Diagnostic performance of [18F]FET PET in newly diagnosed cerebral lesions

Diagnostic performance of [18F]FET PET in newly diagnosed cerebral lesions | 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 Article Diagnostic performance of [ 18 F]FET PET in newly diagnosed cerebral lesions Seo Young Kang, Soo Jeong Park, Byung Seok Moon, Bom Sahn Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7042589/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract O-(2-[¹⁸F]Fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET is a valuable tool for the initial assessment of newly developed cerebral lesions, offering diagnostic and grading information for brain neoplasms. We aim to investigate clues for initial differential diagnosis in patients with newly developed cerebral lesions. We retrospectively analyzed 57 patients who underwent [¹⁸F]FET PET to evaluate newly diagnosed brain lesions. Tumor-to-brain ratios (TBRmax and TBRmean) of [¹⁸F]FET uptake were assessed to differentiate brain neoplastic lesions (BNLs) from non-neoplastic lesions (NNLs), and between tumor subgroups. [¹⁸F]FET uptake was significantly higher in BNLs compared to NNLs (TBRmax: 3.82 ± 1.41 vs 2.36 ± 0.60, P < 0.001; TBRmean: 2.22 ± 0.41 vs 1.70 ± 0.35, P < 0.001). ROC analysis identified a TBRmax cutoff of 2.77 to distinguish BNLs from NNLs (sensitivity: 86.7%, specificity: 78.6%, AUC: 0.837). Further analysis showed that high-grade tumors (e.g., high-grade gliomas and lymphoma) had higher TBRmax values than low-grade gliomas or NNLs (4.18 ± 1.31 vs 2.52 ± 1.09, P < 0.001), with a cutoff of 2.82 (sensitivity: 85.7%, specificity: 88.9%, AUC: 0.887, 95% CI: 0.779–0.996). [¹⁸F]FET PET uptake provides important diagnostic information for differentiating brain neoplasms from non-neoplastic lesions and helps stratify tumor grades at initial presentation. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Neurology Health sciences/Oncology [18F]FET PET brain tumor diagnosis high-grade glioma non-neoplastic lesions Figures Figure 1 Figure 2 Introduction Traditionally, in clinical oncology field, 2-[ 18 F]-2-deoxy-D-glucose ([ 18 F]FDG) is the most widely used tracer for evaluating tumor lesion 1 , 2 . However, compared to other organ systems, 18 F-FDG is not a proper radiotracer due to high background glucose metabolism of normal gray matter structures in brain because glucose is a major energy source for brain tissue. In recent years, PET using radiolabelled amino acids has gained considerable interest as an additional tool besides MRI to improve the diagnosis of cerebral neoplasms, especially gliomas and brain metastases 3 , 4 . There are several kinds of amino acid-based radiotracers for PET/CT for evaluating brain tumors 5 . The most commonly employed technique using radiolabeled amino acids was positron emission tomography (PET) using L-methyl-[ 11 C]-methionine ([ 11 C]MET) 6 . A number of clinical studies have demonstrated the clinical value of [ 11 C]MET PET for evaluating initial diagnosis and staging, biopsy planning, tumor recurrence versus radiation necrosis, and prognosis prediction for brain neoplasm 7 , 8 . However, O-(2-[ 18 F]Fluoroethyl)-L-tyrosine ([ 18 F]FET) which has overcome weakness of [ 11 C]MET, a short half-life, has been developed and started to be used as a substitute for MET in many neuro-oncological centers. [¹⁸F]FET is a radiolabeled amino acid tracer taken up by tumor cells through the L-type amino acid transporter (LAT), allowing its accumulation without blood–brain barrier disruption and providing insight into the physiological state of brain neoplasms. [ 18 F]FET PET/CT has become an appropriate imaging modality for evaluating brain neoplasms 9 , 10 . Grosu et al. have demonstrated comparable diagnostic information of [ 18 F]FET and [ 11 C]MET on gliomas and brain metastases 11 . The researchers have reported that [ 18 F]FET PET can be used for differentiation of residual or recurrent tumor from treatment-related changes/pseudoprogression, as well as for delineation of gliomas. Many clinical studies have demonstrated that [ 18 F]FET PET/CT provides important diagnostic information regarding the delineation of cerebral gliomas for therapy planning, an improved differentiation of tumor recurrence from treatment-related changes and sensitive treatment monitoring. Further, several studies demonstrated the diagnostic utility of [ 18 F]FET PET in differentiating brain neoplasms from non-neoplastic lesions (NNLs). Dunet et al . demonstrated much better performance of [ 18 F]FET PET compared to [ 18 F]FDG PET for assessing a new isolated brain tumor in systemic review and meta-analysis 12 . According to the study, [ 18 F]FET PET demonstrated significantly higher diagnostic performance for the diagnosis of brain tumor and glioma compared with [ 18 F]FDG PET. The mean and maximum target-to-background ratio (TBR max /TBR mean ) values on [ 18 F]FET PET showed a good performance in distinguishing tumorous lesion from non-tumorous condition in the brain. Several studies have proposed a TBR max threshold value for the discrimination of high-grade tumors from low-grade tumors or NNLs 13 . Rapp et al . demonstrated that TBR max of [ 18 F]FET uptake beyond the threshold of 2.5 has a high positive predictive value for detection of high-grade tumors and supports the necessity of an invasive procedure such as surgical biopsy or resection. Although many researchers have proposed clinically relevant metabolic thresholds for distinguishing high-grade tumors, a major limitation remains: the inability to reliably differentiate low-grade tumors from inflammatory or benign lesions. As a result, the use of [ 18 F]FET PET/CT in the clinical setting for the differential diagnosis of brain neoplasm remains limited. We aimed to investigate [¹⁸F]FET PET-derived metabolic parameters to enhance the differentiation of brain neoplasms, including tumor subtypes, from non-neoplastic lesions, thereby improving the accuracy of early diagnosis and informing clinical management. Results Subjects A total of 57 patients were finally enrolled in this study. Among the subjects, the average age was 57.96 ± 14.99 years, and 59.6% of the subjects were female. Table 1 presents a comprehensive overview of the demographic and clinical characteristics, including the distribution of lesion types and genetic profiles among the 57 patients. The cohort includes a diverse range of lesion types, such as high-grade gliomas (HGG), low-grade gliomas (LGG), CNS lymphomas, metastases, meningiomas, brain abscesses, encephalomyelitis, hematomas, and infarctions. The distribution of the metabolic parameters of [ 18 F]FET uptake in brain neoplastic lesions (BNLs) and NNLs was shown in Supplemental Table 1. Table 1 Demographic data and clinical characteristics Variables Value, n (%) No. of Patients 57 Age at diagnosis (years) 58.0 (range, 11–84) Sex, n (%) Male 23 Female 34 Diagnosis Brain Neoblastic Lesions (BNL) 42 (73.7) High grade glioma 23 (54.8) Low grade glioma 6 (14.3) CNS lymphoma 4 (9.5) Metastasis 7 (16.7) Meningioma 2 (4.8) Non-neoplastic lesions (NNL) 15 (25.9) Brain abscess 6 (40.0) Encephalomyelitis 6 (40.0) Hematoma 2 (13.3) Infarction 1 (6.7) Gene mutation status in the glioma subgroups (n = 25) IDH mutation 2 (8.0) MGMT promoter methylation 9 (36.0) TERT promoter 14 (56.0) ATRX mutation 19 (76.0) GFAP positive 12 (48.0) Chromosome 1p and 19q codeletion 0 Mean Follow-up time (d) 444.4 Mortality rate (%) 26.30 [ 18 F]FET uptake in differentiating BNLs and NNLs The [ 18 F]FET uptake in BNLs was significantly higher than in NNLs, with mean TBR max , 3.82 ± 1.41 vs 2.36 ± 0.60 (P < 0.001) and TBR mean values of 2.22 ± 0.41 vs 1.70 ± 0.35 (P < 0.001), respectively (Table 2 ). Receiver Operating Characteristic (ROC) curve analysis identified optimal cutoff values of 2.77 for TBR max and 2.08 of TBR mean to differentiate BNLs from NNLs. A TBR max threshold demonstrated substantial diagnostic efficacy, yielding a sensitivity of 86.7%, specificity of 78.6%, and an area under the curve (AUC) of 0.837 (95% confidence interval [CI], 0.733–0.94). Multivariate ROC analysis incorporating multiple PET-derived parameters yielded a modest improvement in diagnostic performance, with the AUC increasing to 0.844 (Fig. 1 ). Table 2 Metabolic parameters for the differential diagnosis between brain neoplasms and non-neoplastic lesions Total (n = 57) BNL (n = 42) NNL (n = 15) P value Age 58.0 59.4 53.9 0.228 Age (range) 20–84 20–84 38–79 Females (%) 34 (59.6%) 26 (61.9%) 8 (53.3%) 0.784 Metabolic parameters TBRmax 3.44 ± 1.40 3.82 ± 1.41 2.36 ± 0.60 < 0.001 *** TBRmean 2.08 ± 0.46 2.22 ± 0.41 1.70 ± 0.35 < 0.001 *** SUVmax 3.85 ± 1.72 4.21 ± 1.82 2.86 ± 0.80 < 0.01 ** SUVmean 2.35 ± 0.66 2.44 ± 0.69 2.09 ± 0.47 0.077 [ 18 F]FET uptake in differentiating subgroups within BNLs The [ 18 F]FET uptake in high-grade tumors (HGTs), including HGG and lymphoma, was significantly higher than in LGG/NNLs group. Specifically, the mean TBR max , 4.18 ± 1.31 in HGTs versus 2.52 ± 1.09 on LGG/NNLs (P < 0.001), and the mean TBR mean was 2.27 ± 0.37 compared to 1.78 ± 0.44 (P < 0.001), respectively. Additionally, both SUV max and SUV mean demonstrated statistically significant differences between two groups. However, comparing HGGs to LGGs, only TBR max showed statistically significant difference (Table 3 ). Table 3 Metabolic parameters for differential diagnosis between brain neoplasms subgroups [ 18 F]FET PET TBR [ 18 F]FET PET SUV TBRmax TBRmean SUVmax SUVmean HGG vs LGG HGG 4.21 ± 1.41 2.25 ± 0.39 4.70 ± 1.71 2.52 ± 0.60 LGG 2.91 ± 1.88 2.00 ± 0.59 3.35 ± 2.58 2.26 ± 0.94 P value 0.022 * 0.062 0.067 0.193 HGG vs metastasis HGG 4.21 ± 1.41 2.25 ± 0.39 4.70 ± 1.71 2.52 ± 0.60 metastasis 2.96 ± 0.75 2.04 ± 0.27 3.11 ± 1.44 2.14 ± 0.76 P value 0.029 * 0.208 0.012 * 0.06 LGG vs metastasis LGG 2.91 ± 1.88 2.00 ± 0.59 3.35 ± 2.58 2.26 ± 0.94 metastasis 2.96 ± 0.75 2.04 ± 0.27 3.11 ± 1.44 2.14 ± 0.76 P value 0.295 0.181 0.628 0.836 LGG vs NNL LGG 2.91 ± 1.88 2.00 ± 0.59 3.35 ± 2.58 2.26 ± 0.94 NNL 2.36 ± 0.60 1.70 ± 0.35 2.86 ± 0.80 2.09 ± 0.47 P value 0.970 0.677 0.533 0.907 For the differentiation of HGTs, from LGG/NNLs, a TBR max cutoff value of 2.82 exhibited excellent diagnostic accuracy, yielding a sensitivity of 85.7%, specificity of 88.9%, and an AUC of 0.887 (95% CI: 0.779–0.996). When differentiating HGGs from LGGs specifically, the same cutoff of 2.82 yielded a sensitivity of 83.3%, specificity of 87.0%, and an AUC of 0.804 (95% CI: 0.514–1.000). Multivariate ROC analysis for this comparison showed further improvement in diagnostic performance, achieving an AUC of 0.913 and a sensitivity of 100%. The corresponding ROC curves are illustrated in Fig. 2 . [¹⁸F]FET Uptake for discrimination between LGGs and NNLs Analysis comparing LGG and NNL revealed no significant differences in metabolic parameters. Specifically, TBR max values were 2.91 ± 1.88 for LGG and 2.36 ± 0.60 for NNLs (P = 0.970), while TBR mean values were 2.00 ± 0.59 and 1.70 ± 0.35, respectively (P = 0.677) (Table 3 ). ROC curve analysis of TBR max demonstrated limited diagnostic utility, with a sensitivity of 86.7%, specificity of 33.3%, and an AUC of 0.511. However, multivariate ROC analysis incorporating TBR max, TBR mean, SUV max, and SUV mean yielded a notable improvement in diagnostic performance, achieving an AUC of 0.878 and a sensitivity of 93.3%. The corresponding ROC curves are presented in Supplemental Fig. 1. Discussion In this study, we investigated the clinical impact of [ 18 F]FET PET/CT for differential diagnosis of newly developed brain neoplasm. The identified optimal cutoff values for TBR max and TBR mean provide a robust diagnostic framework, facilitating accurate differentiation between neoplastic and non-neoplastic lesions, as well as high-grade and low-grade tumors. The sensitivity and specificity associated with these thresholds underscore the clinical utility of [¹⁸F]FET PET in improving diagnostic accuracy and supporting more informed clinical decision-making. Our findings align with the studies by Dunet et al . (2012) and Rapp et al . (2013), which emphasize excellent performance for diagnosis of [ 18 F]FET PET/CT in brain tumors 13 , 14 . Dunet et al . suggested that a mean TBR threshold of at least 1.6 and a maximum TBR of at least 2.1 had the best diagnostic value for differentiating primary brain tumors from non-tumoral lesions. In addition, Rapp et al. reported that TBR max and TBR mean of [ 18 F]FET uptake beyond the threshold of 2.5 and 1.9 has a high positive predictive value for detection of neoplastic lesions. In comparison to previously published data, the TBR max threshold identified in this study (2.77) was relatively higher, and the TBR mean threshold (2.08) was comparable to, or slighlty elevated from, values reported in the literature. This variation may be attributed to the higher proportion of high-grade tumors within our cohort, as well as differences in tumor segmentation methodologies. Additionally, variability in imaging hardware and data processing protocols across institutions may contribute to the observed discrepancies. 15 . [ 18 F]FET uptake is primarily mediated by L-type amino acid transporter (LAT) subtypes LAT1 and LAT2, which are heterodimers consisting of a light (LAT1, LAT2) and a heavy chain (CD98) and serve as an essential stereospecific exchanger of large neutral essential amino acids 16 , 17 . LAT1, encoded by the SLC7A5 gene, is commonly overexpressed in malignant cells 18 , and high LAT1 expression is closely related to the proliferation of tumor cells and angiogenesis in various types of cancers including primary brain tumors 19 – 22 . Therefore, [ 18 F]FET PET uptake in brain has a high specificity for gliomas mediated by a tracer uptake almost independent of blood–brain barrier (BBB) dysfunction. Although [ 18 F]FET uptake by non-tumor tissue is generally considered rare, several studies have reported uptake in various inflammatory conditions (e.g., acute disseminated encephalomyelitis plaques, bacterial meningoencephalitis, progressive multifocal leukoencephalopathy) as well as in vascular brain lesions (e.g., cavernoma and cortical dysplasia) 23 . Strong LAT1/LAT2/CD98 expression of resident and immigrated inflammatory activated immune cells in reactive astrocytosis is a major factor driving non-tumor [ 18 F]FET uptake, alongside passive tracer influx through a disrupted blood–brain barrier 24 , 25 . In differentiating LGGs from NNLs, no significant differences were observed in the metabolic parameters of [ 18 F]FET PET. This finding aligns with the majority of existing literature assessing the diagnostic utility of [ 18 F]FET PET/CT. Given the low proliferative activity and limited angiogenesis of low-grade tumors, high tracer accumulation is not typically expected—consistent with the underlying mechanism of [ 18 F]FET uptake—which complicates differentiation from non-tumor lesions. Conversely, certain inflammatory conditions, such as brain abscesses, may exhibit increased [ 18 F]FET uptake due to reactive astrocytosis. Nevertheless, multivariate ROC analysis combining TBR max, TBR mean, SUV max, and SUV mean yielded notable diagnostic performance, achieving an AUC of 0.878 with a sensitivity of 93.3%. Thus, this finding highlights the need for further studies with larger patient cohorts. At the same time, it underscores the distinct value of our study in contrast to previously published reports. While the clinical utility of [¹⁸F]FET PET/CT in differential diagnosis has been extensively investigated, the majority of these studies have predominantly involved cohorts from European populations, limiting the generalizability of the findings. The introduction of the FET tracer to Asia occurred relatively late, and only a limited number of studies have examined its use in Asian populations. Moreover, there is a lack of well-designed studies specifically addressing various non-neoplastic cases. In this context, our study provides valuable complementary data by evaluating a non-European cohort, thereby contributing to the broader generalizability of [¹⁸F]FET PET/CT findings in clinical practice. The current study has several limitations. First, it was designed retrospectively, which is known to introduce inherent biases that may affect the generalizability of the findings. Second, the sample size was insufficient to establish a reliable reference threshold for differential diagnosis. Additionally, the prognostic value of [ 18 F]FET PET/CT was not explored, leaving this as an open avenue for future research. Therefore, further investigations with larger patient cohorts and a prospective study design are warranted to obtain more accurate and generalizable results. In conclusion, our study contributes to the growing understanding of the role of [ 18 F]FET PET/CT in the evaluation of newly diagnosed cerebral lesions. Despite certain limitations, the proposed cutoff values—supported by pathophysiological rationale and comparative analysis—may aid in achieving more reliable clinical interpretations. Therefore, we cautiously suggest that [18F]FET PET/CT could be considered a useful tool in the differential diagnosis of newly diagnosed cerebral lesions. Methods Subjects In this retrospective study, we identified 103 patients who were referred to the Department of Neurosurgery at our hospital between October 2020 and March 2025 for evaluation of intracerebral masses or lesions, all of whom underwent [¹⁸F]FET PET imaging. Patient enrollment was conducted based on stringent criteria to ensure a representative study cohort. Inclusion criteria encompassed individuals with newly diagnosed cerebral lesions who underwent [ 18 F]FET PET/CT between October 2020 and March 2025 prior to any therapeutic interventions that could influence [ 18 F]FET uptake—such as surgery, biopsy, chemotherapy, radiotherapy, or radiosurgery. A neuropathologic diagnosis, obtained through stereotactic biopsy or open resection after [ 18 F]FET PET/CT, was available for 39 patients: 22 high-grade gliomas (HGG), 3 low-grade gliomas (LGG), 4 CNS lymphomas, 3 metastasis, 2 meningioma, 4 brain abscess, and 1 encephalomyelitis. Furthermore, we included 18 additional patients without histologic evaluation, for whom both the clinical course (treatment response) and definite MR imaging findings clearly confirmed the lesion types (3 LGG, 4 metastases, 1 glioblastoma, and 10 NNL). Exclusion criteria were applied to eliminate potential confounders. Patients with incomplete or inadequate [ 18 F]FET PET data were excluded to ensure the reliability and completeness of the dataset, Specifically, cases in which PET-positive volumes could not be delineated—thereby preventing the evaluation of TBRmax and TBRmean—were excluded from the analysis. Additionally, individuals who did not meet the criteria for either confirmed pathology or clinical diagnosis were excluded from the analysis. This study was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the institutional review board of Ewha Womans University Seoul Hospital waived the need of obtaining informed consent (IRB no. 2025-05-060). [ 18 F]FET PET/CT image acquisition All [ 18 F]FET PET scans were acquired using a dedicated PET/CT scanner (Discovery MI with LightBurst Digital 4-Ring Detector, GE Medical Systems, Milwaukee, WI, USA), which provides images with a three-dimensional resolution of 2.3 mm full width at half maximum at the center of the field of view. All subjects were instructed to fast for at least 4 hours prior to scan. Before the emission scan, CT scan was performed in spiral mode at 120 kVp and 250 mA. Static emission scans of 20 min were acquired 20 min after the intravenous injection of 185–200 MBq of [¹⁸F]FET. PET images were attenuation-corrected and reconstructed on a 512 × 512 matrix using the Q.Clear reconstruction algorithm with a ß value of 350. [ 18 F]FET/CT images analysis Semiquantitative analysis measuring metabolic parameters of the target lesions on [ 18 F]FET PET was conducted using MIM Encore software (MIM Software Inc. Cleveland, OH, USA). Background activity on [¹⁸F]FET PET, used for calculating the target-to-background ratio (TBR), was assessed in contralateral, healthy-appearing cerebral tissue encompassing both gray and white matter. In accordance with recommendations from the EANM, SNMMI, EANO, and RANO, this measurement was performed using a crescent-shaped volume of interest placed in the frontal lobe 26,27 . A spherical volume encompassing the tumor lesion was manually drawn, and volumes of interest (VOIs) were automatically defined using a standardized uptake value (SUV) equal to or greater than 1.6 times the mean background activity 26 . To avoid inclusion of physiologically high-uptake structures—such as vasculature, muscles, choroid plexus—within the volume of interest, a visual plausibility check was conducted and manual corrections were applied as necessary. The TBR max and TBR mean were quantified to characterize the [ 18 F]FET uptake within the identified VOIs by dividing the maximum and mean uptake value by the mean SUV of the healthy background. Statistical analysis Descriptive statistics included the mean ± standard deviation or frequency for each clinical characteristic. Quantitative variables were analyzed using Student’s t -test for normally distributed data, while the Mann–Whitney U test was employed for data that did not meet the assumption of normality, as assessed by the Shapiro–Wilk test. ROC curve analysis based on TBR max and TBR mean as well as SUV max and SUV mean was performed to evaluate the diagnostic performance of [ 18 F]FET uptake metrics in differentiating between BNs and NNLs, as well as between high-grade tumors (HGG or lymphoma) and LGGs/NNLs, and between LGGs and NNLs. Optimal cutoff values for each parameter were determined based on ROC curve analysis using the Youden index to achieve the best balance between sensitivity and specificity. Analyses were conducted with R software (version 4.3.1, www.Rproject.org), and “multipleROC” package was used to plot the ROC curves. All statistical tests were two-sided, with a significance level of 0.05. Declarations Funding: This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00449684 and RS-2024-00339811) and the Ministry of Health and Welfare (RS-2024-00439928). Conflicts of interest statement: The authors have no conflicts of interest to declare. Author Contribution S.Y.K., S.J.P., and B.S.K. conceptualized the study.S.Y.K. curated the data and performed the formal analysis.B.S.M. and B.S.K. acquired funding.S.Y.K. and S.J.P. conducted the investigation and developed the methodology.S.J.P. and B.S.M. provided resources.B.S.K. supervised the project.S.J.P., B.S.M., and B.S.K. validated the results.S.Y.K. was responsible for data visualization.S.Y.K. and S.J.P. wrote the original draft.All authors reviewed and approved the final manuscript. Acknowledgement This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00449684 and RS-2024-00339811) and the Ministry of Health and Welfare (RS-2024-00439928). Data Availability The datasets generated or analyzed during the study are available from the corresponding author on reasonable request. References Fletcher, J. W. et al. 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[18F]-fluoro-ethyl-L-tyrosine PET: a valuable diagnostic tool in neuro-oncology, but not all that glitters is glioma. Neuro Oncol. 15 , 341–351. https://doi.org/10.1093/neuonc/nos300 (2013). Hutterer, M. et al. Epileptic Activity Increases Cerebral Amino Acid Transport Assessed by 18F-Fluoroethyl-l-Tyrosine Amino Acid PET: A Potential Brain Tumor Mimic. J. Nucl. Med. 58 , 129–137. https://doi.org/10.2967/jnumed.116.176610 (2017). Hutterer, M. et al. AIDS-Related Central Nervous System Toxoplasmosis With Increased 18F-Fluoroethyl-L-Tyrosine Amino Acid PET Uptake Due to LAT1/2 Expression of Inflammatory Cells. Clin. Nucl. Med. 42 , e506–e508. https://doi.org/10.1097/rlu.0000000000001873 (2017). Law, I. 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 . 46 , 540–557. https://doi.org/10.1007/s00259-018-4207-9 (2019). Albert, N. L. et al. PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): a report of the RANO group. Lancet Oncol. 25 , e29–e41. https://doi.org/10.1016/s1470-2045(23)00525-9 (2024). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted 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-7042589","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489529770,"identity":"62d1a7ec-18c3-46e1-b113-f07acc8ed4b2","order_by":0,"name":"Seo Young Kang","email":"","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Seo","middleName":"Young","lastName":"Kang","suffix":""},{"id":489529771,"identity":"803ab38f-79c4-460c-8829-2b6285ba5093","order_by":1,"name":"Soo Jeong Park","email":"","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Soo","middleName":"Jeong","lastName":"Park","suffix":""},{"id":489529774,"identity":"85a7fb01-fd2f-42c2-9059-307f60530301","order_by":2,"name":"Byung Seok Moon","email":"","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Byung","middleName":"Seok","lastName":"Moon","suffix":""},{"id":489529776,"identity":"8e6a5764-4090-4ef9-9ce7-7456799784de","order_by":3,"name":"Bom Sahn Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYBACCQkwdYCBn4GBDSLEQ6wWyQaStRgcIFaL5Ozmoxt/1NzJMz7eY/bgB4OdPAPP2Qd4tUjLHEu7zXPsWbHZmTPmhj0MyYYNvO0GeLXISeSY3WZgO5y47UbuNgkeBuYEBn42/A4Dabn549/hxM3z326T/MNQT1iLNFDLDd62w4kbJHi3SfMwHE5g4G3Dr0VyDtAvvH2HE2ecyf8mLWNw3LCN5xh+LRK3m4/d/PHtcGJ/+7E0yTcV1fL8PGn4taABA3gaGAWjYBSMglFACQAA8RhEqDgLnx4AAAAASUVORK5CYII=","orcid":"","institution":"Ewha Womans University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Bom","middleName":"Sahn","lastName":"Kim","suffix":""}],"badges":[],"createdAt":"2025-07-04 03:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7042589/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7042589/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87553464,"identity":"9d4bb202-67c4-4461-bbde-3b9cfe0a3e15","added_by":"auto","created_at":"2025-07-25 06:38:08","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137708,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves for diagnostic performance comparison of metabolic parameters in differential diagnosis between BNL and NNL\u003c/p\u003e\n\u003cp\u003eA, The ROC curve for TBR\u003csub\u003emax\u003c/sub\u003e; B, The multivariate ROC curves incorporating TBR\u003csub\u003emax\u003c/sub\u003e, TBR\u003csub\u003emean\u003c/sub\u003e, SUV\u003csub\u003emax\u003c/sub\u003e, and SUV\u003csub\u003emean.\u003c/sub\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7042589/v1/d643232101dc64133ac1c23e.jpeg"},{"id":87554642,"identity":"cc185a85-cee2-46da-8874-9112dcabf92e","added_by":"auto","created_at":"2025-07-25 06:46:08","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187841,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves for diagnostic performance comparison of metabolic parameters in differential diagnosis between brain neoplasms subgroups\u003c/p\u003e\n\u003cp\u003eA, The ROC curve for TBR\u003csub\u003emax \u003c/sub\u003ebetween HGT and LGG/NNL; B, The multivariate ROC curves incorporating TBR\u003csub\u003emax\u003c/sub\u003e, TBR\u003csub\u003emean\u003c/sub\u003e, SUV\u003csub\u003emax\u003c/sub\u003e, and SUV\u003csub\u003emean \u003c/sub\u003ebetween HGT and LGG/NNL; C. The ROC curve for TBR\u003csub\u003emax \u003c/sub\u003ebetween HGG and LGG; D, The multivariate ROC curves incorporating TBR\u003csub\u003emax\u003c/sub\u003e, TBR\u003csub\u003emean\u003c/sub\u003e, SUV\u003csub\u003emax\u003c/sub\u003e, and SUV\u003csub\u003emean \u003c/sub\u003ebetween HGG and LGG.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7042589/v1/0f50a4c7520480443f3153a2.jpeg"},{"id":91388611,"identity":"366c675c-d96a-4a45-aa65-345722ffa687","added_by":"auto","created_at":"2025-09-16 03:16:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1031572,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7042589/v1/c234c29a-1a77-4afc-967c-8e64d79eb04a.pdf"},{"id":87553463,"identity":"261b2b16-c46b-4723-a656-d00b7ef7b2ef","added_by":"auto","created_at":"2025-07-25 06:38:08","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":160801,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7042589/v1/ae227cd74fc04d7caa747b4c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDiagnostic performance of [\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FET PET in newly diagnosed cerebral lesions\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTraditionally, in clinical oncology field, 2-[\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]-2-deoxy-D-glucose ([\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FDG) is the most widely used tracer for evaluating tumor lesion \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, compared to other organ systems, \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF-FDG is not a proper radiotracer due to high background glucose metabolism of normal gray matter structures in brain because glucose is a major energy source for brain tissue. In recent years, PET using radiolabelled amino acids has gained considerable interest as an additional tool besides MRI to improve the diagnosis of cerebral neoplasms, especially gliomas and brain metastases \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThere are several kinds of amino acid-based radiotracers for PET/CT for evaluating brain tumors \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The most commonly employed technique using radiolabeled amino acids was positron emission tomography (PET) using L-methyl-[\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]-methionine ([\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]MET) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A number of clinical studies have demonstrated the clinical value of [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]MET PET for evaluating initial diagnosis and staging, biopsy planning, tumor recurrence versus radiation necrosis, and prognosis prediction for brain neoplasm \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHowever, O-(2-[\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]Fluoroethyl)-L-tyrosine ([\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET) which has overcome weakness of [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]MET, a short half-life, has been developed and started to be used as a substitute for MET in many neuro-oncological centers. [\u0026sup1;⁸F]FET is a radiolabeled amino acid tracer taken up by tumor cells through the L-type amino acid transporter (LAT), allowing its accumulation without blood\u0026ndash;brain barrier disruption and providing insight into the physiological state of brain neoplasms. [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT has become an appropriate imaging modality for evaluating brain neoplasms \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Grosu et al. have demonstrated comparable diagnostic information of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET and [\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003eC]MET on gliomas and brain metastases \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The researchers have reported that [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET can be used for differentiation of residual or recurrent tumor from treatment-related changes/pseudoprogression, as well as for delineation of gliomas.\u003c/p\u003e\u003cp\u003eMany clinical studies have demonstrated that [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT provides important diagnostic information regarding the delineation of cerebral gliomas for therapy planning, an improved differentiation of tumor recurrence from treatment-related changes and sensitive treatment monitoring. Further, several studies demonstrated the diagnostic utility of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET in differentiating brain neoplasms from non-neoplastic lesions (NNLs). Dunet \u003cem\u003eet al\u003c/em\u003e. demonstrated much better performance of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET compared to [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FDG PET for assessing a new isolated brain tumor in systemic review and meta-analysis \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. According to the study, [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET demonstrated significantly higher diagnostic performance for the diagnosis of brain tumor and glioma compared with [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FDG PET. The mean and maximum target-to-background ratio (TBR\u003csub\u003emax\u003c/sub\u003e/TBR\u003csub\u003emean\u003c/sub\u003e) values on [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET showed a good performance in distinguishing tumorous lesion from non-tumorous condition in the brain.\u003c/p\u003e\u003cp\u003eSeveral studies have proposed a TBR\u003csub\u003emax\u003c/sub\u003e threshold value for the discrimination of high-grade tumors from low-grade tumors or NNLs \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Rapp \u003cem\u003eet al\u003c/em\u003e. demonstrated that TBR\u003csub\u003emax\u003c/sub\u003e of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake beyond the threshold of 2.5 has a high positive predictive value for detection of high-grade tumors and supports the necessity of an invasive procedure such as surgical biopsy or resection. Although many researchers have proposed clinically relevant metabolic thresholds for distinguishing high-grade tumors, a major limitation remains: the inability to reliably differentiate low-grade tumors from inflammatory or benign lesions. As a result, the use of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT in the clinical setting for the differential diagnosis of brain neoplasm remains limited.\u003c/p\u003e\u003cp\u003eWe aimed to investigate [\u0026sup1;⁸F]FET PET-derived metabolic parameters to enhance the differentiation of brain neoplasms, including tumor subtypes, from non-neoplastic lesions, thereby improving the accuracy of early diagnosis and informing clinical management.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eSubjects\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 57 patients were finally enrolled in this study. Among the subjects, the average age was 57.96\u0026thinsp;\u0026plusmn;\u0026thinsp;14.99 years, and 59.6% of the subjects were female. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comprehensive overview of the demographic and clinical characteristics, including the distribution of lesion types and genetic profiles among the 57 patients. The cohort includes a diverse range of lesion types, such as high-grade gliomas (HGG), low-grade gliomas (LGG), CNS lymphomas, metastases, meningiomas, brain abscesses, encephalomyelitis, hematomas, and infarctions. The distribution of the metabolic parameters of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake in brain neoplastic lesions (BNLs) and NNLs was shown in Supplemental Table\u0026nbsp;1.\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\u003eDemographic data and clinical characteristics\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\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue, n (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo. of Patients\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge at diagnosis (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.0 (range, 11\u0026ndash;84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n (%)\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\u003e23\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\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnosis\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\u003eBrain Neoblastic Lesions (BNL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (73.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh grade glioma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (54.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow grade glioma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (14.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCNS lymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (9.5)\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\u003e7 (16.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeningioma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (4.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-neoplastic lesions (NNL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (25.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrain abscess\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (40.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEncephalomyelitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (40.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHematoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (13.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfarction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (6.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGene mutation status in the glioma subgroups (n\u0026thinsp;=\u0026thinsp;25)\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\u003eIDH mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMGMT promoter methylation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (36.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTERT promoter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (56.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eATRX mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (76.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGFAP positive\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (48.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChromosome 1p and 19q codeletion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Follow-up time (d)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e444.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMortality rate (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.30\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\u003cb\u003e[\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eF]FET uptake in differentiating BNLs and NNLs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake in BNLs was significantly higher than in NNLs, with mean TBR\u003csub\u003emax\u003c/sub\u003e, 3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41 vs 2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and TBR\u003csub\u003emean\u003c/sub\u003e values of 2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41 vs 1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Receiver Operating Characteristic (ROC) curve analysis identified optimal cutoff values of 2.77 for TBR\u003csub\u003emax\u003c/sub\u003e and 2.08 of TBR\u003csub\u003emean\u003c/sub\u003e to differentiate BNLs from NNLs. A TBR\u003csub\u003emax\u003c/sub\u003e threshold demonstrated substantial diagnostic efficacy, yielding a sensitivity of 86.7%, specificity of 78.6%, and an area under the curve (AUC) of 0.837 (95% confidence interval [CI], 0.733\u0026ndash;0.94). Multivariate ROC analysis incorporating multiple PET-derived parameters yielded a modest improvement in diagnostic performance, with the AUC increasing to 0.844 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eMetabolic parameters for the differential diagnosis between brain neoplasms and non-neoplastic lesions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eTotal (n\u0026thinsp;=\u0026thinsp;57)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBNL (n\u0026thinsp;=\u0026thinsp;42)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNNL (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (range)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026ndash;84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u0026ndash;84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38\u0026ndash;79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemales (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (59.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (61.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolic parameters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBRmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBRmean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSUVmean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.077\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\u003e\u003cb\u003e[\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eF]FET uptake in differentiating subgroups within BNLs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake in high-grade tumors (HGTs), including HGG and lymphoma, was significantly higher than in LGG/NNLs group. Specifically, the mean TBR\u003csub\u003emax\u003c/sub\u003e, 4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31 in HGTs versus 2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 on LGG/NNLs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the mean TBR\u003csub\u003emean\u003c/sub\u003e was 2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 compared to 1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Additionally, both SUV\u003csub\u003emax\u003c/sub\u003e and SUV\u003csub\u003emean\u003c/sub\u003e demonstrated statistically significant differences between two groups. However, comparing HGGs to LGGs, only TBR\u003csub\u003emax\u003c/sub\u003e showed statistically significant difference (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eMetabolic parameters for differential diagnosis between brain neoplasms subgroups\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\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e[\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET TBR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e[\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET SUV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003eTBRmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTBRmean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSUVmax\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSUVmean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHGG vs LGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eHGG vs metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emetastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.208\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLGG vs metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emetastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.836\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eLGG vs NNL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLGG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNNL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.70\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.09\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.907\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\u003eFor the differentiation of HGTs, from LGG/NNLs, a TBR\u003csub\u003emax\u003c/sub\u003e cutoff value of 2.82 exhibited excellent diagnostic accuracy, yielding a sensitivity of 85.7%, specificity of 88.9%, and an AUC of 0.887 (95% CI: 0.779\u0026ndash;0.996). When differentiating HGGs from LGGs specifically, the same cutoff of 2.82 yielded a sensitivity of 83.3%, specificity of 87.0%, and an AUC of 0.804 (95% CI: 0.514\u0026ndash;1.000). Multivariate ROC analysis for this comparison showed further improvement in diagnostic performance, achieving an AUC of 0.913 and a sensitivity of 100%. The corresponding ROC curves are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e[\u0026sup1;⁸F]FET Uptake for discrimination between LGGs and NNLs\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAnalysis comparing LGG and NNL revealed no significant differences in metabolic parameters. Specifically, TBR\u003csub\u003emax\u003c/sub\u003e values were 2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88 for LGG and 2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 for NNLs (P\u0026thinsp;=\u0026thinsp;0.970), while TBR\u003csub\u003emean\u003c/sub\u003e values were 2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59 and 1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35, respectively (P\u0026thinsp;=\u0026thinsp;0.677) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). ROC curve analysis of TBR\u003csub\u003emax\u003c/sub\u003e demonstrated limited diagnostic utility, with a sensitivity of 86.7%, specificity of 33.3%, and an AUC of 0.511. However, multivariate ROC analysis incorporating TBR\u003csub\u003emax,\u003c/sub\u003e TBR\u003csub\u003emean,\u003c/sub\u003e SUV\u003csub\u003emax, and\u003c/sub\u003e SUV\u003csub\u003emean\u003c/sub\u003e yielded a notable improvement in diagnostic performance, achieving an AUC of 0.878 and a sensitivity of 93.3%. The corresponding ROC curves are presented in Supplemental Fig.\u0026nbsp;1.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the clinical impact of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT for differential diagnosis of newly developed brain neoplasm. The identified optimal cutoff values for TBR\u003csub\u003emax\u003c/sub\u003e and TBR\u003csub\u003emean\u003c/sub\u003e provide a robust diagnostic framework, facilitating accurate differentiation between neoplastic and non-neoplastic lesions, as well as high-grade and low-grade tumors. The sensitivity and specificity associated with these thresholds underscore the clinical utility of [¹⁸F]FET PET in improving diagnostic accuracy and supporting more informed clinical decision-making.\u003c/p\u003e\u003cp\u003eOur findings align with the studies by Dunet \u003cem\u003eet al\u003c/em\u003e. (2012) and Rapp \u003cem\u003eet al\u003c/em\u003e. (2013), which emphasize excellent performance for diagnosis of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT in brain tumors \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Dunet \u003cem\u003eet al\u003c/em\u003e. suggested that a mean TBR threshold of at least 1.6 and a maximum TBR of at least 2.1 had the best diagnostic value for differentiating primary brain tumors from non-tumoral lesions. In addition, Rapp \u003cem\u003eet al.\u003c/em\u003e reported that TBR\u003csub\u003emax\u003c/sub\u003e and TBR\u003csub\u003emean\u003c/sub\u003e of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake beyond the threshold of 2.5 and 1.9 has a high positive predictive value for detection of neoplastic lesions. In comparison to previously published data, the TBR\u003csub\u003emax\u003c/sub\u003e threshold identified in this study (2.77) was relatively higher, and the TBR\u003csub\u003emean\u003c/sub\u003e threshold (2.08) was comparable to, or slighlty elevated from, values reported in the literature. This variation may be attributed to the higher proportion of high-grade tumors within our cohort, as well as differences in tumor segmentation methodologies. Additionally, variability in imaging hardware and data processing protocols across institutions may contribute to the observed discrepancies. \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e[\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake is primarily mediated by L-type amino acid transporter (LAT) subtypes LAT1 and LAT2, which are heterodimers consisting of a light (LAT1, LAT2) and a heavy chain (CD98) and serve as an essential stereospecific exchanger of large neutral essential amino acids \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. LAT1, encoded by the SLC7A5 gene, is commonly overexpressed in malignant cells \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and high LAT1 expression is closely related to the proliferation of tumor cells and angiogenesis in various types of cancers including primary brain tumors \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Therefore, [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET uptake in brain has a high specificity for gliomas mediated by a tracer uptake almost independent of blood–brain barrier (BBB) dysfunction. Although [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake by non-tumor tissue is generally considered rare, several studies have reported uptake in various inflammatory conditions (e.g., acute disseminated encephalomyelitis plaques, bacterial meningoencephalitis, progressive multifocal leukoencephalopathy) as well as in vascular brain lesions (e.g., cavernoma and cortical dysplasia) \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Strong LAT1/LAT2/CD98 expression of resident and immigrated inflammatory activated immune cells in reactive astrocytosis is a major factor driving non-tumor [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake, alongside passive tracer influx through a disrupted blood–brain barrier \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn differentiating LGGs from NNLs, no significant differences were observed in the metabolic parameters of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET. This finding aligns with the majority of existing literature assessing the diagnostic utility of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT. Given the low proliferative activity and limited angiogenesis of low-grade tumors, high tracer accumulation is not typically expected—consistent with the underlying mechanism of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake—which complicates differentiation from non-tumor lesions. Conversely, certain inflammatory conditions, such as brain abscesses, may exhibit increased [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET uptake due to reactive astrocytosis. Nevertheless, multivariate ROC analysis combining TBR\u003csub\u003emax,\u003c/sub\u003e TBR\u003csub\u003emean,\u003c/sub\u003e SUV\u003csub\u003emax, and\u003c/sub\u003e SUV\u003csub\u003emean\u003c/sub\u003e yielded notable diagnostic performance, achieving an AUC of 0.878 with a sensitivity of 93.3%. Thus, this finding highlights the need for further studies with larger patient cohorts. At the same time, it underscores the distinct value of our study in contrast to previously published reports.\u003c/p\u003e\u003cp\u003eWhile the clinical utility of [¹⁸F]FET PET/CT in differential diagnosis has been extensively investigated, the majority of these studies have predominantly involved cohorts from European populations, limiting the generalizability of the findings. The introduction of the FET tracer to Asia occurred relatively late, and only a limited number of studies have examined its use in Asian populations. Moreover, there is a lack of well-designed studies specifically addressing various non-neoplastic cases. In this context, our study provides valuable complementary data by evaluating a non-European cohort, thereby contributing to the broader generalizability of [¹⁸F]FET PET/CT findings in clinical practice.\u003c/p\u003e\u003cp\u003eThe current study has several limitations. First, it was designed retrospectively, which is known to introduce inherent biases that may affect the generalizability of the findings. Second, the sample size was insufficient to establish a reliable reference threshold for differential diagnosis. Additionally, the prognostic value of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT was not explored, leaving this as an open avenue for future research. Therefore, further investigations with larger patient cohorts and a prospective study design are warranted to obtain more accurate and generalizable results.\u003c/p\u003e\u003cp\u003eIn conclusion, our study contributes to the growing understanding of the role of [\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eF]FET PET/CT in the evaluation of newly diagnosed cerebral lesions. Despite certain limitations, the proposed cutoff values—supported by pathophysiological rationale and comparative analysis—may aid in achieving more reliable clinical interpretations. Therefore, we cautiously suggest that [18F]FET PET/CT could be considered a useful tool in the differential diagnosis of newly diagnosed cerebral lesions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eSubjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this retrospective study, we identified 103 patients who were referred to the Department of Neurosurgery at our hospital between October 2020 and March 2025 for evaluation of intracerebral masses or lesions, all of whom underwent [\u0026sup1;⁸F]FET PET imaging. Patient enrollment was conducted based on stringent criteria to ensure a representative study cohort. Inclusion criteria encompassed individuals with newly diagnosed cerebral lesions who underwent [\u003csup\u003e18\u003c/sup\u003eF]FET PET/CT between October 2020 and March 2025 prior to any therapeutic interventions that could influence [\u003csup\u003e18\u003c/sup\u003eF]FET uptake\u0026mdash;such as surgery, biopsy, chemotherapy, radiotherapy, or radiosurgery. A neuropathologic diagnosis, obtained through stereotactic biopsy or open resection after [\u003csup\u003e18\u003c/sup\u003eF]FET PET/CT, was available for 39 patients: 22 high-grade gliomas (HGG), 3 low-grade gliomas (LGG), 4 CNS lymphomas, 3 metastasis, 2 meningioma, 4 brain abscess, and 1 encephalomyelitis. Furthermore, we included 18 additional patients without histologic evaluation, for whom both the clinical course (treatment response) and definite MR imaging findings clearly confirmed the lesion types (3 LGG, 4 metastases, 1 glioblastoma, and 10 NNL).\u003c/p\u003e\n\u003cp\u003eExclusion criteria were applied to eliminate potential confounders. Patients with incomplete or inadequate [\u003csup\u003e18\u003c/sup\u003eF]FET PET data were excluded to ensure the reliability and completeness of the dataset, Specifically, cases in which PET-positive volumes could not be delineated\u0026mdash;thereby preventing the evaluation of TBRmax and TBRmean\u0026mdash;were excluded from the analysis. Additionally, individuals who did not meet the criteria for either confirmed pathology or clinical diagnosis were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the institutional review board of Ewha Womans University Seoul Hospital waived the need of obtaining informed consent (IRB no. 2025-05-060).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FET PET/CT image acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll [\u003csup\u003e18\u003c/sup\u003eF]FET PET scans were acquired using a dedicated PET/CT scanner (Discovery MI with LightBurst Digital 4-Ring Detector, GE Medical Systems, Milwaukee, WI, USA), which provides images with a three-dimensional resolution of 2.3 mm full width at half maximum at the center of the field of view. All subjects were instructed to fast for at least 4 hours prior to scan. Before the emission scan, CT scan was performed in spiral mode at 120 kVp and 250 mA. Static emission scans of 20 min were acquired 20 min after the intravenous injection of 185\u0026ndash;200 MBq of [\u0026sup1;⁸F]FET. PET images were attenuation-corrected and reconstructed on a 512 \u0026times; 512 matrix using the Q.Clear reconstruction algorithm with a \u0026szlig; value of 350.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF]FET/CT images analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSemiquantitative analysis measuring metabolic parameters of the target lesions on [\u003csup\u003e18\u003c/sup\u003eF]FET PET was conducted using MIM Encore software (MIM Software Inc. Cleveland, OH, USA). Background activity on [\u0026sup1;⁸F]FET PET, used for calculating the target-to-background ratio (TBR), was assessed in contralateral, healthy-appearing cerebral tissue encompassing both gray and white matter. In accordance with recommendations from the EANM, SNMMI, EANO, and RANO, this measurement was performed using a crescent-shaped volume of interest placed in the frontal lobe \u003csup\u003e26,27\u003c/sup\u003e. A spherical volume encompassing the tumor lesion was manually drawn, and volumes of interest (VOIs) were automatically defined using a standardized uptake value (SUV) equal to or greater than 1.6 times the mean background activity \u003csup\u003e26\u003c/sup\u003e. To avoid inclusion of physiologically high-uptake structures\u0026mdash;such as vasculature, muscles, choroid plexus\u0026mdash;within the volume of interest, a visual plausibility check was conducted and manual corrections were applied as necessary. The TBR\u003csub\u003emax\u003c/sub\u003e and TBR\u003csub\u003emean\u003c/sub\u003e were quantified to characterize the [\u003csup\u003e18\u003c/sup\u003eF]FET uptake within the identified VOIs by dividing the maximum and mean uptake value by the mean SUV of the healthy background.\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics included the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or frequency for each clinical characteristic. Quantitative variables were analyzed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test for normally distributed data, while the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test was employed for data that did not meet the assumption of normality, as assessed by the Shapiro\u0026ndash;Wilk test. ROC curve analysis based on TBR\u003csub\u003emax\u003c/sub\u003e and TBR\u003csub\u003emean\u003c/sub\u003e as well as SUV\u003csub\u003emax\u003c/sub\u003e and SUV\u003csub\u003emean\u003c/sub\u003e was performed to evaluate the diagnostic performance of [\u003csup\u003e18\u003c/sup\u003eF]FET uptake metrics in differentiating between BNs and NNLs, as well as between high-grade tumors (HGG or lymphoma) and LGGs/NNLs, and between LGGs and NNLs. Optimal cutoff values for each parameter were determined based on ROC curve analysis using the Youden index to achieve the best balance between sensitivity and specificity. Analyses were conducted with R software (version 4.3.1, www.Rproject.org), and \u0026ldquo;multipleROC\u0026rdquo; package was used to plot the ROC curves. All statistical tests were two-sided, with a significance level of 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00449684 and RS-2024-00339811) and the Ministry of Health and Welfare (RS-2024-00439928).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest statement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.Y.K., S.J.P., and B.S.K. conceptualized the study.S.Y.K. curated the data and performed the formal analysis.B.S.M. and B.S.K. acquired funding.S.Y.K. and S.J.P. conducted the investigation and developed the methodology.S.J.P. and B.S.M. provided resources.B.S.K. supervised the project.S.J.P., B.S.M., and B.S.K. validated the results.S.Y.K. was responsible for data visualization.S.Y.K. and S.J.P. wrote the original draft.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00449684 and RS-2024-00339811) and the Ministry of Health and Welfare (RS-2024-00439928).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated or analyzed during the study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFletcher, J. 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PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): a report of the RANO group. \u003cem\u003eLancet Oncol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, e29\u0026ndash;e41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s1470-2045(23)00525-9\u003c/span\u003e\u003cspan address=\"10.1016/s1470-2045(23)00525-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"[18F]FET PET, brain tumor, diagnosis, high-grade glioma, non-neoplastic lesions","lastPublishedDoi":"10.21203/rs.3.rs-7042589/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7042589/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eO-(2-[¹⁸F]Fluoroethyl)-L-tyrosine ([¹⁸F]FET) PET is a valuable tool for the initial assessment of newly developed cerebral lesions, offering diagnostic and grading information for brain neoplasms. We aim to investigate clues for initial differential diagnosis in patients with newly developed cerebral lesions. We retrospectively analyzed 57 patients who underwent [¹⁸F]FET PET to evaluate newly diagnosed brain lesions. Tumor-to-brain ratios (TBRmax and TBRmean) of [¹⁸F]FET uptake were assessed to differentiate brain neoplastic lesions (BNLs) from non-neoplastic lesions (NNLs), and between tumor subgroups. [¹⁸F]FET uptake was significantly higher in BNLs compared to NNLs (TBRmax: 3.82 ± 1.41 vs 2.36 ± 0.60, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; TBRmean: 2.22 ± 0.41 vs 1.70 ± 0.35, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). ROC analysis identified a TBRmax cutoff of 2.77 to distinguish BNLs from NNLs (sensitivity: 86.7%, specificity: 78.6%, AUC: 0.837). Further analysis showed that high-grade tumors (e.g., high-grade gliomas and lymphoma) had higher TBRmax values than low-grade gliomas or NNLs (4.18 ± 1.31 vs 2.52 ± 1.09, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), with a cutoff of 2.82 (sensitivity: 85.7%, specificity: 88.9%, AUC: 0.887, 95% CI: 0.779–0.996). [¹⁸F]FET PET uptake provides important diagnostic information for differentiating brain neoplasms from non-neoplastic lesions and helps stratify tumor grades at initial presentation.\u003c/p\u003e","manuscriptTitle":"Diagnostic performance of [18F]FET PET in newly diagnosed cerebral lesions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 06:38:03","doi":"10.21203/rs.3.rs-7042589/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f449137-3a48-41b4-b9a4-a0c836e368ff","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52068994,"name":"Health sciences/Biomarkers"},{"id":52068995,"name":"Biological sciences/Cancer"},{"id":52068996,"name":"Health sciences/Neurology"},{"id":52068997,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-09-16T03:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 06:38:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7042589","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7042589","identity":"rs-7042589","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}