Abstract
The aim of this study was to assess the clinical value of O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) PET in the initial diagnosis of cerebral lesions suggestive of glioma. Methods: In a retrospective study, we analyzed the clinical, radiologic, and neuropathologic data of 174 patients (77 women and 97 men; mean age, 45 ± 15 y) who had been referred for neurosurgical assessment of unclear brain lesions and had undergone 18F-FET PET. Initial histology (n = 168, confirmed after surgery or biopsy) and the clinical course and follow-up MR imaging in 2 patients revealed 66 high-grade gliomas (HGG), 77 low-grade gliomas (LGG), 2 lymphomas, and 25 nonneoplastic lesions (NNL). In a further 4 patients, initial histology was unspecific, but during the course of the disease all patients developed an HGG. The diagnostic value of maximum and mean tumor-to-brain ratios (TBRmax/TBRmean) of 18F-FET uptake was assessed using receiver-operating-characteristic (ROC) curve analyses to differentiate between neoplastic lesions and NNL, between HGG and LGG, and between high-grade tumor (HGG or lymphoma) and LGG or NNL. Results: Neoplastic lesions showed significantly higher 18F-FET uptake than NNL (TBRmax, 3.0 ± 1.3 vs. 1.8 ± 0.5; P < 0.001). ROC analysis yielded an optimal cutoff of 2.5 for TBRmax to differentiate between neoplastic lesions and NNLs (sensitivity, 57%; specificity, 92%; accuracy, 62%; area under the curve [AUC], 0.76; 95% confidence interval [CI], 0.68–0.84). The positive predictive value (PPV) was 98%, and the negative predictive value (NPV) was 27%. ROC analysis for differentiation between HGG and LGG (TBRmax, 3.6 ± 1.4 vs. 2.4 ± 1.0; P < 0.001) yielded an optimal cutoff of 2.5 for TBRmax (sensitivity, 80%; specificity, 65%; accuracy, 72%; AUC, 0.77; PPV, 66%; NPV, 79%; 95% CI, 0.68–0.84). Best differentiation between high-grade tumors (HGG or lymphoma) and both NNL and LGG was achieved with a TBRmax cutoff of 2.5 (sensitivity, 79%; specificity, 72%; accuracy, 75%; AUC, 0.79; PPV, 65%; NPV, 84%; 95% CI, 0.71–0.86). The results for TBRmean were similar with a cutoff of 1.9. Conclusion: 18F-FET uptake ratios provide valuable additional information for the differentiation of cerebral lesions and the grading of gliomas. TBRmax of 18F-FET uptake beyond the threshold of 2.5 has a high PPV for detection of a neoplastic lesion and supports the necessity of an invasive procedure, for example, biopsy or surgical resection. Low 18F-FET uptake (TBRmax < 2.5) excludes a high-grade tumor with high probability.
Conventional contrast-enhanced MR imaging is the diagnostic method of choice for primary brain tumors, but the specificity of conventional MR imaging for differentiating neoplastic lesions from nonspecific changes in brain tissue is limited (1). In combination with MR imaging, PET using radiolabeled amino acids is a valuable method to improve diagnostic accuracy for cerebral glioma. Of particular concern are the need for improved delineation of solid tumor tissue for biopsy guidance and treatment planning (2), treatment monitoring (3), and detection of brain tumor recurrence (4).
The most widely used tracer for amino acid PET is l-methyl-11C-methionine (11C-MET), but its use is limited to PET centers with a cyclotron because of its short physical half-life (20 min) (5). O-(2-18F-fluoroethyl)-l-tyrosine (18F-FET) is a well-established 18F-labeled amino acid for PET (half-life, 110 min) that shows logistic advantages over 11C-MET for clinical practice (6,7). Clinical results in brain tumors with PET have been reported to be similar between 11C-MET and 18F-FET (8).
The role of amino acid PET in the differential diagnosis of primary brain tumors is still being debated. In the largest study to date evaluating 11C-MET PET in a consecutive series of 196 patients with suspected brain tumors, differentiation between gliomas and nonneoplastic lesions (NNL) was achieved with a sensitivity of 76% and a specificity of 87% using a threshold of 1.47 for the mean tumor-to-brain ratio, resulting in 79% correct classifications (9). Because of limited availability and logistic disadvantages, PET using the short-lived 11C-MET is increasingly being replaced by PET using 18F-labeled amino acids.
Only a few studies with limited patient populations have addressed the role of 18F-FET PET in the differential diagnosis of primary brain tumors. In the currently largest series of 88 patients with primary brain tumors, 18F-FET PET yielded a sensitivity of 93% for detecting a malignant tumor entity, and the negative predictive value (NPV) for a malignant entity was 89% (10). These results, however, were based only on a visual rating, and histology was available in only two thirds of patients. A recent metaanalysis of 18F-FET PET in 401 patients yielded a pooled sensitivity of 82% and a specificity of 76% for the diagnosis of a primary brain tumor (11). That analysis, however, was limited by variations in ROI definitions and acquisition times among the different studies.
The aim of this retrospective study was to further explore the diagnostic performance of 18F-FET PET in a large series of 174 strictly selected patients with suspected cerebral glioma on MR imaging and subsequent histologic confirmation.
With regard to the possible role of 18F-FET PET in the clinical decision-making process, this study focused on the value of the method for differentiating between neoplastic lesions and NNLs and between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Furthermore, the value of 18F-FET PET for differentiating a high-grade tumor (i.e., HGG or lymphoma) on the one hand from an LGG or benign lesion on the other hand was considered.
MATERIALS AND METHODS
Patients
In a retrospective analysis, we identified 262 patients who were referred to the Department of Neurosurgery of the University of Düsseldorf between 2001 and 2010 for assessment of an intracerebral mass or lesions and underwent 18F-FET PET. From this database, subjects were selected who had newly diagnosed and completely untreated lesions that were suggestive of a cerebral glioma on contrast-enhanced MR imaging according to the neuroradiologic report and who had undergone 18F-FET PET before any therapeutic interventions that might have influenced 18F-FET uptake in the tissue (surgery, biopsy, chemo- or radiotherapy, or radiosurgery). Furthermore, a definite neuropathologic diagnosis after stereotactic biopsy or open resection within 6 wk after MR and PET imaging had to be available. The Heinrich Heine University Ethics Committee approved the study. All subjects gave written informed consent for investigation by 18F-FET PET. A group of 172 patients met these inclusion criteria. Furthermore, we decided to include 2 additional patients without histologic evaluation for whom both the clinical course (response to treatment) and declining MR imaging findings clearly confirmed a benign lesion (1 intracerebral abscess and 1 unspecific lesion). Forty-nine patients were excluded because they had a recurrent tumor, 11 patients were excluded because the biopsy had been performed before the PET scan, and 30 patients had no histologic confirmation within 6 wk after 18F-FET PET.
Thus, the final group, which was included in the statistical evaluation, consisted of 174 patients (97 men and 77 women; mean age, 45 y; range, 2–84 y). Histology after surgery or biopsy revealed 66 HGGs, 77 LGGs, 2 intracerebral malignant B-cell lymphomas, and 25 NNLs. Four lesions were classified as occult glioma because histologic investigation of biopsies at initial diagnosis were nondiagnostic but 18F-FET PET was positive and all 4 patients developed an HGG in the further course of disease.
Clinical and demographic patient data (age, sex, and initial leading symptom of the lesion), MR imaging findings (lesion size, location, and contrast enhancement), results of 18F-FET PET (tumor-to-brain ratios), and neuropathologic diagnoses are summarized in Tables 1 and 2. Further details on each individual patient are given in Supplemental Table 1 (supplemental materials are available online only at http://jnm.snmjournals.org).
Patient Characteristics
Imaging Results (MR Imaging and 18F-FET PET)
18F-FET PET Imaging and Data Analysis
The amino acid O-(2-18F-fluorethyl)-l-tyrosine (18F-FET) was produced via nucleophilic 18F-fluorination with a specific radioactivity of more than 200 GBq/μmol as described previously (12). All patients fasted for at least 12 h before the PET studies.
PET studies were acquired after intravenous injection of 200 MBq of 18F-FET using an ECAT EXACT HR+ scanner (Siemens Medical Systems, Inc.) in 3-dimensional mode (32 rings; axial field of view, 15.5 cm). For attenuation correction, transmission was measured with three 68Ge/68Ga rotating line sources. After Fourier rebinning and correction for attenuation, random coincidences, scattered coincidences, and decay, 63 image planes were iteratively reconstructed (ordered-subsets expectation maximization, 6 iterations, 16 subsets) using the ECAT 7.2 software. The reconstructed image resolution was approximately 5.5 mm. In recent years (2006–2010) dynamic PET studies have been routinely acquired (0–50 min), but for earlier investigations (2001–2005) only static scans were available (15–40 min). The further evaluation was based on the summed PET data from 20 to 40 min after injection.
18F-FET PET and contrast-enhanced MR images were coregistered using MPI tool software (version 6.48; ATV). The transaxial slices showing the highest 18F-FET accumulation in the tumors were chosen for ROI analyses. 18F-FET uptake in the unaffected brain tissue was determined by a larger ROI placed on the contralateral hemisphere in an area of normal-appearing brain tissue including white and gray matter. 18F-FET uptake in the tumor was determined by a 2-dimensional autocontouring process using a tumor-to-brain ratio (TBR) of at least 1.6. This cutoff was based on a biopsy-controlled study in cerebral gliomas in that a lesion-to-brain ratio of 1.6 best separated tumoral from peritumoral tissue (2). When 18F-FET uptake in the lesions was similar to that in the normal brain tissue, a representative irregular ROI was placed manually on the area of signal abnormality in the T1- and T2-weighted transversal MR scan and transferred to the coregistered 18F-FET PET scan in each case. Mean and maximum TBR (TBRmean and TBRmax) were calculated by dividing the mean and maximum SUV of the tumor ROI by the mean SUV of normal brain in the 18F-FET PET scan.
MR Imaging and Analysis
All patients had routine MR imaging using a 1.5-T MR scanner with a standard head coil before and after administration of gadolinium-diethylenetriaminepentaacetic acid (T1-, T2-, and fluid-attenuated inversion recovery sequences; slice thickness of 4–6 mm). The MR imaging was performed within 6 wk before stereotactic needle biopsy or open surgical tissue resection. To assess the lesion size, the longest diameter of the contrast-enhancing lesion was measured. In studies without contrast enhancement, the longest diameter of the lesion in the T2-weighted MR image was used.
Histopathology
Histology was performed for 172 patients either by stereotactic needle biopsy (n = 93; 54%) or by open surgical tissue resection (n = 79; 46%). The diagnoses were established from formalin-fixed and paraffin-embedded tissue samples as described previously (2).
Receiver-Operating-Characteristic (ROC) Curve Analysis
The diagnostic accuracy of the TBRmax and TBRmean of 18F-FET uptake for differentiation of neoplastic lesions from NNLs, for differentiation of HGGs from LGGs, and for differentiation of high-grade tumors (i.e., HGG or lymphoma) from both NNLs and LGGs was evaluated by ROC curve analyses (SigmaPlot, version 11.0; Systat Software Inc.) using subsequent histologic analysis of 172 lesions or clinical course and MR imaging findings (n = 2) as a reference. The decision cutoff was considered optimal when the product of paired values for sensitivity and specificity reached its maximum. In addition, the area under the ROC curve (AUC), its SE, and the level of significance were determined as a measure of the diagnostic quality of the test.
Statistical Analysis
Descriptive statistics are provided as mean and SD or median and range. To compare 2 groups, the Student t test was used. The Mann–Whitney rank sum test was used when variables were not normally distributed. Probability values of less than 0.05 were considered significant. Statistical analysis was performed using SigmaPlot software (version 11.0; Systat Software Inc.).
RESULTS
Based on neuropathologic and clinical assessment, we identified 25 patients with NNLs and 145 patients with neoplastic lesions (i.e., LGGs, HGGs, and lymphomas). The results of 18F-FET PET (tumor-to-brain ratios) are summarized in Tables 1 and 2. Further details of each individual patient are given in Supplemental Table 1. The distribution of the TBRmax of 18F-FET uptake in NNLs and gliomas of World Health Organization (WHO) grades I–IV is shown in Figure 1. The corresponding data for the TBRmean are shown in Figure 2. The data for the 2 lymphomas are not included in the figures. The TBRmax of the 2 lymphomas was 1.9 and 2.9, and the TBRmean was 1.6 and 2.0 (Supplemental Table 1). For ROC analysis, gliomas of WHO grades III and IV were considered together as HGGs. The 4 lesions classified as occult glioma were not included in the statistical evaluation.
Distribution of TBRmax of 18F-FET uptake in NNL and gliomas of WHO grades I/II, III, and IV. Dotted line indicates optimal cutoff of 2.5 determined by ROC analysis to differentiate between neoplastic lesions and NNL. This cutoff also differentiates best between LGG (WHO grades I and II) and HGG (WHO grades III and IV), as well as between high-grade tumors (HGG and lymphomas) and NNL or LGG.
Distribution of TBRmean of 18F-FET uptake in NNL and gliomas of WHO grades I/II, III, and IV. Dotted line indicates optimal cutoff of 1.9 determined by ROC analysis to differentiate between neoplastic lesions and NNL. This cutoff also differentiates best between LGG (WHO grades I and II) and HGG (WHO grades III and IV), as well as between high-grade tumors (HGG and lymphomas) and NNL or LGG.
Differentiation of Neoplastic Lesions from NNLs
Neoplastic lesions showed significantly higher 18F-FET uptake than did NNLs (TBRmax, 3.0 ± 1.3 vs. 1.8 ± 0.5; P < 0.001). ROC analysis yielded an optimal cutoff of 2.5 for TBRmax to differentiate between neoplastic lesions and NNLs (sensitivity, 57%; specificity, 92%; accuracy, 62%; area under the curve [AUC], 0.76; 95% confidence interval [CI], 0.68–0.84). The positive predictive value (PPV) was 98%, and the NPV was 27% (Fig. 3). Thus, a TBRmax of more than 2.5 is highly indicative of a neoplastic lesion and suggests that histopathologic confirmation is necessary for further treatment planning.
ROC curve analysis of TBRmean (black curve) and TBRmax (red curve) to differentiate between neoplastic lesions and NNL. Area under curve was 0.74 for TBRmean (P < 0.001; 95% CI, 0.66–0.82; optimal cutoff, 1.9) and 0.76 for TBRmax (P < 0.001; 95% CI, 0.68–0.84; optimal cutoff, 2.5).
Similar diagnostic performance was achieved when TBRmean was used as a parameter (Fig. 3). Neoplastic lesions showed significantly higher TBRmean than did NNLs (TBRmean, 1.9 ± 0.6 vs. 1.4 ± 0.4; P < 0.001). ROC analysis yielded an optimal cutoff of 1.9 for TBRmean to differentiate between neoplastic lesions and NNLs (sensitivity, 58%; specificity, 88%; accuracy, 62%; AUC, 0.74; 95% CI, 0.66–0.82). The PPV was 97%, and the NPV was 27%.
Differentiation of HGG from LGG
Patients with HGGs (n = 66) exhibited significantly higher 18F-FET uptake than did patients with LGGs (n = 77) (TBRmax, 3.6 ± 1.4 vs. 2.4 ± 1.0; P < 0.001). ROC analysis for differentiation between HGG and LGG yielded an optimal cutoff of 2.5 for TBRmax (sensitivity, 80%; specificity, 65%; accuracy, 72%; AUC, 0.77; 95% CI, 0.68–0.84) (ROC curve not shown). The PPV was 66%, and the NPV was 79%. These values were not sufficient to justify a clinical decision, and a biopsy to determine the therapeutic procedure could not be avoided.
Similar diagnostic performance was achieved when TBRmean was used as a parameter. HGGs showed a significantly higher TBRmean than did LGGs (TBRmean, 2.1 ± 0.6 vs. 1.6 ± 0.5; P < 0.001). ROC analysis yielded an optimal cutoff of 1.9 for TBRmean to differentiate between HGGs and LGGs (sensitivity, 82%; specificity, 62%; accuracy, 71%; AUC, 0.74; 95% CI, 0.67–0.83). The PPV was 65%, and the NPV was 80%.
Differentiation of High-Grade Tumors from NNLs and LGGs
The best differentiation between high-grade tumors (i.e., HGG and lymphoma) and both NNLs and LGGs (TBRmax, 3.6 ± 1.4 vs. 2.3 ± 0.9; P < 0.001) was achieved with a cutoff of 2.5 for TBRmax (sensitivity, 79%; specificity, 72%; accuracy, 75%; AUC, 0.79; 95% CI, 0.71–0.86) (Fig. 4). The PPV was 65%, and the NPV was 84%. Thus, a TBRmax of less than 2.5 suggests that a high-grade tumor is unlikely.
ROC curve analysis of TBRmean (red curve) and TBRmax (black curve) to differentiate between high-grade tumors (i.e., HGG and lymphoma) and LGG/NNL. Area under curve was 0.78 for TBRmean (P < 0.001; 95% CI, 0.70–0.85; optimal cutoff, 1.9) and 0.79 for TBRmax (P < 0.001; 95% CI, 0.72–0.86; optimal cutoff, 2.5).
A similar diagnostic performance was achieved when TBRmean was used as a parameter. HGGs and lymphomas showed a significantly higher TBRmean than did NNLs and LGGs (TBRmean, 2.1 ± 0.6 vs. 1.6 ± 0.5; P < 0.001). ROC analysis yielded an optimal cutoff of 1.9 for TBRmean to differentiate between tumors and NNLs (sensitivity, 81%; specificity, 69%; accuracy, 74%; AUC, 0.78; 95% CI, 0.70–0.85) (Fig. 4). The PPV was 63%, and the NPV was 84%.
DISCUSSION
This study on a relatively large group of selected patients with untreated primary brain tumors and histopathologic confirmation demonstrated that there is wide variability in 18F-FET uptake ratios for cerebral gliomas of different WHO grades and TBRs of 18F-FET uptake. These observations are in line with those of several other studies (2,10,13,14). Nevertheless, we identified threshold values for TBRmax and TBRmean that may be helpful in the clinical decision-making process. For neoplastic lesions, 18F-FET uptake beyond a TBRmax cutoff of 2.5 or a TBRmean cutoff of 1.9 had a PPV of 98% or 97%, respectively. Furthermore, for high-grade tumors such as HGGs or lymphomas, 18F-FET uptake with a TBRmax of less than 2.5 or a TBRmean of less than 1.9 had an NPV of 84%.
This information might be helpful for the neurooncologist who is deciding on patient management. If a benign lesion can be excluded by 18F-FET PET with high probability, the need for an immediate histologic examination is a given. On the other hand, when the clinical course and MR imaging suggest a benign process, the finding of low 18F-FET uptake may support a decision to observe the process temporarily.
The selection of patients in this study was based on the challenge to improve diagnostic information about cerebral lesions suspected of being a glioma on the basis of MR imaging results and for which either the extent and optimal biopsy site or differentiation from an NNL is unclear. As this challenge concerns in particular tumors that exhibit no contrast enhancement on MR imaging, the patient population included more patients with WHO grade II and III gliomas than are normally found. However, there is no consequent restriction on the validity of our study since 18FET PET is particularly required by the neurooncologist in these patients.
The results of our study are similar to those of previous studies. In a study evaluating 11C-MET PET in a consecutive series of 196 patients with suspected brain tumors, gliomas could be differentiated from NNLs with a sensitivity of 76% and a specificity of 87% using a threshold of 1.47 for TBRmean. From these values, a TBRmean greater than 1.47 has a positive likelihood ratio of 5.85 for a tumor, compared with 4.83 using a TBRmean greater than 1.9 in our study. The negative likelihood ratio for a tumor at a TBRmean of less than 1.47 in that study was 0.28, compared with 0.48 in our study. That is, the NPV of low amino acid uptake to exclude a tumor is limited, because approximately 30% of LGGs exhibit low or absent amino acid uptake (14,15). The difference between the threshold in the above-mentioned study and that in our study may be explained by differences in the definition of the tumor ROI, since the TBRmean is dependent on the size of the tumor ROI.
Another study focused on the role of 18F-FET PET in the initial evaluation of 88 patients with brain lesions (10). However, in that study images were interpreted visually and the diagnostic accuracy cannot be compared properly to that in our study. The sensitivity for detecting a malignant tumor was 93%, compared with 81% in our study, and the NPV for a malignant entity was 89%, compared with 86% in our study. Thus, the results are similar despite methodologic differences.
Recently, a metaanalysis evaluated the performance of 18F-FET PET in the differential diagnosis of 401 patients with primary brain tumors (11). A limitation of that particular metaanalysis is the fact that the methods for the ROI definition and acquisition times varied among the different studies. Moreover, 7 of the 13 studies that focused on various clinical topics came from one center (2,15–20) and 2 from another center (21,22). In the patient populations of those studies, there appears to be an overlap that was not considered in the metaanalysis.
In that study, ROC analysis yielded an optimal threshold of 1.6 for TBRmean and 2.1 for TBRmax to differentiate primary brain tumors from NNLs—thresholds that are somewhat lower than that observed in our study. With respect to the different methodologies of the studies, the authors emphasized that those thresholds should be considered with caution and that further investigations with strict standardization of PET acquisition protocols are necessary. We would like to emphasize that our study meets these criteria.
Great importance was placed on the objectivity and reproducibility of the methodology. The tumor borders were defined by a lesion-to-brain ratio greater than 1.6, which is based on a previous biopsy-controlled study (2). Furthermore, the evaluation of TBRmax and TBRmean yielded similar diagnostic accuracy concerning the differentiation of brain lesions, but both parameters have limitations that need to be considered. Although mean TBRmean is influenced by the size of the tumor ROI, TBRmax is dependent on the spatial resolution of the PET scanner and on the reconstruction matrix. Therefore, it is important to adapt the evaluation methods such that the data among various centers are comparable. In addition, the timing of data acquisition plays an important role since the TBR varies over time depending on the grade of malignancy. Therefore, in our study all data that were acquired during the last 10 y were reconstructed again using the summarized data from 20 to 40 min to determine the TBR.
The metaanalysis mentioned above (11) yielded a pooled sensitivity of 82% for primary brain tumors and a specificity of 76%, and the authors emphasized the excellent performance of 18F-FET PET for diagnosing primary brain tumors. Although we cannot accept this view entirely, we agree that important information may be extracted from 18F-FET PET during the initial diagnosis. The diagnostic value of 18F-FET PET during the initial diagnosis of cerebral lesions lies especially in defining an optimal site for biopsy and determining the extent of metabolically active tumor for treatment planning rather than in making a differential diagnosis of the lesion. A recent analysis indicated the cost-effectiveness of 18F-FET PET–guided biopsy (23).
With respect to differentiation between HGGs and LGGs, it is evident that there is a wide overlap of uptake ratios between gliomas of different WHO grades. The diagnostic accuracy of 18F-FET PET is not sufficient to decisively influence the therapeutic approach, and histopathologic confirmation by biopsy or open surgery remains necessary.
Several studies have indicated that the evaluation of 18F-FET kinetics may allow differentiation between HGGs and LGGs with higher sensitivity and specificity (13,14,24–27). HGGs appear to be characterized by an early peak around 10–15 min after injection followed by a decrease in 18F-FET uptake. In contrast, steadily increasing time–activity curves seem to be typical for LGGs. Unfortunately, such data were not available for most of the patients included in this study.
CONCLUSION
18F-FET uptake ratios provide valuable additional information for the differentiation of cerebral lesions and the grading of gliomas. 18F-FET uptake with a TBRmax of more than 2.5 has a high PPV for a neoplastic lesion, and a TBRmax of less than 2.5 has a high NPV for a high-grade tumor (i.e., HGG or lymphoma), which may be helpful for the neurooncologist in the clinical decision-making process. Thus, 18F-FET PET may add relevant complementary information to clinical data and MR imaging in the clinical setting.
DISCLOSURE
The costs of publication of this article were defrayed in part by the payment of page charges. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 USC section 1734. The Brain Imaging Center West (BICW) supported this work. No other potential conflict of interest relevant to this article was reported.
Acknowledgments
We thank Suzanne Schaden, Elisabeth Theelen, and Kornelia Frey for assistance with the patient studies and Johannes Ermert, Silke Grafmüller, Erika Wabbals, and Sascha Rehbein for radiosynthesis of 18F-FET.
Footnotes
↵* Contributed equally to this work.
Published online Dec. 11, 2012
- © 2013 by the Society of Nuclear Medicine and Molecular Imaging, Inc.
REFERENCES
- Received for publication June 2, 2012.
- Accepted for publication September 17, 2012.
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