fig. 2. All values below are expressed as mean values with range and standard deviation. Concerning the mean values of MIB-1 (Fig. 2, A): “T”: 4.0% (±1.10, 3–6%) and “Pmax”: 3.9 3% (±2.21, 0.5–6%) displayed the highest values, ahead of “CE” areas: 2.75% (±1.50, 1–4%). The lowest MIB-1 value was found in areas of “VP” with 0.5% (±0.5, 0–1%) and “Pmin”: 0.8% (±0.27, 0.5–1%). The highest CD 31 measures (Fig. 2, B) were displayed in “T” with 101 (±32.24, 52–145). “CE” had a mean value of 78 (±41.00, 27–124). “VP” presented a mean of 73 (±91.80, 19–179). The lowest CD31 values were observed for “E,” with a mean of 19.5 (±0.71, 19–20).

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Fig. 2. The results of the mean values of Mib1 (A), cell density (C), CD31 (B), and in the different defined biopsy are displayed. All values below are expressed as mean values. Regarding Mib1 (A) and CD31 (B), “T” displays the highest mean values. Regarding cell density, the highest values were found for “CE”.

Cell density counts (Fig. 2, C) were found to be highest in “CE,” with a mean of 1431 nuclei/FOV (±673.63, 498–1973 nuclei/FOV), followed by “Pmax”: 1245 nuclei/FOV (±685.00, 331–2534 nuclei/FOV), “E”: 1278 nuclei/FOV (±569.22, 875–1680 nuclei/FOV), and “T”: 1165 nuclei/FOV (±734–1439 nuclei/FOV). “N” revealed the lowest values: 162 nuclei/FOV (±16.26, 150–173 nuclei/FOV) followed by “Pmin”: 649 nuclei/FOV (±142.50, 455–803 nuclei/FOV). 2. “Pmax” displayed a higher Cho/Cr ratio (Fig. 3A) of 2.78 (±1.47, 1.19–5.60), in comparison to “CE” (1.98 ± 0.90, 0.78–2.92) and “Pmin” (1.12 ± 0.49, 0.63–1.77). 3. “CE” displayed the least NAA/Cr ratio (0.97 ± 0.23, 0.76–1.29) followed by “Pmax” (1.09 ± 0.69, 0.32–2.08). The highest NAA/Cr ratio (Fig. 4) was seen in “VP” (2.55 ± 0.78, 1.76–3.32). 4. Significant correlations (Pearson’ correlations, two-tailed) were observed only in the target point “T” (Fig. 4).

A significant negative correlation between NAA/Cr and T/B ratio (Fig. 4A) was found (−0.845, p < 0.05) as well as between Cho/Cr and cell density (0.742, p < 0.05, see Fig. 4B) and between NAA/Cr ratio and Mib1 (−0761, p < 0.05, see Fig. 4C). For “CE” and “Pmax,” no significant correlation was disclosed for our tissue samples. However, some of these correlations revealed statistical trends: Pearson coefficients for T/B ratio versus cell density of respective specimens for “T” were 0.438 and 0.245 for “Pmax.” A tendency with

regard to increased MIB-1 values with a Pearson coefficient of 0.516 was disclosed for “T” and 0.145 for “Pmax.”

4. Discussion In this study, we aimed at evaluating the use of multimodal imaging to define tumor components in heterogeneous intrinsic brain tumors. 1H-MRS, PWI and 18 F-FET-PET in combination with conventional MRI sequences were conducted to define regions of interest in heterogeneous gliomas, which were preoperatively biopsied and histologically validated. We differentiated the tumor components in areas defined by conventional MRI only (“CE”), by additional Perfusion and spectroscopic MRI (“E,” “CI,” “VP”), by molecular imaging only (“Pmin,” “Pmax”), and by all modalities together (“T”). Regarding tumor grading, real tumor extension, or the differentiation of tumor borders and areas of high malignancy, the different imaging modalities showed different advantages and disadvantages. The limitations in the abilities of contrast enhancement in conventional MRI to reliably distinguish between areas of inflammation, necrosis, therapy-induced effects, and tumor-associated disruption of the blood–brain barrier are well known [10]. Several comparison studies have also proven that a considerable number of non-enhanced gliomas show, in fact, a higher-grade morphology in histological sampling, suggesting a possible underdiagnosis in patients with putative low-grade glioma, if establishment of diagnosis by biopsy/operation and subsequent therapy are based exclusively on conventional contrast MRI [11,12]. On the other

Fig. 3. (A) Measurements of the Cho/Cr ratio in the tumor components that were selected either by PET or by morphological imaging. “Pmax” showed the highest mean value. (B) Measurements of the NAA/Cr ratio in the different tumor components. The lowest mean value was found for “CE”.

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Fig. 4. Correlations in the target point “T.” A significant negative correlation between NAA/Cr and T/B ratio (A) is displayed (−0.845, p < 0.05) as well as a positive correlation between Cho/Cr and cell density (0.742, p < 0.05, B) and a negative correlation between NAA/Cr ratio and Mib1 (−0761, p < 0.05, C).

hand, conventional MRI offers the excellent spatial resolution that is necessary for surgery and radiotherapy. Radiolabeled amino acids or amino acid-like agents, in this case, 18 F-FET-PET, are increasingly used for brain tumor imaging. Several studies on therapy monitoring, grading, patient prognosis and outcome, and the detection of tumor recurrence indicate its strengths and advantages compared to structural MRI alone [13–15]. 18 F-FETPET is known to be incorporated relatively specifically by tumor cells via an amino acid carrier system; however, disruption of the blood–brain barrier and other yet unidentified factors may influence 18 F-FET-PET uptake in certain cases [16,17]. By experimenting with dynamic imaging methods, several studies have attempted to improve the accuracy and value of 18 FFET-imaging in terms of tumor grading and the differentiation of tumor progression and therapy associated changes. Especially with regard to therapy monitoring, dynamic imaging could bear potential advantages [18–20]. Proton magnetic resonance spectroscopic imaging also provides information on changes in metabolite patterns compared to normal brain tissue [21–23]. In a clinical setting, proton magnetic resonance spectroscopic imaging has an influence on therapy planning and therapy monitoring as well, although it has been evaluated primarily in cohort studies with a limited number of patients [24–26]. Since methodological limitations exist for all imaging modalities, the different aspects of glioma diagnostics might be improved with a combination of different modalities. In our patients, the highest rate of proliferation, indicated by MIB-1, was found at target point “T,” followed by “Pmax” and “CE.” Areas defined as “VP” and “Pmin” displayed the lowest proliferation. In other studies on glioma grading, MIB-1 was found to be a parameter correlating with tumor malignancy [27,28]. The target area defined as “T,” therefore, displays the highest rate of malignancy among our target points with regard to proliferation. Cell density counts were found to be highest in “CE,” “Pmax,” and “T.” “N” revealed the lowest values, followed by “Pmin.” Stockhammer et al. discovered a correlation between PET uptake and cell density in their biopsy study in patients with noncontrast-enhancing gliomas, but a correlation with MIB-1 was not reproduced [29]. The highest CD 31 measures were displayed in “T,” “CE,” and “VP.” The lowest CD31 values were observed in “E.” Concerning histopathological diagnosis, surgical therapy, and radiotherapy, knowledge about spots of high proliferation bears clinical importance. In spite of our limited number of patients, we observed a significant correlation between imaging and histology measures in the biopsy target point “T,” which was selected in all of the imaging

modalities. Therefore, these findings speak in favor of using a multimodal imaging setting for biopsy target selection. Additionally, “T” revealed a correlation of T/N and NAA/Cr ratio. Other studies on the comparison of FET-PET and proton magnetic resonance spectroscopic imaging also found correlations between certain spectroscopic metabolites and FET-PET uptake in different tumor components, although this was not validated by histology [30]. Stadlbauer and co-workers disclosed a positive correlation of Cho/NAA and tracer uptake in all areas of increased FET uptake and a negative correlation of NAA and tracer uptake only in areas of maximum FET uptake, thereby also indicating the relevance of different tumor components and the ability of multimodal imaging to visualize them [30]. 5. Conclusions In the current study, we have shown that multimodalimaging-guided stereotactic biopsy has a stronger correlation with histological malignancy indices, such as cell density and MIB-1 labeling, than targets that were based on imaging modalities used alone. Furthermore, we observed that elevated Cho/Cr ratios are more closely associated with cellular density, rather than higher Mib1 labeling indices, and we have shown that regions with the highest contrast enhancement frequently revealed tissue samples that were less malignant than those that were targeted under the additional consideration of 1H-MR spectroscopy, PWI, and 18 F-FETPET. Finally, we were able to identify infiltrative tumor zones using this multimodal approach. The results of most studies indicate that multimodal imaging approaches are valuable clinical tools, bearing potential benefits in differential diagnosis and tumor recurrence, biopsy guidance, planning of surgical resection, radiation therapy, and assessment of therapy response. Nonetheless, multimodal imaging protocols also typically involve logistic and financial challenges, with higher costs, scanners being located in different clinical departments, and reliance on manual or semi-automatic image processing and fusion. Competing interests The authors declare that they have no competing interests. References [2] Paulus W, Peiffer J. Intratumoral histologic heterogeneity of gliomas. A quantitative study. Cancer 1989;64(2):442–7. [3] Jackson RJ, Fuller GN, Abi-Said D, et al. Limitations of stereotactic biopsy in the initial management of gliomas. Neuro Oncol 2001;3(3):193–200. [4] Okita Y, Kinoshita M, Goto T, et al. (11)C-methionine uptake correlates with tumor cell density rather than with microvessel density in glioma: a stereotactic image-histology comparison. Neuroimage 2010;49(4):2977–82.

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J. Gempt et al. / European Journal of Radiology 83 (2014) 829–834

[5] Tsien CI, Cao Y, Lawrence TS. Functional and metabolic magnetic resonance imaging and positron emission tomography for tumor volume definition in high-grade gliomas. Semin Radiat Oncol 2009;19(3):155–62. [6] Sood S, Gupta A, Tsiouris AJ. Advanced magnetic resonance techniques in neuroimaging: diffusion, spectroscopy, and perfusion. Semin Roentgenol 2010;45(2):137–46. [7] Popperl G, Gotz C, Rachinger W, Gildehaus FJ, Tonn JC, Tatsch K. Value of O(2-[18F]fluoroethyl)-l-tyrosine PET for the diagnosis of recurrent glioma. Eur J Nucl Med Mol Imaging 2004;31(11):1464–70. [8] Gempt J, Buchmann N, Ryang YM, et al. Frameless image-guided stereotaxy with real-time visual feedback for brain biopsy. Acta Neurochir (Wien) 2012;154(9):1663–7. [9] Tuominen VJ, Ruotoistenmaki S, Viitanen A, Jumppanen M, Isola J. Immuno ratio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67. Breast Cancer Res 2010;12(4):R56. [10] Faehndrich J, Weidauer S, Pilatus U, Oszvald A, Zanella FE, Hattingen E. Neuroradiological viewpoint on the diagnostics of space-occupying brain lesions. Clin Neuroradiol 2011;21(3):123–39. [11] Ginsberg LE, Fuller GN, Hashmi M, Leeds NE, Schomer DF. The significance of lack of MR contrast enhancement of supratentorial brain tumors in adults: histopathological evaluation of a series. Surg Neurol 1998;49(4):436–40. [12] Scott JN, Brasher PM, Sevick RJ, Rewcastle NB, Forsyth PA. How often are nonenhancing supratentorial gliomas malignant? A population study. Neurology 2002;59(6):947–9. [13] Calcagni ML, Galli G, Giordano A, et al. Dynamic O-(2-[18F]fluoroethyl)-ltyrosine (F-18 FET) PET for glioma grading: assessment of individual probability of malignancy. Clin Nucl Med 2011;36(10):841–7. [14] Floeth FW, Sabel M, Ewelt C, et al. Comparison of (18)F-FET PET and 5-ALA fluorescence in cerebral gliomas. Eur J Nucl Med Mol Imaging 2011;38(4): 731–41. [15] Hustinx R, Pourdehnad M, Kaschten B, Alavi A. PET imaging for differentiating recurrent brain tumor from radiation necrosis. Radiol Clin North Am 2005;43(1):35–47. [16] Wester HJ, Herz M, Weber W, et al. Synthesis and radiopharmacology of O-(2-[18F]fluoroethyl)-l-tyrosine for tumor imaging. J Nucl Med 1999;40(1):205–12. [17] Heiss P, Mayer S, Herz M, Wester HJ, Schwaiger M, Senekowitsch-Schmidtke R. Investigation of transport mechanism and uptake kinetics of O-(2[18F]fluoroethyl)-l-tyrosine in vitro and in vivo. J Nucl Med 1999;40(8):1367– 73.

[18] Jansen NL, Schwartz C, Graute V, et al. Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [(18)F]FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro Oncol 2012;14(12):1473–80. [19] Niyazi M, Jansen N, Ganswindt U, et al. Re-irradiation in recurrent malignant glioma: prognostic value of [18F]FET-PET. J Neurooncol 2012;110(3):389–95. [20] Galldiks N, Rapp M, Stoffels G, et al. Response assessment of bevacizumab in patients with recurrent malignant glioma using [18F]Fluoroethyl-l-tyrosine PET in comparison to MRI. Eur J Nucl Med Mol Imaging 2013;40(1):22–33. [21] Burtscher IM, Skagerberg G, Geijer B, Englund E, Stahlberg F, Holtas S. Proton MR spectroscopy and preoperative diagnostic accuracy: an evaluation of intracranial mass lesions characterized by stereotactic biopsy findings. AJNR Am J Neuroradiol 2000;21(1):84–93. [22] Shimizu H, Kumabe T, Shirane R, Yoshimoto T. Correlation between choline level measured by proton MR spectroscopy and Ki-67 labeling index in gliomas. AJNR Am J Neuroradiol 2000;21(4):659–65. [23] Widhalm G, Krssak M, Minchev G, et al. Value of 1H-magnetic resonance spectroscopy chemical shift imaging for detection of anaplastic foci in diffusely infiltrating gliomas with non-significant contrast-enhancement. J Neurol Neurosurg Psychiatry 2011;82(5):512–20. [24] Guillevin R, Menuel C, Taillibert S, et al. Predicting the outcome of grade II glioma treated with temozolomide using proton magnetic resonance spectroscopy. Br J Cancer 2011;104(12):1854–61. [25] Guillevin R, Menuel C, Duffau H, et al. Proton magnetic resonance spectroscopy predicts proliferative activity in diffuse low-grade gliomas. J Neurooncol 2008;87(2):181–7. [26] Hattingen E, Raab P, Franz K, et al. Prognostic value of choline and creatine in WHO grade II gliomas. Neuroradiology 2008;50(9):759–67. [27] Preusser M, Hoeftberger R, Woehrer A, et al. Prognostic value of Ki67 index in anaplastic oligodendroglial tumours – a translational study of the European Organization for Research and Treatment of Cancer Brain Tumor Group. Histopathology 2012;60(6):885–94. [28] Uehara K, Sasayama T, Miyawaki D, et al. Patterns of failure after multimodal treatments for high-grade glioma: effectiveness of MIB-1 labeling index. Radiat Oncol 2012;7(1):104. [29] Stockhammer F, Plotkin M, Amthauer H, van Landeghem FK, Woiciechowsky C. Correlation of F-18-fluoro-ethyl-tyrosin uptake with vascular and cell density in non-contrast-enhancing gliomas. J Neurooncol 2008;88(2):205–10. [30] Stadlbauer A, Prante O, Nimsky C, et al. Metabolic imaging of cerebral gliomas: spatial correlation of changes in O-(2-18F-fluoroethyl)-l-tyrosine PET and proton magnetic resonance spectroscopic imaging. J Nucl Med 2008;49(5):721–9.