18F-FET PET/CT Can Aid in Diagnosing Patients with Indeterminate MRI Findings for Brain Tumors: A Prospective Study

18F-FET PET/CT Can Aid in Diagnosing Patients with Indeterminate MRI Findings for Brain Tumors: A Prospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 18F-FET PET/CT Can Aid in Diagnosing Patients with Indeterminate MRI Findings for Brain Tumors: A Prospective Study Sheng-Chieh Chan, Tsung-Lang Chiu, Shu-Hang Ng, Sheng-Tzung Tsai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5062302/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Annals of Nuclear Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Objective This prospective study aimed to evaluate the diagnostic value of fluorine-18-labeled fluoroethyltyrosine ( 18 F-FET) positron emission tomography (PET)/computed tomography (CT) in diagnosing brain tumors within an Asian patient population. Methods Patients suspected of having primary or recurrent brain tumors were prospectively recruited. Each patient underwent 18 F-FET and fluorine-18 fluorodeoxyglucose ( 18 F-FDG) PET/CT on separate days within one week. We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy to compare the diagnostic performance of the two PET scans. The standardized uptake value (SUV) and tumor-to-background ratio (TBR) of the lesions were determined using static images. Additionally, time-activity curves (TACs) and time-to-peak (TTP) were generated from the dynamic PET images. Results From September 2019 to December 2023, 33 subjects were enrolled for reasons including suspected brain tumors (n = 20) or suspicious glioma recurrence (n = 8) on magnetic resonance imaging (MRI) and restaging for glioma (n = 5). Among the patients with suspected brain tumors or glioma recurrence on MRI, 25% had false-positive results. 18 F-FET PET/CT accurately identified 86% of these false positives. The sensitivity, specificity, PPV, NPV, and accuracy of visual interpretation of 18 F-FET PET/CT were 96.15%, 85.71%, 96.15%, 85.71%, and 93.90%, respectively. The corresponding 18 F-FDG PET/CT values were 73.08%, 71.42%, 90.48%, 41.67%, and 72.70%. 18 F-FET PET/CT demonstrated significantly higher sensitivity and accuracy than 18 F-FDG PET ( p = 0.031 and p = 0.030, respectively). Using TBRmean as an adjunct reference index enhanced the diagnostic accuracy of 18 F-FET PET/CT, achieving a sensitivity and NPV of 100%. Wash-out TAC or TTP < 20 min was associated with a PPV of 100% for brain tumors. Conclusions 18 F-FET PET/CT appears to be a valuable tool for assessing brain tumors with indeterminate MRI findings in this Asian cohort. 18 F-FET PET/CT offers benefits over 18 F-FDG PET in differentiating brain tumors from nontumor brain lesions, particularly when using semiquantitative analysis with TBR. brain neoplasms glioma O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) positron emission tomography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Brain tumors are a highly heterogeneous group that encompass various types of central nervous system (CNS) tumors. According to recent epidemiological data, the global incidence is 3.9 per 100,000 person-years for all CNS tumors, and they can be either benign or malignant [ 1 ]. The incidence of brain tumors is higher in developed countries, such as North America, Western Europe, and Australia, with an annual incidence of approximately 6 to 11 new cases per 100,000 people in the United States [ 2 , 3 ]. Its incidence varies globally; therefore, understanding the specific situation in other countries is crucial for local healthcare planning and resource allocation. The annual incidence of primary malignant brain tumors is approximately 2.73–3.75 per 100,000 people in Taiwan, showing an increasing trend over the years [ 4 , 5 ]. Gliomas represent a particularly challenging subset of brain tumors, accounting for 81% of all malignant brain tumors in adults. Glioblastoma multiforme (GBM), the most aggressive form, has a dismal five-year survival rate of approximately 5% [ 1 , 6 ]. This poor prognosis underscores the urgent need for improved diagnostic and management strategies for patients with gliomas. Consequently, the identification of more effective diagnostic and treatment methods has become an important research focus. Magnetic resonance imaging (MRI) is the primary diagnostic tool for brain tumors and provides detailed anatomical information. However, it has limitations in differentiating tumor tissue from edema or necrosis, and distinguishing tumor recurrence from post-treatment changes [ 7 , 8 ]. MRI also struggles to identify low-grade tumors and non-enhancing components, potentially leading to an incomplete assessment [ 9 ]. Consequently, there is a need for complementary imaging modalities to address these shortcomings and improve brain tumor diagnosis and management [ 10 ]. Fluorine-18-labeled fluoroethyltyrosine ( 18 F-FET) positron emission tomography (PET) has emerged as a promising non-invasive imaging technique for brain tumor diagnosis. As a radiolabeled amino acid analog, 18 F-FET offers advantages over traditional fluorine-18 fluorodeoxyglucose ( 18 F-FDG) PET, particularly in distinguishing tumors from inflammation [ 11 , 12 ]. Studies have demonstrated its value in assessing tumor malignancy, surgical planning, and evaluating radiotherapy effects [ 13 , 14 ]. 18 F-FET PET accurately displays tumor metabolic activity, aiding in tumor boundary delineation and improving resection outcomes [ 15 , 16 ]. Its low uptake in post-treatment inflammation also makes it useful for monitoring recurrence and distinguishing between necrosis and recurrence after treatment [ 17 , 18 ]. Despite these advantages, the application of 18 F-FET PET faces several challenges. These include potential limitations in diagnosing low-grade brain tumors [ 19 ] and high costs, which limit its widespread clinical application [ 20 ]. Additionally, gliomas in East Asian populations differ in epidemiology and genomic characteristics compared to those in other ancestry groups. For instance, East Asian glioblastoma patients typically experience a significantly younger age of onset and longer overall survival than White patients [ 21 ]. East Asian cohorts also show a lower incidence of epidermal growth factor receptor amplification in glioblastoma and a higher incidence of 1p19q-IDH-TERT triple-negative low-grade glioma [ 22 , 23 ]. Studies further reveal survival rate differences among East Asian countries. For instance, the five-year relative survival rate for anaplastic astrocytoma ranges from 25.2–26.2% in South Korea, is 22.1% in Taiwan, but reaches 41.1% in Japan [ 5 , 24 , 25 ]. Although significant research on 18 F-FET PET has been conducted in Europe and the United States, fewer prospective studies or clinical applications are available in Asian countries. It remains uncertain whether the findings from Europe and the United States can be generalized in Asia or across different Asian countries. We conducted this study with the aim to prospectively explore the application of 18 F-FET PET in the diagnosis and management of patients with brain tumors in an Ascian cohort. Materials and Methods Patient population Between September 2019 and December 2023, 33 patients suspected of having primary or recurrent brain tumors on MRI or who were undergoing restaging for glioma were consecutively recruited. The patients underwent both 18 F-FET and 18 F-FDG PET/CT. Each PET study pair was performed within a 3-day interval. This study was approved by the Institutional Review Board of Hualien Tzu Chi Hospital (IRB108-262-A), registered on CinicalTrial.gov (NCT06563024), and conducted in accordance with the 1964 Declaration of Helsinki and all subsequent revisions. All patients provided written informed consent prior to their inclusion in this study. The general characteristics of the participants are presented in Table 1 . Table 1 General characteristics of the study participants Variable Number of patients (%) Age (years), mean ± SD 54 ± 14 Sex Male 21 (64) Female 12 (36) Reason for the PET scan Suspected brain tumor 20 (61) Suspected glioma recurrence 8 (24) Glioma restaging 5 (15) Final diagnosis of suspected brain tumor Primary glioma 6 Other brain tumor 9 metastasis 4 other primary tumor 5 Benign lesion 5 Final diagnosis of suspected glioma recurrence Recurrent glioma 6 Benign lesion (post-treatment change) 2 Data are expressed as the count (percentage) unless otherwise indicated. SD standard deviation, PET positron emission tomography PET/CT scan The 18 F-FET and 18 F-FDG were synthesized at a cyclotron center at our hospital. The patients were asked to fast for a minimum of 6 h prior to the injection of either PET tracer. 18 F-FET and 18 F-FDG scans were performed on separate days within one week of each other. Blood glucose levels on the day of FDG-PET were less than 150 mg/dL in all patients. The scans were obtained using a Discovery MI PET/CT scanner (GE Medical Systems, Milwaukee, WI, USA). Dynamic PET studies were performed up to 60 min after intravenous injection of approximately 200 MBq 18 F -FET or 18 F-FDG. Images were processed with the measured attenuated correction obtained from contemporaneous non-contrast CT. CT and PET scans were also coregistered using proprietary fusion software to allow for anatomical correlation (Xeleris Workstation; GE Medical Systems). Image analysis The region of interest (ROI) for analysis was determined on the slices showing the highest 18 F-FET or 18 F-FDG accumulation in the tumors. The nuclear medicine physician placed the three-dimensional volumetric ROI in the axial, coronal, and sagittal planes. For conventional semiquantitative evaluation, 18 F-FET or 18 F-FDG standardized uptake value (SUV) in the tumor or the tumor-to-background ratio (TBR) was determined on a summation image (20–40 min after injection) [ 26 ]. TBR was calculated using the following formula, and the background region was selected from the contralateral hemisphere of the normal-appearing brain tissue. $$\:\mathbf{T}\mathbf{B}\mathbf{R}=\frac{\mathbf{S}\mathbf{U}\mathbf{V}\mathbf{t}\mathbf{u}\mathbf{m}\mathbf{o}\mathbf{r}}{\mathbf{S}\mathbf{U}\mathbf{V}\mathbf{b}\mathbf{a}\mathbf{c}\mathbf{k}\mathbf{g}\mathbf{r}\mathbf{o}\mathbf{u}\mathbf{n}\mathbf{d}}\text{}\text{}$$ Time-activity curves (TACs) of the mean SUV of 18 F-FET uptake in the tumor and brain were generated by applying a spherical volume of interest (VOI) of 2 mL, centered on the area of maximal tumor uptake. A reference VOI was applied to unaffected brain tissue, covering the entire dynamic dataset. TACs, representing the mean tissue radioactivity in the tumor VOI as a function of time, were generated from dynamic 18 F-FET PET images. The TAC shape was classified as increasing (accumulative) or decreasing (washout). In a visual analysis, foci of increased uptake were evaluated and the uptake scored on a 5-point scale: 0 = no abnormal uptake, 1 = benign, 2 = probably benign, 3 = probably malignant, and 4 = definitely malignant. Both grade 3 and grade 4 were considered to indicate positive findings. Histopathological and imaging-based assessment for classifying brain lesions The nature of brain lesions was primarily determined through histopathological verification. Histological classification and tumor grading were performed according to the WHO guidelines current at the respective date of histopathological assessment. When histopathological confirmation was not feasible, follow-up MR imaging results were used. The final diagnosis was established either through histopathological examination or with clinical and imaging follow-up for at least one year. Assessment of treatment response in gliomas was based on the Response Assessment in Neuro- Oncology (RANO) criteria. Pseudoprogression was defined as new or increasing contrast enhancement that eventually subsides without any change in therapy [ 27 ]. Statistical analysis The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 18 F-FET PET and 18 F-FDG PET were calculated and the results were compared using McNemar’s Chi-square test. Semi-qualitative or qualitative PET parameters were compared using the Mann–Whitney U test. All statistical analyses were conducted using SPSS software version 20. Statistical significance was defined as a two-tailed P-value of < 0.05. Results 18 F-FET uptake and PET diagnostic performance for false-positive MRI lesions A total of 33 patients were enrolled in the study due to suspected brain tumors or glioma recurrence based on MRI. Patients were enrolled for the following reasons: suspected brain tumor on MRI (n = 20), suspected glioma recurrence (n = 8), or restaging for glioma (n = 5) (Table 1 ). Figure 1 summarizes the 18 F-FET PET/CT results. For the eight patients suspected of glioma recurrence, six were confirmed to have tumor recurrence and two had post-treatment inflammatory changes; 18 F-FET PET/CT accurately demonstrated increased 18 F-FET uptake in recurrent tumors and showed only mild 18 F-FET uptake in cases of post-treatment inflammatory changes (Figs. 2 and 3 ). In the 20 patients with suspected brain tumors, six were confirmed to have gliomas, nine had other brain tumors, and five had benign lesions. 18 F-FET PET demonstrated significantly increased uptake in five of the six patients with confirmed gliomas and in all nine patients with other brain tumors. A GBM (small, thin-walled) was identified as a false-negative result. Among the five patients with benign lesions, four exhibited low 18 F-FET uptake, while one case of tumefactive demyelination produced a false-positive finding (Fig. 4 ). In the restaging group, all patients demonstrated markedly increased increased 18 F-FET uptake. Among the 28 patients suspected of having brain tumors or glioma recurrence based on MRI, 25% (7 out of 28) had false-positive MRI results. Of note, 18 F-FET PET/CT accurately identified 86% (6 out of 7) of these false-positive MRI lesions. Comparison of the diagnostic performance of F-FET PET and F-FDG PET Table 2 summarizes the patient-based diagnostic performance metrics for 18 F-FET PET and 18 F-FDG PET based on visual analysis. The sensitivity, specificity, PPV, NPV, and accuracy of 18 F-FET PET/CT were 96.15%, 85.71%, 96.15%, 85.71%, and 93.90%, respectively. The corresponding 18 F-FDG PET/CT values were 73.08%, 71.42%, 90.48%, 41.67%, and 72.70%. 18 F-FET PET/CT had significantly higher sensitivity and accuracy than 18 F-FDG PET ( p = 0.031 and 0.030, respectively). Table 2 Comparative accuracy of fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET in diagnosing suspected brain tumors or glioma recurrence FN TP TN FP Sen Spe Acc PPV NPV 18F-FET PET 1 25 6 1 96.15 85.71 93.90 96.15 85.71 18F-FDG PET 7 19 5 2 73.08 71.42 72.70 90.48 41.67 FN false-negative, TP true-positive, TN true-negative, FP false-positive, Sen sensitivity, Spe specificity, Acc accuracy, PPV positive predictive value, NPV negative predictive value, AUC area under curve Sensitivity, 18 F-FET PET versus 18 F-FDG PET, p = 0.031 Accuracy, 18 F-FET PET versus 18 F-FDG PET, p = 0.030 Qualitative analysis of PET imaging Table 3 presents the quantitative analysis of 18 F-FET PET and 18 F-FDG PET parameters. The TBRmean values for glial tumors (primary or recurrent glioma), other brain tumors, and benign lesions on 18 F-FET PET were 3.64, 3.10, and 1.22, respectively. Corresponding TBRmean values for 18 F-FDG PET were 1.73, 1.17, and 0.70, respectively. The TBR was significantly higher with 18 F-FET PET than with 18 F-FDG PET ( p < 0.001), regardless of lesion type. On 18 F-FET PET, the TBRmean for gliomas and other brain tumors was markedly higher than that of benign lesions ( p = 0.001 and 0.003, respectively; Fig. 5 ). In comparison, although the TBRmean for gliomas on 18 F-FDG PET was elevated compared to benign lesions, there was no significant difference between other brain tumors and benign lesions. Table 3 Analysis of 18F-FET PET and 18F-FDG PET parameters Patients (n) TBRmean (SD) on 18 F-FET PET SUVmax (SD) on 18 F-FET PET TTP (SD) on 18 F-FET PET Dynamic curve pattern on 18 F-FET PET TBRmean (SD) on 18 F-FDG PET SUVmax (SD) on 18 F-FDG PET TTP (SD) on 18 F-FDG PET Dynamic curve pattern on 18 F-FDG PET Glial tumors 17 3.64 (1.56) 5.42 (2.11) 1414 (960) Wash-out (n = 10) Accumulative (n = 7) 1.73 (0.85) 8.56 (4.78) 3263 (150) Accumulative (n = 16) Not available a (n = 1) Other brain tumors 9 3.10 (0.89) 5.15 (2.71) 1130 (1233) Wash-out (n = 5) Accumulative (n = 4) 1.17 (0.51) 7.91 (3.56) 3263 (106) Accumulative (n = 9) Benign brain lesions 7 1.22 (1.11) 3.55 (1.34) 2160 (805) Accumulative (n = 7) 0.70 (0.67) 8.87 (1.83) 3150 (300) Accumulative (n = 7) p 1 = 0.672 p 2 = 0.001 p 3 = 0.003 p 1 = 0.491 p 2 = 0.093 p 3 = 0.364 p 1 = 0.339 p 2 = 0.140 p 3 = 0.147 p 1 = 0.095 p 2 = 0.019 p 3 = 0.299 p 1 = 0.628 p 2 = 0.362 p 3 = 0.683 p 1 = 0.834 p 2 = 0.617 p 3 = 0.683 a Not available due to patient movement during the scan p1 glial versus non-glial, p2 glial versus inflammatory, p3 non-glial versus inflammatory, 18F-FET fluorine-18-labeled fluoroethyltyrosine, PET positron emission tomography, TBR tumor-to-background ratio, SUV standardized uptake value, 18F-FDG fluorine-18 fluorodeoxyglucose, TTP time-to-peak, SD standard deviation In the analysis of 18 F-FET PET dynamic curves, the wash-out curve pattern was observed only in glial tumors or other brain tumors (Table 3 and Fig. 6 ), while all benign inflammatory lesions demonstrated an accumulative pattern. The time-to-peak (TTP) values for glial and other brain tumors were lower than those for benign lesions, but the difference was not statistically significant. With TBRmean as an adjunct interpretive reference [ 28 ], the diagnostic accuracy of 18 F-FET PET improved, showing sensitivity, specificity, PPV, and NPV values of 100%, 85.71%, 96.30%, and 100%, respectively (Table 4 and Fig. 7 ). In contrast, using the washout curve pattern or TTP as interpretation criteria resulted in a high PPV but moderate sensitivity. Table 4 Comparative accuracy of 18F-FET PET based on different interpretation criteria Interpretation criteria Sen Spe Acc PPV NPV Visual 96.15 85.71 93.94 96.15 85.71 TBRmean a 100.00 85.71 96.97 96.30 100.00 TTP b 73.08 83.33 75.00 95.00 41.67 Dynamic curve pattern 57.69 100.00 66.67 100.00 38.89 Visual + TBRmean 100.00 85.71 96.97 96.30 100.00 Sen sensitivity, Spe specificity, Acc accuracy, PPV positive predictive value, NPV negative predictive value, AUC area under curve, 18F-FET fluorine-18-labeled fluoroethyltyrosine, PET positron emission tomography, TBR tumor-to-background ratio, TTP time-to-peak a TBRmean = 2.0 as the cut-off value [ 29 ] b TTP = 45 min as the cut-off value Discussion This prospective study evaluated the use of 18 F-FET PET/CT for diagnosing and treating brain tumors in an Asian population. We found that 18 F-FET PET/CT can assist clinicians in making more accurate diagnoses. Among patients suspected of having brain tumors or glioma recurrence, 25% had false-positive MRI results, of which 86% were accurately identified with 18 F-FET PET/CT. The use of 18 F-FET PET parameters, especially TBRmean, further improved diagnostic sensitivity and accuracy to 100% and 97%, respectively. Compared to 18 F-FDG PET/CT, 18 F-FET PET/CT showed superior sensitivity and accuracy, with a significantly higher TBR, making it more effective in distinguishing tumors from inflammatory lesions. Overall, 18 F-FET PET/CT proves to be a valuable tool for evaluating brain tumors, offering clear advantages over 18 F-FDG PET, particularly in diagnosing patients with indeterminate MRI findings. The standard-of-care neuroimaging modality for the detection of brain tumors is contrast-enhanced MRI, which is a critical component of the clinical management strategy, from diagnosis to prognosis and treatment response assessment [ 7 , 8 ]. However, MRI has limitations in diagnosing brain tumor lesions, including challenges in distinguishing specific tumor types, unclear tumor boundaries, and limited sensitivity in detecting small tumors [ 29 ]. There are also some limitations to the use of standard MRI for response assessment and treatment monitoring. Given these limitations, there is clearly a need for more sophisticated and advanced neuroimaging modalities that can augment and resolve the gaps in existing standard imaging techniques. Metabolic imaging using PET with the amino acid analogue 18 F-FET has gained increasing importance for the imaging of gliomas. 18 F-FET PET has shown potential in treatment planning and therapy monitoring in glioma [ 30 ]. Of the patients suspected of having brain tumors or glioma recurrence on MRI in this study, 25% had false-positive MRI results. 18 F-FET PET correctly identified most of these false-positive MRI lesions. Thus, 18 F-FET PET is effective in identifying patients suspected of having primary brain tumors who actually have benign lesions, or differentiate which recurrent patients are experiencing pseudoprogression (Figs. 2 and 3 ). Puranik et al. reported that 18 F-FET PET complemented MRI in the detection of primary brain tumors [ 31 ]. Kebir et al. noted that 18 F-FET PET could be a non-invasive tool for distinguishing pseudoprogression from progressive disease in patients with glioblastoma [ 32 ]. In a study of 36 patients with glioblastoma conducted by Mihovilovic et al., static 18 F-FET PET discriminated between true progression and treatment-related changes, with a sensitivity of 89% and specificity of 75% [ 33 ]. Our study included both primary and recurrent brain tumor patients and demonstrated that 18 F-FET PET/CT was effective in distinguishing false-positive MRI results, which is consistent with the previous literature. This suggests that 18 F-FET PET/CT is an effective complement to MRI, particularly for differentiating between tumorous and non-tumorous lesions. Previous research on 18 F-FET PET/CT has primarily focused on Western countries. This study further validates the applicability of 18 F-FET PET/CT for brain tumors in Asia, providing a reference for practical applications in the region. This study also evaluated the utility of quantitative 18 F-FET PET parameters in differentiating malignant from benign brain lesions. The results showed that the TBRmean values of 18 F-FET PET were significantly higher in glial tumors and other brain tumors compared to benign brain lesions ( p = 0.001 and 0.003, respectively). In contrast, 18 F-FDG PET imaging did not reveal a significant difference in TBRmean values between other brain tumors and benign lesions. Using TBRmean as a adjunct interpretive reference enhanced diagnostic sensitivity and accuracy of 18 F-FET PET. (Table 4 ). Our study results align with those of Galldiks et al. [ 34 ], who also demonstrated high accuracy in using TBR to differentiate primary brain tumors from non-tumorous lesions. The static 18 F-FET PET parameter TBRmean provides additional information on tumor metabolism, which aids in the clinical diagnosis of brain tumors. The use of dynamic curve analysis of 18 F-FET PET images in the diagnosis of brain tumors has been investigated in some studies [ 12 , 35 ]. Galldiks et al. studied the utility of dynamic PET parameter for the diagnosis of recurrent glioma [ 35 ]. In their report, using the curve pattern or TTP as diagnostic criteria resulted in a sensitivity of up to 80% and a PPV of 97%. In our study, we also found that the curve pattern or TTP had a PPV of 95–100% in diagnosing brain tumors, but the sensitivity was only moderate. Although TTP or the dynamic curve pattern has a high PPV, its sensitivity in diagnosis appears to be limited. 18 F-FDG uptake is well-characterized in extracranial tumors and has also been applied to brain tumor imaging for many years. However, the use of FDG for imaging in neuro-oncology has declined in recent years due to several limitations. These include a high rate of glucose metabolism in the normal brain parenchyma, resulting in a diminished signal-to-noise ratio in brain tumors. Another problem with FDG is its high tracer uptake by inflammatory cells, which can occur in a variety of disease processes and is independent of tumor growth or the response. In our study, we found that 18 F-FET PET had significantly higher sensitivity (96.15% vs. 73.08%, p = 0.031) and accuracy (93.90% vs. 72.70%, p = 0.030) than 18 F-FDG PET in detecting primary or recurrent brain tumors. Lau et al. reported that 18 F-FET PET demonstrated greater sensitivity and accuracy than 18 F-FDG PET for detecting malignant brain tumors [ 36 ]. In a meta-analysis, 18 F-FET PET showed higher sensitivity and diagnostic performance than 18 F-FDG PET in diagnosing brain tumors [ 12 ]. Our experience in Taiwan, along with findings from previous studies, suggests that 18 F-FET PET outperforms 18 F-FDG PET in the evaluation of primary and recurrent brain tumors. Additionally, 18 F-FET PET has been reported to be superior to 18 F-FDG-PET for biopsy planning for brain tumors [ 37 ]. In one of our cases, a stereotactic-guided biopsy at the area of highest 18 F-FET uptake led to the diagnosis of a higher-grade recurrent glioma (Figure. 6). Several amino acid PET tracers have been used to enhance diagnostic precision in patients with brain tumors. The earliest amino acid PET studies commonly used C-11-labeled methionine (MET), known for its high specificity in visualizing tumor metabolism, though it requires on-site cyclotron support because of its short half-life [ 38 ]. The development of 18 F-labeled amino acids with longer half-lives has enabled their transport to various medical centers. 18 F-FET has been developed for over 20 years, with many studies on its applications in the diagnosis and treatment of brain tumors. As a result, it has begun to replace 11 C-MET in Western countries, particularly in Europe. Recently, the F-18-labeled fluciclovine (FACBC) has been approved for glioma imaging in the United States and Japan, with studies showing a higher detection rate for glioma recurrence compared to 11 C-MET [ 10 ]. Initially used primarily for diagnosing prostate cancer recurrence, 18 F-FACBC, like other amino acid tracers, also accumulates in non-enhancing gliomas. It has shown good diagnostic accuracy for detecting high-grade glioma [ 39 ] and can assist in determining the extent of glioma resection during surgical planning [ 40 ]. While 18 F-FACBC is partially transported by LAT1, it is primarily mediated by the neutral alanine, serine, and cysteine transporter 2 [ 41 ]. Another 18 F-labeled amino acid analog, 18 F-FDOPA, initially developed to assess dopamine synthesis in the basal ganglia, is increasingly used for imaging brain tumors. 18 F-FDOPA demonstrates good performance for diagnosing primary or recurrent glioma [ 42 ]. However, physiological uptake in the striatum may limit its effectiveness in delineating tumor extent [ 41 ]. This study has some limitations. First, the sample size is relatively small. Second, histopathological data were not available for every patient. Owing to a poor clinical condition or patient refusal, biopsy or surgery could not always be performed, and clinicoradiological criteria were used for the final diagnosis in these cases. Finally, this study was conducted at a single institute, and external validation studies using multi-institutional datasets are necessary to confirm our findings. Conclusion In this prospective study, 18 F-FET PET/CT demonstrated significant potential in enhancing the accuracy of diagnosing patients suspected of having primary or recurrent brain tumors. It can also aid in diagnosing patients with indeterminate MRI findings, correctly identifying 86% of false-positive MRI lesions. Additionally, the imaging parameter TBRmean further refines the diagnostic accuracy of 18 F-FET PET. Future multicenter studies in Asia are recommended to explore the role of 18 F-FET PET/CT in monitoring treatment responses and predicting patient outcomes, thus fully establishing its clinical utility. Declarations Funding Sources: This study was supported by the Ministry of Science and Technology of Taiwan (grant numbers: 109-2314-B-303-015, 110-2314-B-303-014, and 111-2314-B-303-006); the Buddhist Tzu Chi Medical Foundation (grant numbers: TCMF-A 110-03[111] and TCMF-A 110-03[112]); and Hualien Tzu Chi Hospital (grant number: TCRD113-035). Author Contributions: All the authors contributed to the conception and design of the study, participated in writing the manuscript, and approved the final manuscript for submission. Acknowledgements: The authors declare that they have no conflicts of interest. Data Availability: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Reynoso-Noverón N, Mohar-Betancourt A, Ortiz-Rafael J. Epidemiology of brain tumors. Principles of neuro-oncology: brain & skull base. 2021:15–25. Ostrom QT, Francis SS, Barnholtz-Sloan JS. Epidemiology of Brain and Other CNS Tumors. 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Dynamic O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy. Neuro Oncol. 2017;19(2):281–8. Celli M, Caroli P, Amadori E, Arpa D, Gurrieri L, Ghigi G, et al. Diagnostic and Prognostic Potential of (18)F-FET PET in the Differential Diagnosis of Glioma Recurrence and Treatment-Induced Changes After Chemoradiation Therapy. Front Oncol. 2021;11:721821. Bashir A, Brennum J, Broholm H, Law I. The diagnostic accuracy of detecting malignant transformation of low-grade glioma using O-(2-[18F]fluoroethyl)-l-tyrosine positron emission tomography: a retrospective study. J Neurosurg. 2018;130(2):451–64. Granov AT, Schwarz L. T. Positron Emission Tomography: Springer; 2013. Mo Z, Xin J, Chai R, Woo PYM, Chan DTM, Wang J. Epidemiological characteristics and genetic alterations in adult diffuse glioma in East Asian populations. Cancer Biol Med. 2022;19(10):1440–59. Chan AK, Mao Y, Ng HK. TP53 and Histone H3.3 Mutations in Triple-Negative Lower-Grade Gliomas. N Engl J Med. 2016;375(22):2206–8. Zeng C, Wang J, Li M, Wang H, Lou F, Cao S, et al. Comprehensive Molecular Characterization of Chinese Patients with Glioma by Extensive Next-Generation Sequencing Panel Analysis. Cancer Manag Res. 2021;13:3573–88. Kang H, Song SW, Ha J, Won YJ, Park CK, Yoo H, et al. A Nationwide, Population-Based Epidemiology Study of Primary Central Nervous System Tumors in Korea, 2007–2016: A Comparison with United States Data. Cancer Res Treat. 2021;53(2):355–66. Narita Y, Shibui S, Committee of Brain Tumor Registry of Japan Supported by the Japan Neurosurgical S. Trends and outcomes in the treatment of gliomas based on data during 2001–2004 from the Brain Tumor Registry of Japan. Neurol Med Chir (Tokyo). 2015;55(4):286–95. Law I, Albert NL, Arbizu J, Boellaard R, Drzezga A, Galldiks N, et al. Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [(18)F]FDG: version 1.0. Eur J Nucl Med Mol Imaging. 2019;46(3):540–57. Wen PY, van den Bent M, Vogelbaum MA, Chang SM. RANO 2.0: The revised Response Assessment in Neuro-Oncology (RANO) criteria for high- and low-grade glial tumors in adults designed for the future. Neuro Oncol. 2024;26(1):2–4. Dunet V, Rossier C, Buck A, Stupp R, Prior JO. Performance of 18F-fluoro-ethyl-tyrosine (18F-FET) PET for the differential diagnosis of primary brain tumor: a systematic review and Metaanalysis. J Nucl Med. 2012;53(2):207–14. Leung D, Han X, Mikkelsen T, Nabors LB. Role of MRI in primary brain tumor evaluation. J Natl Compr Canc Netw. 2014;12(11):1561–8. Niyazi M, Geisler J, Siefert A, Schwarz SB, Ganswindt U, Garny S, et al. FET-PET for malignant glioma treatment planning. Radiother Oncol. 2011;99(1):44–8. Puranik AD, Boon M, Purandare N, Rangarajan V, Gupta T, Moiyadi A, et al. Utility of FET-PET in detecting high-grade gliomas presenting with equivocal MR imaging features. World J Nucl Med. 2019;18(3):266–72. Kebir S, Fimmers R, Galldiks N, Schafer N, Mack F, Schaub C, et al. Late Pseudoprogression in Glioblastoma: Diagnostic Value of Dynamic O-(2-[18F]fluoroethyl)-L-Tyrosine PET. Clin Cancer Res. 2016;22(9):2190–6. Mihovilovic MI, Kertels O, Hanscheid H, Lohr M, Monoranu CM, Kleinlein I, et al. O-(2-((18)F)fluoroethyl)-L-tyrosine PET for the differentiation of tumour recurrence from late pseudoprogression in glioblastoma. J Neurol Neurosurg Psychiatry. 2019;90(2):238–9. Galldiks N, Dunkl V, Stoffels G, Hutterer M, Rapp M, Sabel M, et al. Diagnosis of pseudoprogression in patients with glioblastoma using O-(2-[18F]fluoroethyl)-L-tyrosine PET. Eur J Nucl Med Mol Imaging. 2015;42(5):685–95. 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 Oncol. 2015;17(9):1293–300. Lau EW, Drummond KJ, Ware RE, Drummond E, Hogg A, Ryan G, et al. Comparative PET study using F-18 FET and F-18 FDG for the evaluation of patients with suspected brain tumour. J Clin Neurosci. 2010;17(1):43–9. Plotkin M, Blechschmidt C, Auf G, Nyuyki F, Geworski L, Denecke T, et al. Comparison of F-18 FET-PET with F-18 FDG-PET for biopsy planning of non-contrast-enhancing gliomas. Eur Radiol. 2010;20(10):2496–502. Dadgar H, Jokar N, Nemati R, Larvie M, Assadi M. PET tracers in glioblastoma: Toward neurotheranostics as an individualized medicine approach. Front Nucl Med. 2023;3:1103262. Castello A, Albano D, Muoio B, Castellani M, Panareo S, Rizzo A et al. Diagnostic Accuracy of PET with (18)F-Fluciclovine ([(18)F]FACBC) in Detecting High-Grade Gliomas: A Systematic Review and Meta-Analysis. Diagnostics (Basel). 2023;13(24). Wakabayashi T, Hirose Y, Miyake K, Arakawa Y, Kagawa N, Nariai T, et al. Determining the extent of tumor resection at surgical planning with (18)F-fluciclovine PET/CT in patients with suspected glioma: multicenter phase III trials. Ann Nucl Med. 2021;35(12):1279–92. Galldiks N, Lohmann P, Fink GR, Langen KJ. Amino Acid PET in Neurooncology. J Nucl Med. 2023;64(5):693–700. Xiao J, Jin Y, Nie J, Chen F, Ma X. Diagnostic and grading accuracy of (18)F-FDOPA PET and PET/CT in patients with gliomas: a systematic review and meta-analysis. BMC Cancer. 2019;19(1):767. Cite Share Download PDF Status: Published Journal Publication published 26 Nov, 2024 Read the published version in Annals of Nuclear Medicine → Version 1 posted Reviewers agreed at journal 11 Nov, 2024 Reviewers invited by journal 11 Nov, 2024 Editor assigned by journal 11 Nov, 2024 First submitted to journal 10 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5062302","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":376668364,"identity":"ecb7e181-e2cc-4881-a61a-895999584045","order_by":0,"name":"Sheng-Chieh Chan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYDACZubGAwkwzgcgZmMnqIWxAaqFmYFxBkgLM0FrgFqgmhmYeSA0fmBwHKjlQc02e90G/mPSNr+2yfMBbfvwMQePlsMghx27nbjtADObdG7fbcM2oG2SM7fh1mIG1sJ2O8EMqOV2bs9tRqAWNmZeglr+3bYHa7HsuW1PnJbEttuMIIfdZvhxO5GgFnuwlj6gXw4zm//sbbid3MbM2IzXL5L9hw8+/PEN6LDjjY8Nfvy5bTu/vfngh494tCAAKDoY20AsxgZi1MPAH1IUj4JRMApGwUgBAHGKVhLhnJ0PAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3236-395X","institution":"Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation","correspondingAuthor":true,"prefix":"","firstName":"Sheng-Chieh","middleName":"","lastName":"Chan","suffix":""},{"id":376668365,"identity":"64befa3e-6b8f-469d-9069-11cb601d332c","order_by":1,"name":"Tsung-Lang Chiu","email":"","orcid":"","institution":"Hualien Tzu Chi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tsung-Lang","middleName":"","lastName":"Chiu","suffix":""},{"id":376668366,"identity":"9baa349d-9f2d-4e5c-b979-7c11ea7f6534","order_by":2,"name":"Shu-Hang Ng","email":"","orcid":"","institution":"Chang Gung Memorial Hospital at Linkou","correspondingAuthor":false,"prefix":"","firstName":"Shu-Hang","middleName":"","lastName":"Ng","suffix":""},{"id":376668367,"identity":"cb2f70e7-95e0-4a83-be67-9cddefafd6e3","order_by":3,"name":"Sheng-Tzung Tsai","email":"","orcid":"","institution":"Hualien Tzu Chi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sheng-Tzung","middleName":"","lastName":"Tsai","suffix":""},{"id":376668368,"identity":"4e698fb4-273f-4736-b6f0-5af32e48eb34","order_by":4,"name":"Hung-Wen Kao","email":"","orcid":"","institution":"Hualien Tzu Chi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hung-Wen","middleName":"","lastName":"Kao","suffix":""},{"id":376668369,"identity":"7f6467ff-1059-41c4-b177-152356b576e2","order_by":5,"name":"Shu-Hsin Liu","email":"","orcid":"","institution":"Hualien Tzu Chi Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shu-Hsin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-09-10 06:39:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5062302/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5062302/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12149-024-02005-4","type":"published","date":"2024-11-26T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68885055,"identity":"e8324c4c-7dac-4c39-a7d4-b993e0dcb03f","added_by":"auto","created_at":"2024-11-13 06:32:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58811,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart summarizing \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT results. \u003cem\u003eFN\u003c/em\u003e false-negative, \u003cem\u003eTP\u003c/em\u003e true-positive, \u003cem\u003eTN\u003c/em\u003e true-negative, \u003cem\u003eFP\u003c/em\u003e false-positive\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/d20badcbc876af0c4c166680.png"},{"id":68885053,"identity":"6310494b-e6b7-410e-8e7c-43889af2acf0","added_by":"auto","created_at":"2024-11-13 06:32:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170946,"visible":true,"origin":"","legend":"\u003cp\u003eA patient with glioblastoma following surgical removal with indeterminate post-treatment MRI findings. The surgical field in the right anterior temporal lobe shows heterogeneous signal intensities with uneven contrast enhancement (a) and FLAIR hyperintensities (b). The corresponding \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT scan (c, d) shows mildly increased radioactivity (TBR=1.41, SUVmax=3.84). The time activity curve exhibits an initially high arterial input, followed by a gradually increasing accumulative pattern (e). Areas of high FDG uptake are still visible on the \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT images (f, g; SUVmax=7.57). Follow-up imaging studies confirmed that this was a benign lesion. \u003cem\u003eMRI \u003c/em\u003emagnetic resonance imaging, \u003cem\u003eFLAIR \u003c/em\u003efluid-attenuated inversion recovery, \u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eF-FET \u003c/em\u003efluorine-18-labeled fluoroethyltyrosine, \u003cem\u003ePET \u003c/em\u003epositron emission tomography, \u003cem\u003eCT\u003c/em\u003e computed tomography, \u003cem\u003eTBR \u003c/em\u003etumor-to-background ratio, \u003cem\u003eSUV \u003c/em\u003estandardized uptake value, \u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eF-FDG \u003c/em\u003efluorine-18 fluorodeoxyglucose\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/e90aec968b3957391431b3f6.png"},{"id":68885062,"identity":"8478c088-2fa4-431b-9a80-1e229d16b8b7","added_by":"auto","created_at":"2024-11-13 06:32:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":148943,"visible":true,"origin":"","legend":"\u003cp\u003eA suspected recurrent tumor in the left occipital lobe and corpus callosum was identified on the MRI of a glioblastoma patient. The surgical cavity shows thick irregular peripheral contrast enhancement (a), a thick splenium of the corpus callosum, and FLAIR hyperintensities in the right parieto-occipital periventricular white matter (b). The \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT scan exhibits only slight uptake in the left occipital region (c, d, TBR=1.74, SUVmax=3.92) with an accumulative curve pattern (e). The corresponding \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT images also show mild uptake (f, g). Follow-up imaging confirmed that the lesion was non-malignant. \u003cem\u003eMRI \u003c/em\u003emagnetic resonance imaging, \u003cem\u003eFLAIR \u003c/em\u003efluid-attenuated inversion recovery, \u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eF-FET \u003c/em\u003efluorine-18-labeled fluoroethyltyrosine, \u003cem\u003ePET \u003c/em\u003epositron emission tomography, \u003cem\u003eCT\u003c/em\u003e computed tomography, \u003cem\u003eTBR \u003c/em\u003etumor-to-background ratio, \u003cem\u003eSUV \u003c/em\u003estandardized uptake value, \u003csup\u003e\u003cem\u003e18\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eF-FDG \u003c/em\u003efluorine-18 fluorodeoxyglucose\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/4cc444275601059b826035d0.png"},{"id":68885058,"identity":"551ba250-0e16-497b-9071-17fa3fa01767","added_by":"auto","created_at":"2024-11-13 06:32:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6767524,"visible":true,"origin":"","legend":"\u003cp\u003eA false-positive \u003csup\u003e18\u003c/sup\u003eF-FET/CT result in a 44-year-old patient with questionable MRI findings for brain tumor. A previous biopsy had yielded inconclusive pathology results. The axial contrast-enhanced MR image (b) reveals heterogeneous signal intensities with uneven contrast enhancement, while the corresponding FLAIR MR image (b) shows a hyperintense mass in the right frontal lobe. The lesion demonstrates heterogeneously increased uptake on the \u003csup\u003e18\u003c/sup\u003eF-FET PET scan (c), with a TBR of 3.09, and on the \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT scan (d), with an SUVmax of 5.41. A subsequent biopsy confirmed the diagnosis of tumefactive demyelination.\u003c/p\u003e","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/9b41b41cab53c8b28ea5cd0b.png"},{"id":68886442,"identity":"b3467a94-b259-474b-aeed-67db3d7051fa","added_by":"auto","created_at":"2024-11-13 06:40:44","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":207188,"visible":true,"origin":"","legend":"\u003cp\u003eThe boxplot of the TBR of the lesions on the \u003csup\u003e18\u003c/sup\u003eF-FET PET image. Gliomas or other brain tumors had significantly higher TBRs than benign brain lesions.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/63de9453306f1bf0280679ec.jpg"},{"id":68885054,"identity":"8db4e1dc-2b9a-4db9-bb0a-416a4c0fc7e6","added_by":"auto","created_at":"2024-11-13 06:32:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":128742,"visible":true,"origin":"","legend":"\u003cp\u003eThis patient had a history of grade II oligodendroglioma that was previously surgically removed and treated with adjuvant radiotherapy. Tumor recurrence was suspected on follow-up MRI in the left middle frontal lobe. The filtrating tumor shows mild contrast enhancement (a) and extensive T2 hyperintensity (b). The \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT scan shows increased radioactivity in this region (c, d; TBR=7.33, SUVmax=8.67), and the time-activity curve demonstrates a wash-out pattern. We performed a stereotactic-guided biopsy at the area with the highest \u003csup\u003e18\u003c/sup\u003eF-FET uptake, and histological examination confirmed the diagnosis of anaplastic oligodendroglioma, IDH mutant, grade III. The tumor boundaries are not well-defined on the \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT images (f, g).\u003c/p\u003e","description":"","filename":"OnlineFig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/b7492e67de189ae93a5b7251.png"},{"id":68885057,"identity":"b38d027f-341f-4475-9446-282d389db515","added_by":"auto","created_at":"2024-11-13 06:32:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72274,"visible":true,"origin":"","legend":"\u003cp\u003eThis is a case of primary glioblastoma in the left frontal lobe. The large heterogeneous mass shows central necrosis with irregular peripheral contrast enhancement (a) and significant perifocal FLAIR hyperintensity (b). On the \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT scan, a brain tumor could not be confirmed visually because the uptake of \u003csup\u003e18\u003c/sup\u003eF-FET was not obvious (c, d). However, semiquantitative analysis showed a TBR higher than 2.0 (TBR=2.34), leading to the impression of a malignant tumor.\u003c/p\u003e","description":"","filename":"OnlineFig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/7de048e206b8267f9594216e.png"},{"id":70388765,"identity":"3a69cc1b-9182-472c-a890-bf88382f8565","added_by":"auto","created_at":"2024-12-02 17:27:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1669247,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5062302/v1/80f130ef-9907-4489-a74f-4d98c9d04e42.pdf"}],"financialInterests":"","formattedTitle":"18F-FET PET/CT Can Aid in Diagnosing Patients with Indeterminate MRI Findings for Brain Tumors: A Prospective Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBrain tumors are a highly heterogeneous group that encompass various types of central nervous system (CNS) tumors. According to recent epidemiological data, the global incidence is 3.9 per 100,000 person-years for all CNS tumors, and they can be either benign or malignant [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of brain tumors is higher in developed countries, such as North America, Western Europe, and Australia, with an annual incidence of approximately 6 to 11 new cases per 100,000 people in the United States [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Its incidence varies globally; therefore, understanding the specific situation in other countries is crucial for local healthcare planning and resource allocation. The annual incidence of primary malignant brain tumors is approximately 2.73\u0026ndash;3.75 per 100,000 people in Taiwan, showing an increasing trend over the years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGliomas represent a particularly challenging subset of brain tumors, accounting for 81% of all malignant brain tumors in adults. Glioblastoma multiforme (GBM), the most aggressive form, has a dismal five-year survival rate of approximately 5% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This poor prognosis underscores the urgent need for improved diagnostic and management strategies for patients with gliomas. Consequently, the identification of more effective diagnostic and treatment methods has become an important research focus.\u003c/p\u003e \u003cp\u003eMagnetic resonance imaging (MRI) is the primary diagnostic tool for brain tumors and provides detailed anatomical information. However, it has limitations in differentiating tumor tissue from edema or necrosis, and distinguishing tumor recurrence from post-treatment changes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. MRI also struggles to identify low-grade tumors and non-enhancing components, potentially leading to an incomplete assessment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Consequently, there is a need for complementary imaging modalities to address these shortcomings and improve brain tumor diagnosis and management [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFluorine-18-labeled fluoroethyltyrosine (\u003csup\u003e18\u003c/sup\u003eF-FET) positron emission tomography (PET) has emerged as a promising non-invasive imaging technique for brain tumor diagnosis. As a radiolabeled amino acid analog, \u003csup\u003e18\u003c/sup\u003eF-FET offers advantages over traditional fluorine-18 fluorodeoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG) PET, particularly in distinguishing tumors from inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Studies have demonstrated its value in assessing tumor malignancy, surgical planning, and evaluating radiotherapy effects [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. \u003csup\u003e18\u003c/sup\u003eF-FET PET accurately displays tumor metabolic activity, aiding in tumor boundary delineation and improving resection outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Its low uptake in post-treatment inflammation also makes it useful for monitoring recurrence and distinguishing between necrosis and recurrence after treatment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these advantages, the application of \u003csup\u003e18\u003c/sup\u003eF-FET PET faces several challenges. These include potential limitations in diagnosing low-grade brain tumors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and high costs, which limit its widespread clinical application [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, gliomas in East Asian populations differ in epidemiology and genomic characteristics compared to those in other ancestry groups. For instance, East Asian glioblastoma patients typically experience a significantly younger age of onset and longer overall survival than White patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. East Asian cohorts also show a lower incidence of epidermal growth factor receptor amplification in glioblastoma and a higher incidence of 1p19q-IDH-TERT triple-negative low-grade glioma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Studies further reveal survival rate differences among East Asian countries. For instance, the five-year relative survival rate for anaplastic astrocytoma ranges from 25.2\u0026ndash;26.2% in South Korea, is 22.1% in Taiwan, but reaches 41.1% in Japan [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Although significant research on \u003csup\u003e18\u003c/sup\u003eF-FET PET has been conducted in Europe and the United States, fewer prospective studies or clinical applications are available in Asian countries. It remains uncertain whether the findings from Europe and the United States can be generalized in Asia or across different Asian countries.\u003c/p\u003e \u003cp\u003eWe conducted this study with the aim to prospectively explore the application of \u003csup\u003e18\u003c/sup\u003eF-FET PET in the diagnosis and management of patients with brain tumors in an Ascian cohort.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient population\u003c/h2\u003e \u003cp\u003eBetween September 2019 and December 2023, 33 patients suspected of having primary or recurrent brain tumors on MRI or who were undergoing restaging for glioma were consecutively recruited. The patients underwent both \u003csup\u003e18\u003c/sup\u003eF-FET and \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT. Each PET study pair was performed within a 3-day interval. This study was approved by the Institutional Review Board of Hualien Tzu Chi Hospital (IRB108-262-A), registered on CinicalTrial.gov (NCT06563024), and conducted in accordance with the 1964 Declaration of Helsinki and all subsequent revisions. All patients provided written informed consent prior to their inclusion in this study. The general characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of the study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54\u0026thinsp;\u0026plusmn;\u0026thinsp;14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReason for the PET scan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected brain tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuspected glioma recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlioma restaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal diagnosis of suspected brain tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther brain tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetastasis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother primary tumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign lesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal diagnosis of suspected glioma recurrence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrent glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign lesion (post-treatment change)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as the count (percentage) unless otherwise indicated. \u003cem\u003eSD\u003c/em\u003e standard deviation, \u003cem\u003ePET\u003c/em\u003e positron emission tomography\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePET/CT scan\u003c/h3\u003e\n\u003cp\u003eThe \u003csup\u003e18\u003c/sup\u003eF-FET and \u003csup\u003e18\u003c/sup\u003eF-FDG were synthesized at a cyclotron center at our hospital. The patients were asked to fast for a minimum of 6 h prior to the injection of either PET tracer. \u003csup\u003e18\u003c/sup\u003eF-FET and \u003csup\u003e18\u003c/sup\u003eF-FDG scans were performed on separate days within one week of each other. Blood glucose levels on the day of FDG-PET were less than 150 mg/dL in all patients. The scans were obtained using a Discovery MI PET/CT scanner (GE Medical Systems, Milwaukee, WI, USA). Dynamic PET studies were performed up to 60 min after intravenous injection of approximately 200 MBq \u003csup\u003e18\u003c/sup\u003eF -FET or \u003csup\u003e18\u003c/sup\u003eF-FDG. Images were processed with the measured attenuated correction obtained from contemporaneous non-contrast CT. CT and PET scans were also coregistered using proprietary fusion software to allow for anatomical correlation (Xeleris Workstation; GE Medical Systems).\u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eThe region of interest (ROI) for analysis was determined on the slices showing the highest \u003csup\u003e18\u003c/sup\u003eF-FET or \u003csup\u003e18\u003c/sup\u003eF-FDG accumulation in the tumors. The nuclear medicine physician placed the three-dimensional volumetric ROI in the axial, coronal, and sagittal planes. For conventional semiquantitative evaluation, \u003csup\u003e18\u003c/sup\u003eF-FET or \u003csup\u003e18\u003c/sup\u003eF-FDG standardized uptake value (SUV) in the tumor or the tumor-to-background ratio (TBR) was determined on a summation image (20\u0026ndash;40 min after injection) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. TBR was calculated using the following formula, and the background region was selected from the contralateral hemisphere of the normal-appearing brain tissue.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\mathbf{T}\\mathbf{B}\\mathbf{R}=\\frac{\\mathbf{S}\\mathbf{U}\\mathbf{V}\\mathbf{t}\\mathbf{u}\\mathbf{m}\\mathbf{o}\\mathbf{r}}{\\mathbf{S}\\mathbf{U}\\mathbf{V}\\mathbf{b}\\mathbf{a}\\mathbf{c}\\mathbf{k}\\mathbf{g}\\mathbf{r}\\mathbf{o}\\mathbf{u}\\mathbf{n}\\mathbf{d}}\\text{}\\text{}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTime-activity curves (TACs) of the mean SUV of \u003csup\u003e18\u003c/sup\u003eF-FET uptake in the tumor and brain were generated by applying a spherical volume of interest (VOI) of 2 mL, centered on the area of maximal tumor uptake. A reference VOI was applied to unaffected brain tissue, covering the entire dynamic dataset. TACs, representing the mean tissue radioactivity in the tumor VOI as a function of time, were generated from dynamic \u003csup\u003e18\u003c/sup\u003eF-FET PET images. The TAC shape was classified as increasing (accumulative) or decreasing (washout).\u003c/p\u003e \u003cp\u003eIn a visual analysis, foci of increased uptake were evaluated and the uptake scored on a 5-point scale: 0\u0026thinsp;=\u0026thinsp;no abnormal uptake, 1\u0026thinsp;=\u0026thinsp;benign, 2\u0026thinsp;=\u0026thinsp;probably benign, 3\u0026thinsp;=\u0026thinsp;probably malignant, and 4\u0026thinsp;=\u0026thinsp;definitely malignant. Both grade 3 and grade 4 were considered to indicate positive findings.\u003c/p\u003e\n\u003ch3\u003eHistopathological and imaging-based assessment for classifying brain lesions\u003c/h3\u003e\n\u003cp\u003eThe nature of brain lesions was primarily determined through histopathological verification. Histological classification and tumor grading were performed according to the WHO guidelines current at the respective date of histopathological assessment. When histopathological confirmation was not feasible, follow-up MR imaging results were used. The final diagnosis was established either through histopathological examination or with clinical and imaging follow-up for at least one year. Assessment of treatment response in gliomas was based on the Response Assessment in Neuro- Oncology (RANO) criteria. Pseudoprogression was defined as new or increasing contrast enhancement that eventually subsides without any change in therapy [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of \u003csup\u003e18\u003c/sup\u003eF-FET PET and \u003csup\u003e18\u003c/sup\u003eF-FDG PET were calculated and the results were compared using McNemar\u0026rsquo;s Chi-square test. Semi-qualitative or qualitative PET parameters were compared using the Mann\u0026ndash;Whitney U test. All statistical analyses were conducted using SPSS software version 20. Statistical significance was defined as a two-tailed P-value of \u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003csup\u003e \u003cb\u003e18\u003c/b\u003e \u003c/sup\u003e \u003cb\u003eF-FET uptake and PET diagnostic performance for false-positive MRI lesions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 33 patients were enrolled in the study due to suspected brain tumors or glioma recurrence based on MRI. Patients were enrolled for the following reasons: suspected brain tumor on MRI (n\u0026thinsp;=\u0026thinsp;20), suspected glioma recurrence (n\u0026thinsp;=\u0026thinsp;8), or restaging for glioma (n\u0026thinsp;=\u0026thinsp;5) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT results. For the eight patients suspected of glioma recurrence, six were confirmed to have tumor recurrence and two had post-treatment inflammatory changes; \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT accurately demonstrated increased \u003csup\u003e18\u003c/sup\u003eF-FET uptake in recurrent tumors and showed only mild \u003csup\u003e18\u003c/sup\u003eF-FET uptake in cases of post-treatment inflammatory changes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the 20 patients with suspected brain tumors, six were confirmed to have gliomas, nine had other brain tumors, and five had benign lesions. \u003csup\u003e18\u003c/sup\u003eF-FET PET demonstrated significantly increased uptake in five of the six patients with confirmed gliomas and in all nine patients with other brain tumors. A GBM (small, thin-walled) was identified as a false-negative result. Among the five patients with benign lesions, four exhibited low \u003csup\u003e18\u003c/sup\u003eF-FET uptake, while one case of tumefactive demyelination produced a false-positive finding (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the restaging group, all patients demonstrated markedly increased increased \u003csup\u003e18\u003c/sup\u003eF-FET uptake.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the 28 patients suspected of having brain tumors or glioma recurrence based on MRI, 25% (7 out of 28) had false-positive MRI results. Of note, \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT accurately identified 86% (6 out of 7) of these false-positive MRI lesions.\u003c/p\u003e\n\u003ch3\u003eComparison of the diagnostic performance of F-FET PET and F-FDG PET\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the patient-based diagnostic performance metrics for \u003csup\u003e18\u003c/sup\u003eF-FET PET and \u003csup\u003e18\u003c/sup\u003eF-FDG PET based on visual analysis. The sensitivity, specificity, PPV, NPV, and accuracy of \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT were 96.15%, 85.71%, 96.15%, 85.71%, and 93.90%, respectively. The corresponding \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT values were 73.08%, 71.42%, 90.48%, 41.67%, and 72.70%. \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT had significantly higher sensitivity and accuracy than \u003csup\u003e18\u003c/sup\u003eF-FDG PET (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031 and 0.030, respectively).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative accuracy of fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) and O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) PET in diagnosing suspected brain tumors or glioma recurrence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAcc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18F-FET PET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18F-FDG PET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e90.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFN\u003c/em\u003e false-negative, \u003cem\u003eTP\u003c/em\u003e true-positive, \u003cem\u003eTN\u003c/em\u003e true-negative, \u003cem\u003eFP\u003c/em\u003e false-positive, \u003cem\u003eSen\u003c/em\u003e sensitivity, \u003cem\u003eSpe\u003c/em\u003e specificity, \u003cem\u003eAcc\u003c/em\u003e accuracy, \u003cem\u003ePPV\u003c/em\u003e positive predictive value, \u003cem\u003eNPV\u003c/em\u003e negative predictive value, \u003cem\u003eAUC\u003c/em\u003e area under curve Sensitivity, \u003csup\u003e18\u003c/sup\u003eF-FET PET versus \u003csup\u003e18\u003c/sup\u003eF-FDG PET, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031 Accuracy, \u003csup\u003e18\u003c/sup\u003eF-FET PET versus \u003csup\u003e18\u003c/sup\u003eF-FDG PET, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eQualitative analysis of PET imaging\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the quantitative analysis of \u003csup\u003e18\u003c/sup\u003eF-FET PET and \u003csup\u003e18\u003c/sup\u003eF-FDG PET parameters. The TBRmean values for glial tumors (primary or recurrent glioma), other brain tumors, and benign lesions on \u003csup\u003e18\u003c/sup\u003eF-FET PET were 3.64, 3.10, and 1.22, respectively. Corresponding TBRmean values for \u003csup\u003e18\u003c/sup\u003eF-FDG PET were 1.73, 1.17, and 0.70, respectively. The TBR was significantly higher with \u003csup\u003e18\u003c/sup\u003eF-FET PET than with \u003csup\u003e18\u003c/sup\u003eF-FDG PET (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), regardless of lesion type. On \u003csup\u003e18\u003c/sup\u003eF-FET PET, the TBRmean for gliomas and other brain tumors was markedly higher than that of benign lesions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and 0.003, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In comparison, although the TBRmean for gliomas on \u003csup\u003e18\u003c/sup\u003eF-FDG PET was elevated compared to benign lesions, there was no significant difference between other brain tumors and benign lesions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of 18F-FET PET and 18F-FDG PET parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatients (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTBRmean (SD) on \u003csup\u003e18\u003c/sup\u003eF-FET PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSUVmax (SD) on \u003csup\u003e18\u003c/sup\u003eF-FET PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTTP (SD) on \u003csup\u003e18\u003c/sup\u003eF-FET PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDynamic curve pattern on \u003csup\u003e18\u003c/sup\u003eF-FET PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTBRmean (SD) on \u003csup\u003e18\u003c/sup\u003eF-FDG PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSUVmax (SD) on \u003csup\u003e18\u003c/sup\u003eF-FDG PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTTP (SD) on \u003csup\u003e18\u003c/sup\u003eF-FDG PET\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDynamic curve pattern on \u003csup\u003e18\u003c/sup\u003eF-FDG PET\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlial tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003cp\u003e(1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003cp\u003e(2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1414\u003c/p\u003e \u003cp\u003e(960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWash-out (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e \u003cp\u003eAccumulative (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003cp\u003e(0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.56\u003c/p\u003e \u003cp\u003e(4.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3263\u003c/p\u003e \u003cp\u003e(150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAccumulative (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003cp\u003eNot available\u003csup\u003ea\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther brain tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003cp\u003e(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003cp\u003e(2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1130\u003c/p\u003e \u003cp\u003e(1233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWash-out (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e \u003cp\u003eAccumulative (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003cp\u003e(0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003cp\u003e(3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3263\u003c/p\u003e \u003cp\u003e(106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAccumulative (n\u0026thinsp;=\u0026thinsp;9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenign brain lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003cp\u003e(1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003cp\u003e(1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2160\u003c/p\u003e \u003cp\u003e(805)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccumulative (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e(0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003cp\u003e(1.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3150\u003c/p\u003e \u003cp\u003e(300)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAccumulative (n\u0026thinsp;=\u0026thinsp;7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;0.672\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;0.001\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;0.491\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;0.093\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;0.339\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;0.140\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;0.095\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;0.019\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;0.628\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;0.362\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e1\u0026thinsp;=\u0026thinsp;0.834\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e2\u0026thinsp;=\u0026thinsp;0.617\u003c/p\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e3\u0026thinsp;=\u0026thinsp;0.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Not available due to patient movement during the scan\u003c/p\u003e \u003cp\u003e\u003cem\u003ep1\u003c/em\u003e glial versus non-glial, \u003cem\u003ep2\u003c/em\u003e glial versus inflammatory, \u003cem\u003ep3\u003c/em\u003e non-glial versus inflammatory, \u003cem\u003e18F-FET\u003c/em\u003e fluorine-18-labeled fluoroethyltyrosine, \u003cem\u003ePET\u003c/em\u003e positron emission tomography, \u003cem\u003eTBR\u003c/em\u003e tumor-to-background ratio, \u003cem\u003eSUV\u003c/em\u003e standardized uptake value, \u003cem\u003e18F-FDG\u003c/em\u003e fluorine-18 fluorodeoxyglucose, \u003cem\u003eTTP\u003c/em\u003e time-to-peak, \u003cem\u003eSD\u003c/em\u003e standard deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the analysis of \u003csup\u003e18\u003c/sup\u003eF-FET PET dynamic curves, the wash-out curve pattern was observed only in glial tumors or other brain tumors (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), while all benign inflammatory lesions demonstrated an accumulative pattern. The time-to-peak (TTP) values for glial and other brain tumors were lower than those for benign lesions, but the difference was not statistically significant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith TBRmean as an adjunct interpretive reference [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the diagnostic accuracy of \u003csup\u003e18\u003c/sup\u003eF-FET PET improved, showing sensitivity, specificity, PPV, and NPV values of 100%, 85.71%, 96.30%, and 100%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). In contrast, using the washout curve pattern or TTP as interpretation criteria resulted in a high PPV but moderate sensitivity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative accuracy of 18F-FET PET based on different interpretation criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterpretation criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e93.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBRmean\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTTP\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDynamic curve pattern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisual\u0026thinsp;+\u0026thinsp;TBRmean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSen\u003c/em\u003e sensitivity, \u003cem\u003eSpe\u003c/em\u003e specificity, \u003cem\u003eAcc\u003c/em\u003e accuracy, \u003cem\u003ePPV\u003c/em\u003e positive predictive value, \u003cem\u003eNPV\u003c/em\u003e negative predictive value, \u003cem\u003eAUC\u003c/em\u003e area under curve, \u003cem\u003e18F-FET\u003c/em\u003e fluorine-18-labeled fluoroethyltyrosine, \u003cem\u003ePET\u003c/em\u003e positron emission tomography, \u003cem\u003eTBR\u003c/em\u003e tumor-to-background ratio, \u003cem\u003eTTP\u003c/em\u003e time-to-peak\u003c/p\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e TBRmean\u0026thinsp;=\u0026thinsp;2.0 as the cut-off value [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e TTP\u0026thinsp;=\u0026thinsp;45 min as the cut-off value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective study evaluated the use of \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT for diagnosing and treating brain tumors in an Asian population. We found that \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT can assist clinicians in making more accurate diagnoses. Among patients suspected of having brain tumors or glioma recurrence, 25% had false-positive MRI results, of which 86% were accurately identified with \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT. The use of \u003csup\u003e18\u003c/sup\u003eF-FET PET parameters, especially TBRmean, further improved diagnostic sensitivity and accuracy to 100% and 97%, respectively. Compared to \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT, \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT showed superior sensitivity and accuracy, with a significantly higher TBR, making it more effective in distinguishing tumors from inflammatory lesions. Overall, \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT proves to be a valuable tool for evaluating brain tumors, offering clear advantages over \u003csup\u003e18\u003c/sup\u003eF-FDG PET, particularly in diagnosing patients with indeterminate MRI findings.\u003c/p\u003e \u003cp\u003eThe standard-of-care neuroimaging modality for the detection of brain tumors is contrast-enhanced MRI, which is a critical component of the clinical management strategy, from diagnosis to prognosis and treatment response assessment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, MRI has limitations in diagnosing brain tumor lesions, including challenges in distinguishing specific tumor types, unclear tumor boundaries, and limited sensitivity in detecting small tumors [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. There are also some limitations to the use of standard MRI for response assessment and treatment monitoring. Given these limitations, there is clearly a need for more sophisticated and advanced neuroimaging modalities that can augment and resolve the gaps in existing standard imaging techniques. Metabolic imaging using PET with the amino acid analogue \u003csup\u003e18\u003c/sup\u003eF-FET has gained increasing importance for the imaging of gliomas. \u003csup\u003e18\u003c/sup\u003eF-FET PET has shown potential in treatment planning and therapy monitoring in glioma [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Of the patients suspected of having brain tumors or glioma recurrence on MRI in this study, 25% had false-positive MRI results. \u003csup\u003e18\u003c/sup\u003eF-FET PET correctly identified most of these false-positive MRI lesions. Thus, \u003csup\u003e18\u003c/sup\u003eF-FET PET is effective in identifying patients suspected of having primary brain tumors who actually have benign lesions, or differentiate which recurrent patients are experiencing pseudoprogression (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Puranik et al. reported that \u003csup\u003e18\u003c/sup\u003eF-FET PET complemented MRI in the detection of primary brain tumors [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Kebir et al. noted that \u003csup\u003e18\u003c/sup\u003eF-FET PET could be a non-invasive tool for distinguishing pseudoprogression from progressive disease in patients with glioblastoma [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In a study of 36 patients with glioblastoma conducted by Mihovilovic et al., static \u003csup\u003e18\u003c/sup\u003eF-FET PET discriminated between true progression and treatment-related changes, with a sensitivity of 89% and specificity of 75% [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our study included both primary and recurrent brain tumor patients and demonstrated that \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT was effective in distinguishing false-positive MRI results, which is consistent with the previous literature. This suggests that \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT is an effective complement to MRI, particularly for differentiating between tumorous and non-tumorous lesions. Previous research on \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT has primarily focused on Western countries. This study further validates the applicability of \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT for brain tumors in Asia, providing a reference for practical applications in the region.\u003c/p\u003e \u003cp\u003eThis study also evaluated the utility of quantitative \u003csup\u003e18\u003c/sup\u003eF-FET PET parameters in differentiating malignant from benign brain lesions. The results showed that the TBRmean values of \u003csup\u003e18\u003c/sup\u003eF-FET PET were significantly higher in glial tumors and other brain tumors compared to benign brain lesions (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and 0.003, respectively). In contrast, \u003csup\u003e18\u003c/sup\u003eF-FDG PET imaging did not reveal a significant difference in TBRmean values between other brain tumors and benign lesions. Using TBRmean as a adjunct interpretive reference enhanced diagnostic sensitivity and accuracy of \u003csup\u003e18\u003c/sup\u003eF-FET PET. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Our study results align with those of Galldiks et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], who also demonstrated high accuracy in using TBR to differentiate primary brain tumors from non-tumorous lesions. The static \u003csup\u003e18\u003c/sup\u003eF-FET PET parameter TBRmean provides additional information on tumor metabolism, which aids in the clinical diagnosis of brain tumors.\u003c/p\u003e \u003cp\u003eThe use of dynamic curve analysis of \u003csup\u003e18\u003c/sup\u003eF-FET PET images in the diagnosis of brain tumors has been investigated in some studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Galldiks et al. studied the utility of dynamic PET parameter for the diagnosis of recurrent glioma [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In their report, using the curve pattern or TTP as diagnostic criteria resulted in a sensitivity of up to 80% and a PPV of 97%. In our study, we also found that the curve pattern or TTP had a PPV of 95\u0026ndash;100% in diagnosing brain tumors, but the sensitivity was only moderate. Although TTP or the dynamic curve pattern has a high PPV, its sensitivity in diagnosis appears to be limited.\u003c/p\u003e \u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-FDG uptake is well-characterized in extracranial tumors and has also been applied to brain tumor imaging for many years. However, the use of FDG for imaging in neuro-oncology has declined in recent years due to several limitations. These include a high rate of glucose metabolism in the normal brain parenchyma, resulting in a diminished signal-to-noise ratio in brain tumors. Another problem with FDG is its high tracer uptake by inflammatory cells, which can occur in a variety of disease processes and is independent of tumor growth or the response. In our study, we found that \u003csup\u003e18\u003c/sup\u003eF-FET PET had significantly higher sensitivity (96.15% vs. 73.08%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) and accuracy (93.90% vs. 72.70%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030) than \u003csup\u003e18\u003c/sup\u003eF-FDG PET in detecting primary or recurrent brain tumors. Lau et al. reported that \u003csup\u003e18\u003c/sup\u003eF-FET PET demonstrated greater sensitivity and accuracy than \u003csup\u003e18\u003c/sup\u003eF-FDG PET for detecting malignant brain tumors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In a meta-analysis, \u003csup\u003e18\u003c/sup\u003eF-FET PET showed higher sensitivity and diagnostic performance than \u003csup\u003e18\u003c/sup\u003eF-FDG PET in diagnosing brain tumors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Our experience in Taiwan, along with findings from previous studies, suggests that \u003csup\u003e18\u003c/sup\u003eF-FET PET outperforms \u003csup\u003e18\u003c/sup\u003eF-FDG PET in the evaluation of primary and recurrent brain tumors. Additionally, \u003csup\u003e18\u003c/sup\u003eF-FET PET has been reported to be superior to \u003csup\u003e18\u003c/sup\u003eF-FDG-PET for biopsy planning for brain tumors [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In one of our cases, a stereotactic-guided biopsy at the area of highest \u003csup\u003e18\u003c/sup\u003eF-FET uptake led to the diagnosis of a higher-grade recurrent glioma (Figure. 6).\u003c/p\u003e \u003cp\u003eSeveral amino acid PET tracers have been used to enhance diagnostic precision in patients with brain tumors. The earliest amino acid PET studies commonly used C-11-labeled methionine (MET), known for its high specificity in visualizing tumor metabolism, though it requires on-site cyclotron support because of its short half-life [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The development of \u003csup\u003e18\u003c/sup\u003eF-labeled amino acids with longer half-lives has enabled their transport to various medical centers. \u003csup\u003e18\u003c/sup\u003eF-FET has been developed for over 20 years, with many studies on its applications in the diagnosis and treatment of brain tumors. As a result, it has begun to replace \u003csup\u003e11\u003c/sup\u003eC-MET in Western countries, particularly in Europe. Recently, the F-18-labeled fluciclovine (FACBC) has been approved for glioma imaging in the United States and Japan, with studies showing a higher detection rate for glioma recurrence compared to \u003csup\u003e11\u003c/sup\u003eC-MET [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Initially used primarily for diagnosing prostate cancer recurrence, \u003csup\u003e18\u003c/sup\u003eF-FACBC, like other amino acid tracers, also accumulates in non-enhancing gliomas. It has shown good diagnostic accuracy for detecting high-grade glioma [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and can assist in determining the extent of glioma resection during surgical planning [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. While \u003csup\u003e18\u003c/sup\u003eF-FACBC is partially transported by LAT1, it is primarily mediated by the neutral alanine, serine, and cysteine transporter 2 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Another \u003csup\u003e18\u003c/sup\u003eF-labeled amino acid analog, \u003csup\u003e18\u003c/sup\u003eF-FDOPA, initially developed to assess dopamine synthesis in the basal ganglia, is increasingly used for imaging brain tumors. \u003csup\u003e18\u003c/sup\u003eF-FDOPA demonstrates good performance for diagnosing primary or recurrent glioma [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, physiological uptake in the striatum may limit its effectiveness in delineating tumor extent [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has some limitations. First, the sample size is relatively small. Second, histopathological data were not available for every patient. Owing to a poor clinical condition or patient refusal, biopsy or surgery could not always be performed, and clinicoradiological criteria were used for the final diagnosis in these cases. Finally, this study was conducted at a single institute, and external validation studies using multi-institutional datasets are necessary to confirm our findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this prospective study, \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT demonstrated significant potential in enhancing the accuracy of diagnosing patients suspected of having primary or recurrent brain tumors. It can also aid in diagnosing patients with indeterminate MRI findings, correctly identifying 86% of false-positive MRI lesions. Additionally, the imaging parameter TBRmean further refines the diagnostic accuracy of \u003csup\u003e18\u003c/sup\u003eF-FET PET. Future multicenter studies in Asia are recommended to explore the role of \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT in monitoring treatment responses and predicting patient outcomes, thus fully establishing its clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Sources:\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Ministry of Science and Technology of Taiwan (grant numbers: 109-2314-B-303-015, 110-2314-B-303-014, and 111-2314-B-303-006); the Buddhist Tzu Chi Medical Foundation (grant numbers: TCMF-A 110-03[111] and TCMF-A 110-03[112]); and Hualien Tzu Chi Hospital (grant number: TCRD113-035).\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\n\u003cp\u003eAll the authors contributed to the conception and design of the study, participated in writing the manuscript, and approved the final manuscript for submission.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eData Availability:\u003c/h2\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eReynoso-Nover\u0026oacute;n N, Mohar-Betancourt A, Ortiz-Rafael J. 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TP53 and Histone H3.3 Mutations in Triple-Negative Lower-Grade Gliomas. N Engl J Med. 2016;375(22):2206\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng C, Wang J, Li M, Wang H, Lou F, Cao S, et al. Comprehensive Molecular Characterization of Chinese Patients with Glioma by Extensive Next-Generation Sequencing Panel Analysis. Cancer Manag Res. 2021;13:3573\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang H, Song SW, Ha J, Won YJ, Park CK, Yoo H, et al. A Nationwide, Population-Based Epidemiology Study of Primary Central Nervous System Tumors in Korea, 2007\u0026ndash;2016: A Comparison with United States Data. Cancer Res Treat. 2021;53(2):355\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarita Y, Shibui S, Committee of Brain Tumor Registry of Japan Supported by the Japan Neurosurgical S. Trends and outcomes in the treatment of gliomas based on data during 2001\u0026ndash;2004 from the Brain Tumor Registry of Japan. Neurol Med Chir (Tokyo). 2015;55(4):286\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaw I, Albert NL, Arbizu J, Boellaard R, Drzezga A, Galldiks N, et al. Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [(18)F]FDG: version 1.0. Eur J Nucl Med Mol Imaging. 2019;46(3):540\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWen PY, van den Bent M, Vogelbaum MA, Chang SM. RANO 2.0: The revised Response Assessment in Neuro-Oncology (RANO) criteria for high- and low-grade glial tumors in adults designed for the future. Neuro Oncol. 2024;26(1):2\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunet V, Rossier C, Buck A, Stupp R, Prior JO. Performance of 18F-fluoro-ethyl-tyrosine (18F-FET) PET for the differential diagnosis of primary brain tumor: a systematic review and Metaanalysis. J Nucl Med. 2012;53(2):207\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeung D, Han X, Mikkelsen T, Nabors LB. Role of MRI in primary brain tumor evaluation. J Natl Compr Canc Netw. 2014;12(11):1561\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiyazi M, Geisler J, Siefert A, Schwarz SB, Ganswindt U, Garny S, et al. FET-PET for malignant glioma treatment planning. Radiother Oncol. 2011;99(1):44\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePuranik AD, Boon M, Purandare N, Rangarajan V, Gupta T, Moiyadi A, et al. Utility of FET-PET in detecting high-grade gliomas presenting with equivocal MR imaging features. World J Nucl Med. 2019;18(3):266\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKebir S, Fimmers R, Galldiks N, Schafer N, Mack F, Schaub C, et al. Late Pseudoprogression in Glioblastoma: Diagnostic Value of Dynamic O-(2-[18F]fluoroethyl)-L-Tyrosine PET. Clin Cancer Res. 2016;22(9):2190\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMihovilovic MI, Kertels O, Hanscheid H, Lohr M, Monoranu CM, Kleinlein I, et al. O-(2-((18)F)fluoroethyl)-L-tyrosine PET for the differentiation of tumour recurrence from late pseudoprogression in glioblastoma. J Neurol Neurosurg Psychiatry. 2019;90(2):238\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalldiks N, Dunkl V, Stoffels G, Hutterer M, Rapp M, Sabel M, et al. Diagnosis of pseudoprogression in patients with glioblastoma using O-(2-[18F]fluoroethyl)-L-tyrosine PET. Eur J Nucl Med Mol Imaging. 2015;42(5):685\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalldiks 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 Oncol. 2015;17(9):1293\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLau EW, Drummond KJ, Ware RE, Drummond E, Hogg A, Ryan G, et al. Comparative PET study using F-18 FET and F-18 FDG for the evaluation of patients with suspected brain tumour. J Clin Neurosci. 2010;17(1):43\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePlotkin M, Blechschmidt C, Auf G, Nyuyki F, Geworski L, Denecke T, et al. Comparison of F-18 FET-PET with F-18 FDG-PET for biopsy planning of non-contrast-enhancing gliomas. Eur Radiol. 2010;20(10):2496\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDadgar H, Jokar N, Nemati R, Larvie M, Assadi M. PET tracers in glioblastoma: Toward neurotheranostics as an individualized medicine approach. Front Nucl Med. 2023;3:1103262.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastello A, Albano D, Muoio B, Castellani M, Panareo S, Rizzo A et al. Diagnostic Accuracy of PET with (18)F-Fluciclovine ([(18)F]FACBC) in Detecting High-Grade Gliomas: A Systematic Review and Meta-Analysis. Diagnostics (Basel). 2023;13(24).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWakabayashi T, Hirose Y, Miyake K, Arakawa Y, Kagawa N, Nariai T, et al. Determining the extent of tumor resection at surgical planning with (18)F-fluciclovine PET/CT in patients with suspected glioma: multicenter phase III trials. Ann Nucl Med. 2021;35(12):1279\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalldiks N, Lohmann P, Fink GR, Langen KJ. Amino Acid PET in Neurooncology. J Nucl Med. 2023;64(5):693\u0026ndash;700.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao J, Jin Y, Nie J, Chen F, Ma X. Diagnostic and grading accuracy of (18)F-FDOPA PET and PET/CT in patients with gliomas: a systematic review and meta-analysis. BMC Cancer. 2019;19(1):767.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"annals-of-nuclear-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anme","sideBox":"Learn more about [Annals of Nuclear Medicine](http://link.springer.com/journal/12149)","snPcode":"12149","submissionUrl":"https://www.editorialmanager.com/anme/default2.aspx","title":"Annals of Nuclear Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"brain neoplasms, glioma, O-(2-[18F]-fluoroethyl)-L-tyrosine (FET), positron emission tomography","lastPublishedDoi":"10.21203/rs.3.rs-5062302/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5062302/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis prospective study aimed to evaluate the diagnostic value of fluorine-18-labeled fluoroethyltyrosine (\u003csup\u003e18\u003c/sup\u003eF-FET) positron emission tomography (PET)/computed tomography (CT) in diagnosing brain tumors within an Asian patient population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003ePatients suspected of having primary or recurrent brain tumors were prospectively recruited. Each patient underwent \u003csup\u003e18\u003c/sup\u003eF-FET and fluorine-18 fluorodeoxyglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG) PET/CT on separate days within one week. We calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy to compare the diagnostic performance of the two PET scans. The standardized uptake value (SUV) and tumor-to-background ratio (TBR) of the lesions were determined using static images. Additionally, time-activity curves (TACs) and time-to-peak (TTP) were generated from the dynamic PET images.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrom September 2019 to December 2023, 33 subjects were enrolled for reasons including suspected brain tumors (n\u0026thinsp;=\u0026thinsp;20) or suspicious glioma recurrence (n\u0026thinsp;=\u0026thinsp;8) on magnetic resonance imaging (MRI) and restaging for glioma (n\u0026thinsp;=\u0026thinsp;5). Among the patients with suspected brain tumors or glioma recurrence on MRI, 25% had false-positive results. \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT accurately identified 86% of these false positives. The sensitivity, specificity, PPV, NPV, and accuracy of visual interpretation of \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT were 96.15%, 85.71%, 96.15%, 85.71%, and 93.90%, respectively. The corresponding \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT values were 73.08%, 71.42%, 90.48%, 41.67%, and 72.70%. \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT demonstrated significantly higher sensitivity and accuracy than \u003csup\u003e18\u003c/sup\u003eF-FDG PET (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030, respectively). Using TBRmean as an adjunct reference index enhanced the diagnostic accuracy of \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT, achieving a sensitivity and NPV of 100%. Wash-out TAC or TTP\u0026thinsp;\u0026lt;\u0026thinsp;20 min was associated with a PPV of 100% for brain tumors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT appears to be a valuable tool for assessing brain tumors with indeterminate MRI findings in this Asian cohort. \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT offers benefits over \u003csup\u003e18\u003c/sup\u003eF-FDG PET in differentiating brain tumors from nontumor brain lesions, particularly when using semiquantitative analysis with TBR.\u003c/p\u003e","manuscriptTitle":"18F-FET PET/CT Can Aid in Diagnosing Patients with Indeterminate MRI Findings for Brain Tumors: A Prospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 06:32:39","doi":"10.21203/rs.3.rs-5062302/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-11-11T13:11:49+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-11T13:09:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-11T10:18:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Annals of Nuclear Medicine","date":"2024-11-11T03:58:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"annals-of-nuclear-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anme","sideBox":"Learn more about [Annals of Nuclear Medicine](http://link.springer.com/journal/12149)","snPcode":"12149","submissionUrl":"https://www.editorialmanager.com/anme/default2.aspx","title":"Annals of Nuclear Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a0675499-fea2-4a7c-9f5f-ca314a008004","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:22:24+00:00","versionOfRecord":{"articleIdentity":"rs-5062302","link":"https://doi.org/10.1007/s12149-024-02005-4","journal":{"identity":"annals-of-nuclear-medicine","isVorOnly":false,"title":"Annals of Nuclear Medicine"},"publishedOn":"2024-11-26 15:57:21","publishedOnDateReadable":"November 26th, 2024"},"versionCreatedAt":"2024-11-13 06:32:39","video":"","vorDoi":"10.1007/s12149-024-02005-4","vorDoiUrl":"https://doi.org/10.1007/s12149-024-02005-4","workflowStages":[]},"version":"v1","identity":"rs-5062302","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5062302","identity":"rs-5062302","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}