Usefulness of whole-body 18F-FDG PET/CT in the presurgical discrimination of cerebellar tumors

Usefulness of whole-body 18F-FDG PET/CT in the presurgical discrimination of cerebellar tumors | 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 Usefulness of whole-body 18 F-FDG PET/CT in the presurgical discrimination of cerebellar tumors Shu Zhang, Leilei Yuan, Qian Chen, Lin Ai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8759459/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 Purpose The present study aims to evaluate the value of whole-body 18 F-FDG PET/CT in distinguishing among different cerebellar tumor types. Methods We retrospectively analyzed 18 F-FDG PET/CT images of 86 patients with histologically confirmed cerebellar tumors, including 25 metastases, 17 lymphomas, 9 low-grade gliomas, 13 high-grade gliomas, 16 hemangioblastomas, and 6 medulloblastomas. Tumors were initially classified as PET-positive or PET-negative by visual assessment. For PET-positive cases, semiquantitative parameters—including the maximum, mean, and peak tumor-to-normal-brain ratios (TNRmax, TNRmean, and TNRpeak, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)—were measured and compared pairwise. Parameters significant for differential diagnosis were evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy. Additionally, the detection of suspicious extracranial malignancy on torso PET/CT was recorded, and its diagnostic value for metastasis was assessed. Results Nearly all hemangioblastomas were PET-negative, with visual assessment achieving a diagnostic accuracy of 97.67% for this tumor type. Lymphomas presented the highest TNRmax, TNRmean, and TNRpeak values, whereas low-grade gliomas presented the lowest values. For distinguishing lymphoma, the AUCs for TNRmax, TNRmean, and TNRpeak were 0.881, 0.889, and 0.898, respectively. When optimal cutoff values of > 1.67, > 0.94, and > 1.37 were used, the diagnostic accuracies were 71.01%, 75.36%, and 85.51%, respectively. For identifying low-grade glioma, the same parameters yielded AUCs of 0.904, 0.916, and 0.869, respectively. With optimal cutoff values of < 0.77, < 0.52, and < 0.62, the accuracies were 92.75%, 91.30%, and 89.86%, respectively. Medulloblastoma demonstrated the highest MTV and TLG. MTV yielded an AUC of 0.847 for differentiating medulloblastoma from other cerebellar tumors, and an optimal cutoff value of > 12.85 provided an accuracy of 89.86%. Torso PET/CT for detecting suspicious extracranial malignancy excelled at diagnosing metastasis, with an accuracy of 96.51%. Conclusion Visual analysis and metabolic parameters from brain 18 F-FDG PET are valuable for differentiating hemangioblastoma, lymphoma, low-grade glioma, and medulloblastoma. Whole-body PET/CT contributes to the diagnosis of metastasis by identifying suspicious extracranial malignancies. Cerebellar neoplasms 18F-FDG PET differential diagnosis Figures Figure 1 Figure 2 Figure 3 Introduction Cerebellar tumors are relatively rare, accounting for 2.1% of all central nervous system tumors[ 1 ]. The spectrum of cerebellar tumors is diverse and distinct from that of supratentorial tumors, with the most common types being metastases, followed by hemangioblastomas, pilocytic astrocytomas, and medulloblastomas. Accurate preoperative diagnosis is crucial for developing appropriate treatment strategies. For instance, suspected metastasis necessitates a comprehensive a systemic stage study to formulate an individual treatment plan[ 2 ]. Surgical resection is the primary treatment option for gliomas and medulloblastomas. In cases of solid hemangioblastoma, combining surgical resection with preoperative embolization can reduce intraoperative bleeding, facilitate complete tumor removal, and prevent the need for puncture biopsy[ 3 ]. Conventional contrast-enhanced Magnetic Resonance Imaging (MRI) is the primary modality used for the differential diagnosis of cerebellar tumors; however, the imaging features of different tumor types often overlap[ 4 ]. For example, brain metastases typically exhibit nodular or annular enhancement, making them difficult to distinguish from glioma or lymphoma. Similarly, solid or mixed cystic-solid hemangioblastomas can be challenging to differentiate from malignant tumors. Thus, conventional morphological imaging alone is often insufficient for reliable presurgical diagnosis. Previous studies suggest that advanced MRI techniques, including diffusion-weighted imaging and perfusion-weighted imaging, can assist in diagnosing hemangioblastomas, pilocytic astrocytomas, metastases, and medulloblastomas [ 5 – 8 ]. However, their utility in diagnosing high-grade gliomas and lymphomas remains limited. Moreover, diffusion and perfusion parameters in brain metastases may vary widely due to the heterogeneous nature of primary tumor pathologies. PET/CT has gained increasing popularity and offers a complementary approach for the differential diagnosis of brain tumors. Although several novel radiotracers have been developed for brain tumor imaging, such as 11 C-methionine[ 9 ], 18 F-fluoroethyltyrosine[ 10 ], 18 F-fluoroglutamine[ 11 ], 18 F-DPA714[ 12 ], 18 F-FDG remains the most widely used tracer in clinical practice, reflecting the extent of glucose metabolism within lesions. Previous studies have demonstrated that 18 F-FDG PET/CT is highly effective in differentiating glioblastoma from lymphoma [ 13 , 14 ]. However, owing to the low incidence of cerebellar tumors, evidence regarding the utility of PET imaging in this region remains scarce. Takahashi et al. reported that 18 F-FDG PET can accurately distinguish solid hemangioblastomas from other cerebellar tumors. Yamauchi et al. reported that 18 F-FDG PET is helpful for differentiating cerebellar hemangioblastoma, low-grade glioma, and lymphoma. Nonetheless, these studies were limited by small sample sizes and a restricted spectrum of diseases, underscoring the need for larger, systematic investigations to validate the role of 18 F-FDG PET in the differential diagnosis of cerebellar tumors. An additional advantage of PET/CT is its ability to perform convenient whole-body scanning. Metabolic and morphological evaluations of extracranial lesions may provide valuable information for the diagnosis of brain tumors. The presence of suspicious malignant lesions in the trunk may support a diagnosis of metastasis. However, the value of whole-body PET/CT imaging in identifying cerebellar metastasis has not been well established. This study enrolled a diverse range of cerebellar tumors and aimed to evaluate the utility of semiquantitative parameters derived from brain 18 F-FDG PET in differential diagnosis. Furthermore, the diagnostic value of torso PET/CT findings for identifying cerebellar metastases was also assessed. Materials and methods Patients This retrospective study included patients with histologically confirmed cerebellar tumors who underwent preoperative whole-body 18 F-FDG PET/CT at Beijing Tiantan Hospital between September 2014 and September 2025. The exclusion criteria were as follows: (1) a history of malignant tumors; (2) multiple lesions involving both the cerebellum and the other region; and (3) a tumor with a maximum diameter of less than 5 mm and adjacent to the cerebral cortex, which may make the quantitative analysis inaccurate. A total of 86 patients (47 men and 39 women), with a mean age of 57.74 ± 15.09 years, were enrolled. The cohort comprised 25 patients with metastases, 17 patients with lymphomas, 9 patients with low-grade gliomas, 13 patients with high-grade gliomas, 16 patients with hemangioblastomas, and 6 patients with medulloblastomas. Among these, multiple cerebellar lesions (two or more) were present in one patient with metastasis, five with lymphoma, one with low-grade glioma, and three with medulloblastoma. A detailed inclusion flowchart is provided in Fig. 1 . Whole-body 18 F-FDG PET/CT imaging All patients fasted for 4–6 h prior to the scan. An 18 F-FDG dose of 0.08mci/kg (2.96 MBq/kg) was intravenously injected after ensuring that blood glucose levels were < 11.1 mmol/l. After 18 F-FDG injection until the start of imaging, the patients rested in a quiet and dimly lit room for 40–60 minutes. PET/CT was performed on an Elite Discovery PET/CT scanner (GE Healthcare, Milwaukee, WI, USA). First, torso PET/CT was performed, covering the anatomy from the skull base to the upper thigh. This coverage was achieved using 7 to 8 beds, with 2 minutes per bed. Afterward, brain PET acquisition was performed for 10 minutes in 3D mode. Low-dose CT was used for attenuation correction. PET images were reconstructed with VUE Point FX (18 subsets, 5 iterations, 1.0 mm Z-axis filter, matrix 192 × 192). Image analysis of brain PET/CT Two experienced nuclear medicine physicians independently performed visual evaluations of the brain 18 F-FDG PET/CT images. Brain tumors were classified as PET-positive if their radioactive uptake exceeded that of the normal white matter, or PET-negative if it was lower. Any discrepancies in interpretation were resolved through consensus. Interobserver consistency between the two physicians was evaluated. Then, semiquantitative analysis of positive brain tumors was subsequently conducted by a nuclear medicine physician as follows: Each lesion was semiautomatically delineated using a threshold of 40% of the maximum standardized uptake value (SUVmax). The SUVmax, mean standardized uptake value (SUVmean), peak standardized uptake value (SUVpeak), metabolic total volume (MTV), and total lesion glycolysis (TLG) were measured within the lesion. In the left normal frontal cortex, a circular area of 20–30 mm 2 was delineated, and its mean standardized uptake value was measured and recorded as SUVnormal. The maximum, mean, and peak tumor-to-normal ratios (TNRmax, TNRmean, TNRpeak, respectively) were calculated by dividing the respective SUV values (SUVmax, SUVmean, SUVpeak, respectively) by SUVnormal. For patients with multiple lesions, the TNR values of the lesion with the highest 18 F-FDG uptake were selected. MTV and TLG were calculated as the sum of multiple lesions. Image analysis of torso PET/CT The torso PET/CT scan was thoroughly reviewed by two experienced nuclear medicine physicians to independently identify any suspicious extracranial malignant lesions. Suspected malignancies were identified on the basis of a combination of factors, including focally increased radiotracer uptake on PET, corresponding morphological abnormalities on CT, and the involvement of regional lymph nodes. Any discrepancies in interpretation were resolved through consensus. Interobserver consistency between the two physicians was evaluated. Statistical analysis All other statistical analyses were conducted using SPSS, version 22 (IBM, Armonk, NY). Interobserver consistency between the two nuclear medicine physicians was assessed with a kappa test for qualitative image interpretation. All semiquantitative data are expressed as medians and interquartile ranges (25th, 75th percentiles) and were assessed for normality by the Kolmogorov–Smirnov test. Normally distributed data were compared using a two-sample t test. Nonnormally distributed data were compared using a Mann–Whitney U test. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the semiquantitative parameters. The optimal cutoff value was selected on the basis of the maximum Youden’s index, and the corresponding sensitivity, specificity, and diagnostic accuracy were determined. P values < 0.05 were considered statistically significant. Results Visual analysis of brain PET/CT Excellent interobserver agreement was observed regarding the visual assessment of cerebral tumors using brain PET/CT (kappa value = 0.847). Most hemangioblastomas were PET-negative (15/16, 93.75%), while all other tumor types were PET-positive except for a single low-grade tumor. Using PET negativity as the diagnostic criterion for hemangioblastoma, the sensitivity, specificity, and accuracy were 93.75%, 98.57%, and 97.67%, respectively. Semiquantitative analysis of brain PET/CT Owing to the limited number of positive cases, hemangioblastoma was excluded from the semiquantitative analysis. Pairwise comparisons were conducted for the semiquantitative metabolic parameters of lymphoma, high-grade glioma, metastasis, medulloblastoma, and low-grade glioma. The specific metabolic parameters for each tumor type are summarized in Table 1 . Comparisons of metabolic parameters across tumor types are illustrated in Supplemental Fig. 1. The TNR values, including TNRmax, TNRmean, and TNRpeak, were highest in lymphoma, followed by high-grade glioma, medulloblastoma, and metastasis, and were lowest in low-grade glioma. Significant differences were observed in the TNRs between lymphoma and other tumors ( p < 0.05), and between low-grade glioma and other tumors ( p 0.05). Table 1 The specific metabolic parameters for each tumor type MET: metastasis; LYM: lymphoma; LGG: low-grade glioma; HGG: high-grade glioma; MB: medulloblastoma; TNRmax: maximum tumor-to-normal ratio; TNRmean: mean tumor-to-normal ratio; TNRpeak: peak tumor-to-normal ratio; TBRmax TBRmean TBRpeak MTV TLG MET 1.20(0.72, 1.90) 0.69(0.45, 1.06) 0.79(0.56, 1.13) 4.40(3.20, 8.15) 31.26(16.80, 76.62) LYM 2.74(1.93, 3.69) 1.73(1.08, 2.24) 1.87(1.31, 2.38) 5.58(3.60, 8.35) 75.10(48.15, 123.55) LGG 0.62(0.51, 0.73) 0.38(0.35, 0.46) 0.52(0.49, 0.57) 3.80(2.93, 7.60) 16.80(8.73, 31.28) HGG 1.56(0.88, 2.44) 0.93(0.62, 1.21) 1.15(0.68, 1.39) 4.60(1.90, 7.50) 27.50(17.10, 58.90) MB 1.33(1.02, 1.72) 0.77(0.66, 1.03) 0.94(0.73, 1.31) 26.99(10.40, 35.60) 218.57(61.77, 322.00) MTV was highest in medulloblastoma, followed by lymphoma, high-grade glioma, metastasis, and low-grade glioma. A statistically significant difference in MTV was observed between medulloblastoma and all the other tumors ( p < 0.05). The TLG was significantly greater for both medulloblastoma and lymphoma than for metastasis, high-grade glioma, and low-grade glioma ( p 0.05). Representative images of the cerebellar tumors are shown in Fig. 2 . Diagnostic performance of semiquantitative parameters Semiquantitative analysis revealed that the TNRmax, TNRmean, and TNRpeak were useful for distinguishing lymphoma and low-grade glioma from other tumor types, while the MTV served as an effective indicator for identifying medulloblastoma. Regarding the differentiation of lymphoma from other tumors, the AUCs of TNRmax, TNRmean, and TNRpeak were 0.881, 0.889, and 0.898, respectively. At optimal cutoff values greater than 1.67, 0.94, and 1.37, the corresponding diagnostic accuracies were 71.01%, 75.36%, and 85.51%, respectively (Table 2 ). Regarding distinguishing low-grade glioma from other tumors, TNRmax, TNRmean, and TNRpeak achieved AUC values of 0.904, 0.916, and 0.869, respectively. At optimal cutoff values below 0.77, 0.52, and 0.62, the diagnostic accuracies reached 92.75%, 91.30%, and 89.86%, respectively (Table 2 ). Table 2 Diagnostic efficacy of TNRs for cerebellar lymphoma and low-grade glioma Tumor type TNR AUC Optimal cutoff value Sensitivity Specificity Accuracy Lymphoma TNRmax 0.881 1.67 100.0% 61.54% 71.01% TNRmean 0.889 0.94 94.12% 69.24% 75.36% TNRpeak 0.898 1.37 76.47% 88.47% 85.51% Low-grade glioma TNRmax 0.904 0.77 87.50% 93.44% 92.75% TNRmean 0.916 0.52 87.50% 91.81% 91.30% TNRpeak 0.869 0.62 87.50% 90.16% 89.86% TNRmax: maximum tumor-to-normal ratio; TNRmean: mean tumor-to-normal ratio; TNRpeak: peak tumor-to-normal ratio. For the identification of medulloblastoma, the MTV yielded an AUC of 0.847. Using a cutoff value greater than 12.85 resulted in a sensitivity of 83.33%, specificity of 90.48%, and accuracy of 89.86%. Diagnostic efficacy of whole-body PET/CT for detecting brain metastasis Excellent interobserver agreement was observed for the detection of these suspicious extracranial malignancies (kappa value = 0.905). The majority of the brain metastases (24/25) were accompanied by suspicious extracranial malignancies. Among these patients, 23 had primary tumors originating from the lung (n = 21) (Fig. 3 A), esophagus (n = 1), colon (n = 1), and breast (n = 1). The remaining patient demonstrated increased FDG uptake in multiple lymph nodes throughout the body, but no definite primary tumor was found. Additionally, one patient with high-grade glioma was found to have a suspected sigmoid colon malignancy, which was pathologically confirmed as a moderately differentiated adenocarcinoma with mismatch repair deficiency on immunohistochemistry (Fig. 3 B). Another patient with lymphoma had suspicious bilateral adrenal malignancies, which were confirmed as lymphoma-involved lesions by follow-up (Fig. 3 C). Suspicious extracranial malignancy detected by torso PET/CT demonstrated high diagnostic performance for brain metastasis, with a sensitivity of 96.00%, specificity of 96.72%, and accuracy of 96.51%. Discussion 18 F-FDG PET/CT provides a comprehensive assessment of glucose metabolism in lesions throughout the body in a single examination, making it a commonly used imaging technique for preoperative differential diagnosis. However, its application for treating cerebellar tumors has been explored in only a limited number of studies with small sample sizes and a restricted spectrum of diseases. In this study, the utility of whole-body 18 F-FDG PET/CT for diagnosing these tumors was systematically evaluated. Our results demonstrate that visual analysis and metabolic parameters based on brain PET can effectively differentiate among cerebellar lymphoma, hemangioblastoma, low-grade glioma, and medulloblastoma. Furthermore, torso PET/CT contributed valuable information for diagnosing cerebellar metastasis. Hemangioblastoma is a common benign cerebellar tumor. The cystic-solid or solid types, which demonstrate marked enhancement on contrast-enhanced MRI and increased perfusion on perfusion-weighted images, can be challenging to distinguish from metastases and high-grade gliomas, particularly in the absence of flow voids [ 15 , 16 ]. In this study, we observed that nearly all hemangioblastomas were PET-negative, with only a single case showing mild FDG uptake with low TNRs. These finding are consistent with the report by Takahashi et al., who reported that compared with metastases, gliomas, and lymphomas, solid hemangioblastomas have lower SUVmax values on FDG PET [ 17 ]. Histopathologically, hemangioblastomas are composed mainly of a dense capillary network with intervening nests or sheets of stromal cells[ 18 ]. This structural composition, coupled with the low proliferative activity of the stromal cells, results in the characteristically low glucose metabolism observed on PET imaging. A cerebellar tumor that demonstrates no obvious FDG uptake but is characterized by high cerebral blood volume on perfusion-weighted imaging typically suggests a diagnosis of hemangioblastoma. Our semiquantitative PET analysis confirmed the utility of TNRs in diagnosing lymphoma and low-grade glioma. As previously documented, central nervous system lymphoma is characterized by high FDG avidity [ 13 , 14 ] and is associated with high tumor cell density and activation of glucose transporters and hexokinase II[ 19 , 20 ]. Our findings align with these findings, showing that compared with other tumor types, cerebellar lymphomas exhibit significantly higher FDG metabolism. Among the semiquantitative indices, TBRpeak yielded the highest diagnostic accuracy (85.5%) at an optimal threshold of 1.37. Consequently, integrating homogenous enhancement on contrast-enhanced MRI with marked FDG avidity strongly supports a diagnosis of cerebellar lymphoma. In this study, cerebellar low-grade gliomas—including pilocytic astrocytoma, pilomyxoid astrocytoma, and other astrocytomas—demonstrated significantly lower TNRs than their malignant counterparts, a finding that is consistent with those seen in prior literature [ 21 ]. This low FDG avidity likely reflects their characteristically low proliferative activity. TNRmax yielded the highest diagnostic accuracy (92.75%), when an optimal cutoff of < 0.77 was used. Notably, one patient with a Grade II astrocytoma exhibited markedly high metabolic activity. Unfortunately, this individual subsequently experienced tumor recurrence and mortality within one year after surgery. This aggressive clinical course suggests the possible presence of a high-grade glioma component that was not sampled or identified during the initial pathological examination. Consequently, FDG PET may serve as a complementary tool for histopathology, offering critical prognostic guidance. Cerebellar medulloblastoma typically appears as a solid mass with marked enhancement on contrast-enhanced MRI and restricted diffusion on diffusion-weighted imaging, mimicking lymphoma or high-grade glioma. Previous studies, largely based on case reports, have indicated that patients with medulloblastoma exhibit increased glucose and amino acid metabolism, along with upregulated expression of the octreotide receptor [ 21 – 24 ]. However, no reliable metabolic parameter has been established to effectively differentiate it from glioblastoma or metastasis. A key novel finding in this study is that medulloblastoma presented significantly higher MTV, which effectively aids in differentiation. When an optimal cutoff of ≥ 12.85 was used, MTV achieved diagnostic sensitivity, specificity, and accuracy of 83.3%, 90.6%, and 90.0%, respectively. The high MTV is likely attributed to the predominantly solid and often multifocal nature of the tumor. While medulloblastoma and lymphoma both show restricted diffusion on DWI, medulloblastoma can be distinguished by its characteristically higher MTVs and lower TNRs, offering a practical diagnostic solution. In this investigation, semi-quantitative PET parameters demonstrated limited utility in differentiating cerebellar metastases from high-grade gliomas. However, the identification of suspicious extracranial malignancies on torso PET/CT provided crucial diagnostic clues for cerebellar metastases. The detection rate for primary tumors among cerebellar metastases reached 94.7%, with pulmonary origins predominating and less frequent occurrences in the esophagus, colon, and breast. Previous studies have reported FDG PET/CT detection rates for primary tumors in patients with brain metastasis ranging from 74% to 77% [ 7 , 25 ]. The higher detection rate observed in our series may be attributable to its relatively small sample size. Notably, the majority of nonmetastatic cases exhibited no suspicious extracranial findings on whole-body PET/CT. One exception was a lymphoma patient who demonstrated bilateral adrenal thickening with intensified FDG metabolism, which was subsequently confirmed as having lymphomatous involvement—which is consistent with known patterns of extranodal lymphoma involvement [ 26 ]. In another case, a high-grade glioma was preoperatively misdiagnosed as metastasis due to the detection of a suspicious malignancy with focal FDG avidity on the sigmoid on torso PET/CT. Subsequent pathology confirmed a moderately differentiated adenocarcinoma with mismatch repair deficiency on immunohistochemistry. The coexistence of colorectal adenocarcinoma and glioma is rare and often indictive of Lynch syndrome [ 27 , 28 ], a possibility further supported by immunohistochemical findings in this patient. Therefore, the presence of suspicious malignant lesions on whole-body PET/CT should raise a strong suspicion that cerebellar lesions represent metastases. However, when cerebellar and adrenal masses with elevated FDG metabolism are concurrently detected, lymphoma should be considered in the differential diagnosis, and biopsy is preferred over surgical resection for pathological confirmation. For patients with a suspected colonic malignancy coexisting with a cerebellar space-occupying lesion, glioma remains a diagnostic consideration, particularly when the colon lesion shows evidence of mismatch repair deficiency. In such cases, surgical resection is the preferred initial management for intracranial tumors. This study has two limitations. First, the sample size was relatively small, particularly for patients diagnosed with glioma and medulloblastoma, which may have reduced the statistical power of our analyses. Nevertheless, the cohort in this study is relatively larger than those reported in previous PET studies focusing on cerebellar tumors. Future large-scale, multicenter studies are required to validate these preliminary findings. Another limitation is that the diagnostic performance of 18 F-FDG was assessed as a standalone tracer. In current neuro-oncology practice, amino acid PET tracers such as 11 C-MET are increasingly being used for differential diagnosis and grading of brain tumor. Despite its reported limited value in discriminating benign from malignant cerebellar tumors because of high uptake in lesions such as hemangioblastomas and pilocytic astrocytomas[ 17 , 29 ], amino acid PET provides a characteristically elevated tumor-to-background ratio that is valuable for delineating tumor margins. Future head-to-head comparative studies are warranted to further evaluate the diagnostic value of FDG versus amino acid PET in differentiating cerebellar tumors, as well as their potential synergistic use. In summary, FDG PET metabolic parameters serve specific diagnostic roles: the TNR aids in distinguishing lymphoma and low-grade glioma, while elevated MTV and TLG values assist in identifying medulloblastoma. Additionally, when torso PET/CT reveals suspicious extracranial malignancies, it enables a relatively accurate diagnosis of cerebellar metastases. Consequently, whole-body FDG PET/CT is an effective imaging modality for the differential diagnosis of cerebellar tumors and provides valuable support for clinical decision-making in treatment planning. Declarations Funding No funding was received for conducting this study. Ethics approval The study was approved by the Animal and Human Ethics Committee of Beijing Tiantan Hospital, Capital Medical University, and was conducted in accordance with the Declaration of Helsinki. Consent to participate Written informed consent was obtained from all patients or their legal guardians. Consent for publication Not applicable. Author Contributions Shu Zhang: Conceptualization, study design, data collection, statistical analysis, and writing—original draft. Leilei Yuan and Qian Chen: Data analysis and interpretation. Lin Ai: Project supervision and critical revision of the manuscript. All authors reviewed and approved the final version of the manuscript. Competing Interests The authors declare no competing interests. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. 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CNS Oncol 7:CNS12 Shen H, Wei Z, Zhou D et al (2018) Primary extra-nodal diffuse large B-cell lymphoma: A prognostic analysis of 141 patients. Oncol Lett 16:1602–1614 Togawa A, Ueno M, Yamaoka M et al (2025) Glioblastoma Arising in Lynch-like Syndrome after Repeated Development of Colorectal Cancers. Intern Med 64:1189–1193 Binder ZA, Johnson MW, Joshi A et al (2011) Glioblastoma multiforme in the Muir-Torre syndrome. Clin Neurol Neurosurg 113:411–415 Beuriat PA, Flaus A, Portefaix A et al (2024) Preoperative 11 C-Methionine PET-MRI in Pediatric Infratentorial Tumors. Clin Nucl Med 49:381–386 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.pdf 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. 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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-8759459","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585267777,"identity":"d6164979-34b2-4de7-a988-b925c78019f2","order_by":0,"name":"Shu Zhang","email":"","orcid":"","institution":"Beijing Tiantan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Zhang","suffix":""},{"id":585267778,"identity":"015ab13c-d6fb-4273-bdd6-9ec45dcfa030","order_by":1,"name":"Leilei Yuan","email":"","orcid":"","institution":"Beijing Tiantan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Leilei","middleName":"","lastName":"Yuan","suffix":""},{"id":585267780,"identity":"6eeaf3c7-4d46-4591-86e4-09729598d94e","order_by":2,"name":"Qian Chen","email":"","orcid":"","institution":"Beijing Tiantan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Chen","suffix":""},{"id":585267783,"identity":"9256258d-5b61-4bdc-ae59-a98e3eb7c5b1","order_by":3,"name":"Lin Ai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACZuaGAwwVB5gZGHiI1sII1HLmADMPG9FaGBgbGBjbDjAQr4W/nbHxcOG8O+z28r0HGD7uqSWsReIwY8PhmdueAR3Gl8A449lxIqwBaeHddhjkFwNmngPHCOuQB2uZQ4oWA7CWBriWGsJaDEFaeI4B/XIsx+DgjAMHCGuRO3/48GeemjvJ7M1nDB98OFBHWAsMJIMIoBWHiddiB6VJsGUUjIJRMApGDAAAlGw60BD1Ct0AAAAASUVORK5CYII=","orcid":"","institution":"Beijing Tiantan Hospital, Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Ai","suffix":""}],"badges":[],"createdAt":"2026-02-02 01:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8759459/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8759459/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101874657,"identity":"4a16b70c-8636-44a0-b622-bbdec59bad43","added_by":"auto","created_at":"2026-02-04 14:01:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84928,"visible":true,"origin":"","legend":"\u003cp\u003ePatient inclusion and exclusion criteria for the study cohorts.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8759459/v1/f8e0de052eb4b322232be5bb.jpg"},{"id":101874658,"identity":"813bff6f-86d1-4355-8114-bccc4bb15698","added_by":"auto","created_at":"2026-02-04 14:01:13","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227254,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative imaging findings of patients with various cerebellar tumors. (A-C). Cerebellar metastasis in the right hemisphere. On contrast-enhanced T1-weighted images (CE-T1WI), the lesion exhibits ring enhancement. \u003csup\u003e18\u003c/sup\u003eF-FDG PET shows moderate radiotracer uptake. The TNRmax, TNRmean, TNRpeak, MTV, and TLG of the lesion were 1.31, 0.66, 0.71, 6.9 mL, and 36.2 g, respectively. (D-F) Cerebellar lymphoma in the vermis extending into the fourth ventricle. CE-T1WI demonstrates intense, homogeneous enhancement, and \u003csup\u003e18\u003c/sup\u003eF-FDG PET reveals marked uptake.\u0026nbsp;The TNRmax, TNRmean, TNRpeak, MTV, and TLG of the lesion were 4.08, 2.65, 2.74, 2.90 mL, and 89.80 g, respectively. (G-I). Pilocytic astrocytoma in the right cerebellar hemisphere. The lesion displays irregular ring-enhancement on CE-T1WI, but mild uptake on \u003csup\u003e18\u003c/sup\u003eF-FDG PET images. The TNRmax, TNRmean, TNRpeak, MTV, and TLG of the lesion were 0.56, 0.31, 0.49, 0.7 mL, and 1.9 g, respectively. (J-L). Cerebellar glioblastoma in the right hemisphere. The cystic-solid lesion shows marked enhancement on CE-T1WI and elevated uptake on \u003csup\u003e18\u003c/sup\u003eF-FDG PET images. The TNRmax, TNRmean, TNRpeak, MTV, and TLG of the lesion were 1.74, 0.97, 1.19, 9.5 mL, and 117.70 g, respectively. (M-O). Medulloblastoma involving the bilateral cerebellar hemispheres. The lesion demonstrates intense enhancement on CE-T1WI and extensive, marked uptake on \u003csup\u003e18\u003c/sup\u003eF-FDG PET.\u0026nbsp;The TNRmax, TNRmean, TNRpeak, MTV, and TLG of the lesion were 1.89, 1.06, 1.41, 32.50 mL, and 353.80 g, respectively. (P-Q). Cerebellar hemangioblastoma in the right hemisphere. CE-T1WI shows heterogeneous, intense enhancement, whereas \u003csup\u003e18\u003c/sup\u003eF-FDG PET demonstrates no significant radiotracer uptake.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8759459/v1/fe442cadd51e65589d23152d.jpg"},{"id":101874656,"identity":"bad912d3-0aa7-45f6-aed1-12ed19bb82cb","added_by":"auto","created_at":"2026-02-04 14:01:13","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134792,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative whole-body PET/CT findings in patients with suspected extracranial malignancies. Hypervascular and FDG-avid masses were identified on contrast-enhanced T1-weighted images and brain PET in all three cases (white arrows). (A) Cerebellar metastasis: Torso PET/CT shows an FDG-avid mass in the right lower lobe (red arrow) and an FDG-avid lymph node in the right pulmonary hilum (blue arrow), suggestive of primary lung malignancy with nodal metastasis, which was pathologically confirmed as adenocarcinoma. (B) Cerebellar glioblastoma: Torso PET/CT reveals a focus of increased FDG uptake in the sigmoid colon (red arrow), later confirmed as a moderately differentiated adenocarcinoma with mismatch repair deficiency. Bilateral hilar FDG-avid lymph nodes (blue arrows) were considered chronic non-specific lymphadenitis. (C) Cerebellar lymphoma: Torso PET/CT demonstrates bilateral adrenal thickening with increased FDG uptake, confirmed as lymphoma involvement. Incidentally noted was L4-L5 facet joint arthritis (blue arrow).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8759459/v1/3609a86cd2a70d75df455f6c.jpg"},{"id":101943197,"identity":"faea3fb9-34a4-44e2-89da-eadb3924a557","added_by":"auto","created_at":"2026-02-05 09:41:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":960334,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8759459/v1/afb4680b-4bd9-4873-a564-0fc551701ce6.pdf"},{"id":101874659,"identity":"4aa21ed4-e8b5-4296-9032-e9fcd505585e","added_by":"auto","created_at":"2026-02-04 14:01:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":379117,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8759459/v1/a01e17faab74f7aa795b2ccb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUsefulness of\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003ewhole-body \u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eF-FDG PET/CT in the presurgical discrimination of cerebellar tumors\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebellar tumors are relatively rare, accounting for 2.1% of all central nervous system tumors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The spectrum of cerebellar tumors is diverse and distinct from that of supratentorial tumors, with the most common types being metastases, followed by hemangioblastomas, pilocytic astrocytomas, and medulloblastomas. Accurate preoperative diagnosis is crucial for developing appropriate treatment strategies. For instance, suspected metastasis necessitates a comprehensive a systemic stage study to formulate an individual treatment plan[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Surgical resection is the primary treatment option for gliomas and medulloblastomas. In cases of solid hemangioblastoma, combining surgical resection with preoperative embolization can reduce intraoperative bleeding, facilitate complete tumor removal, and prevent the need for puncture biopsy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional contrast-enhanced Magnetic Resonance Imaging (MRI) is the primary modality used for the differential diagnosis of cerebellar tumors; however, the imaging features of different tumor types often overlap[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. For example, brain metastases typically exhibit nodular or annular enhancement, making them difficult to distinguish from glioma or lymphoma. Similarly, solid or mixed cystic-solid hemangioblastomas can be challenging to differentiate from malignant tumors. Thus, conventional morphological imaging alone is often insufficient for reliable presurgical diagnosis. Previous studies suggest that advanced MRI techniques, including diffusion-weighted imaging and perfusion-weighted imaging, can assist in diagnosing hemangioblastomas, pilocytic astrocytomas, metastases, and medulloblastomas [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, their utility in diagnosing high-grade gliomas and lymphomas remains limited. Moreover, diffusion and perfusion parameters in brain metastases may vary widely due to the heterogeneous nature of primary tumor pathologies.\u003c/p\u003e \u003cp\u003ePET/CT has gained increasing popularity and offers a complementary approach for the differential diagnosis of brain tumors. Although several novel radiotracers have been developed for brain tumor imaging, such as \u003csup\u003e11\u003c/sup\u003eC-methionine[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], \u003csup\u003e18\u003c/sup\u003eF-fluoroethyltyrosine[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], \u003csup\u003e18\u003c/sup\u003eF-fluoroglutamine[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], \u003csup\u003e18\u003c/sup\u003eF-DPA714[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], \u003csup\u003e18\u003c/sup\u003eF-FDG remains the most widely used tracer in clinical practice, reflecting the extent of glucose metabolism within lesions. Previous studies have demonstrated that \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT is highly effective in differentiating glioblastoma from lymphoma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, owing to the low incidence of cerebellar tumors, evidence regarding the utility of PET imaging in this region remains scarce. Takahashi et al. reported that \u003csup\u003e18\u003c/sup\u003eF-FDG PET can accurately distinguish solid hemangioblastomas from other cerebellar tumors. Yamauchi et al. reported that \u003csup\u003e18\u003c/sup\u003eF-FDG PET is helpful for differentiating cerebellar hemangioblastoma, low-grade glioma, and lymphoma. Nonetheless, these studies were limited by small sample sizes and a restricted spectrum of diseases, underscoring the need for larger, systematic investigations to validate the role of \u003csup\u003e18\u003c/sup\u003eF-FDG PET in the differential diagnosis of cerebellar tumors.\u003c/p\u003e \u003cp\u003eAn additional advantage of PET/CT is its ability to perform convenient whole-body scanning. Metabolic and morphological evaluations of extracranial lesions may provide valuable information for the diagnosis of brain tumors. The presence of suspicious malignant lesions in the trunk may support a diagnosis of metastasis. However, the value of whole-body PET/CT imaging in identifying cerebellar metastasis has not been well established.\u003c/p\u003e \u003cp\u003eThis study enrolled a diverse range of cerebellar tumors and aimed to evaluate the utility of semiquantitative parameters derived from brain \u003csup\u003e18\u003c/sup\u003eF-FDG PET in differential diagnosis. Furthermore, the diagnostic value of torso PET/CT findings for identifying cerebellar metastases was also assessed.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eThis retrospective study included patients with histologically confirmed cerebellar tumors who underwent preoperative whole-body \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT at Beijing Tiantan Hospital between September 2014 and September 2025. The exclusion criteria were as follows: (1) a history of malignant tumors; (2) multiple lesions involving both the cerebellum and the other region; and (3) a tumor with a maximum diameter of less than 5 mm and adjacent to the cerebral cortex, which may make the quantitative analysis inaccurate. A total of 86 patients (47 men and 39 women), with a mean age of 57.74\u0026thinsp;\u0026plusmn;\u0026thinsp;15.09 years, were enrolled. The cohort comprised 25 patients with metastases, 17 patients with lymphomas, 9 patients with low-grade gliomas, 13 patients with high-grade gliomas, 16 patients with hemangioblastomas, and 6 patients with medulloblastomas. Among these, multiple cerebellar lesions (two or more) were present in one patient with metastasis, five with lymphoma, one with low-grade glioma, and three with medulloblastoma. A detailed inclusion flowchart is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhole-body \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging\u003c/p\u003e \u003cp\u003eAll patients fasted for 4\u0026ndash;6 h prior to the scan. An \u003csup\u003e18\u003c/sup\u003eF-FDG dose of 0.08mci/kg (2.96 MBq/kg) was intravenously injected after ensuring that blood glucose levels were \u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;11.1 mmol/l. After \u003csup\u003e18\u003c/sup\u003eF-FDG injection until the start of imaging, the patients rested in a quiet and dimly lit room for 40\u0026ndash;60 minutes. PET/CT was performed on an Elite Discovery PET/CT scanner (GE Healthcare, Milwaukee, WI, USA). First, torso PET/CT was performed, covering the anatomy from the skull base to the upper thigh. This coverage was achieved using 7 to 8 beds, with 2 minutes per bed. Afterward, brain PET acquisition was performed for 10 minutes in 3D mode. Low-dose CT was used for attenuation correction. PET images were reconstructed with VUE Point FX (18 subsets, 5 iterations, 1.0 mm Z-axis filter, matrix 192 \u0026times; 192).\u003c/p\u003e \u003cp\u003eImage analysis of brain PET/CT\u003c/p\u003e \u003cp\u003eTwo experienced nuclear medicine physicians independently performed visual evaluations of the brain \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT images. Brain tumors were classified as PET-positive if their radioactive uptake exceeded that of the normal white matter, or PET-negative if it was lower. Any discrepancies in interpretation were resolved through consensus. Interobserver consistency between the two physicians was evaluated. Then, semiquantitative analysis of positive brain tumors was subsequently conducted by a nuclear medicine physician as follows: Each lesion was semiautomatically delineated using a threshold of 40% of the maximum standardized uptake value (SUVmax). The SUVmax, mean standardized uptake value (SUVmean), peak standardized uptake value (SUVpeak), metabolic total volume (MTV), and total lesion glycolysis (TLG) were measured within the lesion. In the left normal frontal cortex, a circular area of 20\u0026ndash;30 mm\u003csup\u003e2\u003c/sup\u003e was delineated, and its mean standardized uptake value was measured and recorded as SUVnormal. The maximum, mean, and peak tumor-to-normal ratios (TNRmax, TNRmean, TNRpeak, respectively) were calculated by dividing the respective SUV values (SUVmax, SUVmean, SUVpeak, respectively) by SUVnormal. For patients with multiple lesions, the TNR values of the lesion with the highest \u003csup\u003e18\u003c/sup\u003eF-FDG uptake were selected. MTV and TLG were calculated as the sum of multiple lesions.\u003c/p\u003e \u003cp\u003eImage analysis of torso PET/CT\u003c/p\u003e \u003cp\u003eThe torso PET/CT scan was thoroughly reviewed by two experienced nuclear medicine physicians to independently identify any suspicious extracranial malignant lesions. Suspected malignancies were identified on the basis of a combination of factors, including focally increased radiotracer uptake on PET, corresponding morphological abnormalities on CT, and the involvement of regional lymph nodes. Any discrepancies in interpretation were resolved through consensus. Interobserver consistency between the two physicians was evaluated.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll other statistical analyses were conducted using SPSS, version 22 (IBM, Armonk, NY). Interobserver consistency between the two nuclear medicine physicians was assessed with a kappa test for qualitative image interpretation. All semiquantitative data are expressed as medians and interquartile ranges (25th, 75th percentiles) and were assessed for normality by the Kolmogorov\u0026ndash;Smirnov test. Normally distributed data were compared using a two-sample \u003cem\u003et\u003c/em\u003e test. Nonnormally distributed data were compared using a Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the semiquantitative parameters. The optimal cutoff value was selected on the basis of the maximum Youden\u0026rsquo;s index, and the corresponding sensitivity, specificity, and diagnostic accuracy were determined. \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eVisual analysis of brain PET/CT\u003c/p\u003e \u003cp\u003eExcellent interobserver agreement was observed regarding the visual assessment of cerebral tumors using brain PET/CT (kappa value\u0026thinsp;=\u0026thinsp;0.847). Most hemangioblastomas were PET-negative (15/16, 93.75%), while all other tumor types were PET-positive except for a single low-grade tumor. Using PET negativity as the diagnostic criterion for hemangioblastoma, the sensitivity, specificity, and accuracy were 93.75%, 98.57%, and 97.67%, respectively.\u003c/p\u003e \u003cp\u003eSemiquantitative analysis of brain PET/CT\u003c/p\u003e \u003cp\u003eOwing to the limited number of positive cases, hemangioblastoma was excluded from the semiquantitative analysis. Pairwise comparisons were conducted for the semiquantitative metabolic parameters of lymphoma, high-grade glioma, metastasis, medulloblastoma, and low-grade glioma.\u003c/p\u003e \u003cp\u003eThe specific metabolic parameters for each tumor type are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Comparisons of metabolic parameters across tumor types are illustrated in Supplemental Fig.\u0026nbsp;1. The TNR values, including TNRmax, TNRmean, and TNRpeak, were highest in lymphoma, followed by high-grade glioma, medulloblastoma, and metastasis, and were lowest in low-grade glioma. Significant differences were observed in the TNRs between lymphoma and other tumors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and between low-grade glioma and other tumors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant differences were found in these parameters among high-grade gliomas, metastatic tumors, and medulloblastomas (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eThe specific metabolic parameters for each tumor type MET: metastasis; LYM: lymphoma; LGG: low-grade glioma; HGG: high-grade glioma; MB: medulloblastoma; TNRmax: maximum tumor-to-normal ratio; TNRmean: mean tumor-to-normal ratio; TNRpeak: peak tumor-to-normal ratio;\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cp\u003eTBRmax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTBRmean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTBRpeak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMTV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.20(0.72, 1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69(0.45, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79(0.56, 1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.40(3.20, 8.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.26(16.80, 76.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.74(1.93, 3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.73(1.08, 2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.87(1.31, 2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.58(3.60, 8.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.10(48.15, 123.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62(0.51, 0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38(0.35, 0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52(0.49, 0.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.80(2.93, 7.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.80(8.73, 31.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56(0.88, 2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93(0.62, 1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15(0.68, 1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.60(1.90, 7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.50(17.10, 58.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33(1.02, 1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77(0.66, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94(0.73, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.99(10.40, 35.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e218.57(61.77, 322.00)\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\u003eMTV was highest in medulloblastoma, followed by lymphoma, high-grade glioma, metastasis, and low-grade glioma. A statistically significant difference in MTV was observed between medulloblastoma and all the other tumors (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eThe TLG was significantly greater for both medulloblastoma and lymphoma than for metastasis, high-grade glioma, and low-grade glioma (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no statistically significant difference in TLG was found between medulloblastoma and lymphoma (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Representative images of the cerebellar tumors are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDiagnostic performance of semiquantitative parameters\u003c/p\u003e \u003cp\u003eSemiquantitative analysis revealed that the TNRmax, TNRmean, and TNRpeak were useful for distinguishing lymphoma and low-grade glioma from other tumor types, while the MTV served as an effective indicator for identifying medulloblastoma.\u003c/p\u003e \u003cp\u003eRegarding the differentiation of lymphoma from other tumors, the AUCs of TNRmax, TNRmean, and TNRpeak were 0.881, 0.889, and 0.898, respectively. At optimal cutoff values greater than 1.67, 0.94, and 1.37, the corresponding diagnostic accuracies were 71.01%, 75.36%, and 85.51%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Regarding distinguishing low-grade glioma from other tumors, TNRmax, TNRmean, and TNRpeak achieved AUC values of 0.904, 0.916, and 0.869, respectively. At optimal cutoff values below 0.77, 0.52, and 0.62, the diagnostic accuracies reached 92.75%, 91.30%, and 89.86%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eDiagnostic efficacy of TNRs for cerebellar lymphoma and low-grade glioma\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOptimal cutoff value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNRmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e61.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e71.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNRmean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e75.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNRpeak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e88.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e85.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLow-grade glioma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNRmax\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e93.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e92.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNRmean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e91.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNRpeak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89.86%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eTNRmax: maximum tumor-to-normal ratio; TNRmean: mean tumor-to-normal ratio; TNRpeak: peak tumor-to-normal ratio.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor the identification of medulloblastoma, the MTV yielded an AUC of 0.847. Using a cutoff value greater than 12.85 resulted in a sensitivity of 83.33%, specificity of 90.48%, and accuracy of 89.86%.\u003c/p\u003e \u003cp\u003eDiagnostic efficacy of whole-body PET/CT for detecting brain metastasis\u003c/p\u003e \u003cp\u003eExcellent interobserver agreement was observed for the detection of these suspicious extracranial malignancies (kappa value\u0026thinsp;=\u0026thinsp;0.905). The majority of the brain metastases (24/25) were accompanied by suspicious extracranial malignancies. Among these patients, 23 had primary tumors originating from the lung (n\u0026thinsp;=\u0026thinsp;21) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), esophagus (n\u0026thinsp;=\u0026thinsp;1), colon (n\u0026thinsp;=\u0026thinsp;1), and breast (n\u0026thinsp;=\u0026thinsp;1). The remaining patient demonstrated increased FDG uptake in multiple lymph nodes throughout the body, but no definite primary tumor was found. Additionally, one patient with high-grade glioma was found to have a suspected sigmoid colon malignancy, which was pathologically confirmed as a moderately differentiated adenocarcinoma with mismatch repair deficiency on immunohistochemistry (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Another patient with lymphoma had suspicious bilateral adrenal malignancies, which were confirmed as lymphoma-involved lesions by follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Suspicious extracranial malignancy detected by torso PET/CT demonstrated high diagnostic performance for brain metastasis, with a sensitivity of 96.00%, specificity of 96.72%, and accuracy of 96.51%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT provides a comprehensive assessment of glucose metabolism in lesions throughout the body in a single examination, making it a commonly used imaging technique for preoperative differential diagnosis. However, its application for treating cerebellar tumors has been explored in only a limited number of studies with small sample sizes and a restricted spectrum of diseases. In this study, the utility of whole-body \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT for diagnosing these tumors was systematically evaluated. Our results demonstrate that visual analysis and metabolic parameters based on brain PET can effectively differentiate among cerebellar lymphoma, hemangioblastoma, low-grade glioma, and medulloblastoma. Furthermore, torso PET/CT contributed valuable information for diagnosing cerebellar metastasis.\u003c/p\u003e \u003cp\u003eHemangioblastoma is a common benign cerebellar tumor. The cystic-solid or solid types, which demonstrate marked enhancement on contrast-enhanced MRI and increased perfusion on perfusion-weighted images, can be challenging to distinguish from metastases and high-grade gliomas, particularly in the absence of flow voids [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, we observed that nearly all hemangioblastomas were PET-negative, with only a single case showing mild FDG uptake with low TNRs. These finding are consistent with the report by Takahashi et al., who reported that compared with metastases, gliomas, and lymphomas, solid hemangioblastomas have lower SUVmax values on FDG PET [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Histopathologically, hemangioblastomas are composed mainly of a dense capillary network with intervening nests or sheets of stromal cells[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This structural composition, coupled with the low proliferative activity of the stromal cells, results in the characteristically low glucose metabolism observed on PET imaging. A cerebellar tumor that demonstrates no obvious FDG uptake but is characterized by high cerebral blood volume on perfusion-weighted imaging typically suggests a diagnosis of hemangioblastoma.\u003c/p\u003e \u003cp\u003eOur semiquantitative PET analysis confirmed the utility of TNRs in diagnosing lymphoma and low-grade glioma. As previously documented, central nervous system lymphoma is characterized by high FDG avidity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and is associated with high tumor cell density and activation of glucose transporters and hexokinase II[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our findings align with these findings, showing that compared with other tumor types, cerebellar lymphomas exhibit significantly higher FDG metabolism. Among the semiquantitative indices, TBRpeak yielded the highest diagnostic accuracy (85.5%) at an optimal threshold of 1.37. Consequently, integrating homogenous enhancement on contrast-enhanced MRI with marked FDG avidity strongly supports a diagnosis of cerebellar lymphoma.\u003c/p\u003e \u003cp\u003eIn this study, cerebellar low-grade gliomas\u0026mdash;including pilocytic astrocytoma, pilomyxoid astrocytoma, and other astrocytomas\u0026mdash;demonstrated significantly lower TNRs than their malignant counterparts, a finding that is consistent with those seen in prior literature [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This low FDG avidity likely reflects their characteristically low proliferative activity. TNRmax yielded the highest diagnostic accuracy (92.75%), when an optimal cutoff of \u0026lt;\u0026thinsp;0.77 was used. Notably, one patient with a Grade II astrocytoma exhibited markedly high metabolic activity. Unfortunately, this individual subsequently experienced tumor recurrence and mortality within one year after surgery. This aggressive clinical course suggests the possible presence of a high-grade glioma component that was not sampled or identified during the initial pathological examination. Consequently, FDG PET may serve as a complementary tool for histopathology, offering critical prognostic guidance.\u003c/p\u003e \u003cp\u003eCerebellar medulloblastoma typically appears as a solid mass with marked enhancement on contrast-enhanced MRI and restricted diffusion on diffusion-weighted imaging, mimicking lymphoma or high-grade glioma. Previous studies, largely based on case reports, have indicated that patients with medulloblastoma exhibit increased glucose and amino acid metabolism, along with upregulated expression of the octreotide receptor [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, no reliable metabolic parameter has been established to effectively differentiate it from glioblastoma or metastasis. A key novel finding in this study is that medulloblastoma presented significantly higher MTV, which effectively aids in differentiation. When an optimal cutoff of \u0026ge;\u0026thinsp;12.85 was used, MTV achieved diagnostic sensitivity, specificity, and accuracy of 83.3%, 90.6%, and 90.0%, respectively. The high MTV is likely attributed to the predominantly solid and often multifocal nature of the tumor. While medulloblastoma and lymphoma both show restricted diffusion on DWI, medulloblastoma can be distinguished by its characteristically higher MTVs and lower TNRs, offering a practical diagnostic solution.\u003c/p\u003e \u003cp\u003eIn this investigation, semi-quantitative PET parameters demonstrated limited utility in differentiating cerebellar metastases from high-grade gliomas. However, the identification of suspicious extracranial malignancies on torso PET/CT provided crucial diagnostic clues for cerebellar metastases. The detection rate for primary tumors among cerebellar metastases reached 94.7%, with pulmonary origins predominating and less frequent occurrences in the esophagus, colon, and breast. Previous studies have reported FDG PET/CT detection rates for primary tumors in patients with brain metastasis ranging from 74% to 77% [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The higher detection rate observed in our series may be attributable to its relatively small sample size. Notably, the majority of nonmetastatic cases exhibited no suspicious extracranial findings on whole-body PET/CT. One exception was a lymphoma patient who demonstrated bilateral adrenal thickening with intensified FDG metabolism, which was subsequently confirmed as having lymphomatous involvement\u0026mdash;which is consistent with known patterns of extranodal lymphoma involvement [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In another case, a high-grade glioma was preoperatively misdiagnosed as metastasis due to the detection of a suspicious malignancy with focal FDG avidity on the sigmoid on torso PET/CT. Subsequent pathology confirmed a moderately differentiated adenocarcinoma with mismatch repair deficiency on immunohistochemistry. The coexistence of colorectal adenocarcinoma and glioma is rare and often indictive of Lynch syndrome [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], a possibility further supported by immunohistochemical findings in this patient. Therefore, the presence of suspicious malignant lesions on whole-body PET/CT should raise a strong suspicion that cerebellar lesions represent metastases. However, when cerebellar and adrenal masses with elevated FDG metabolism are concurrently detected, lymphoma should be considered in the differential diagnosis, and biopsy is preferred over surgical resection for pathological confirmation. For patients with a suspected colonic malignancy coexisting with a cerebellar space-occupying lesion, glioma remains a diagnostic consideration, particularly when the colon lesion shows evidence of mismatch repair deficiency. In such cases, surgical resection is the preferred initial management for intracranial tumors.\u003c/p\u003e \u003cp\u003eThis study has two limitations. First, the sample size was relatively small, particularly for patients diagnosed with glioma and medulloblastoma, which may have reduced the statistical power of our analyses. Nevertheless, the cohort in this study is relatively larger than those reported in previous PET studies focusing on cerebellar tumors. Future large-scale, multicenter studies are required to validate these preliminary findings. Another limitation is that the diagnostic performance of \u003csup\u003e18\u003c/sup\u003eF-FDG was assessed as a standalone tracer. In current neuro-oncology practice, amino acid PET tracers such as \u003csup\u003e11\u003c/sup\u003eC-MET are increasingly being used for differential diagnosis and grading of brain tumor. Despite its reported limited value in discriminating benign from malignant cerebellar tumors because of high uptake in lesions such as hemangioblastomas and pilocytic astrocytomas[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], amino acid PET provides a characteristically elevated tumor-to-background ratio that is valuable for delineating tumor margins. Future head-to-head comparative studies are warranted to further evaluate the diagnostic value of FDG versus amino acid PET in differentiating cerebellar tumors, as well as their potential synergistic use.\u003c/p\u003e \u003cp\u003eIn summary, FDG PET metabolic parameters serve specific diagnostic roles: the TNR aids in distinguishing lymphoma and low-grade glioma, while elevated MTV and TLG values assist in identifying medulloblastoma. Additionally, when torso PET/CT reveals suspicious extracranial malignancies, it enables a relatively accurate diagnosis of cerebellar metastases. Consequently, whole-body FDG PET/CT is an effective imaging modality for the differential diagnosis of cerebellar tumors and provides valuable support for clinical decision-making in treatment planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for conducting this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Animal and Human Ethics Committee of Beijing Tiantan Hospital, Capital Medical University, and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients or their legal guardians.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShu Zhang: Conceptualization, study design, data collection, statistical analysis, and writing\u0026mdash;original draft. Leilei Yuan and Qian Chen: Data analysis and interpretation. Lin Ai: Project supervision and critical revision of the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOstrom QT, Patil N, Cioffi G et al (2020) CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013\u0026ndash;2017. 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AJNR Am J Neuroradiol 35:1009\u0026ndash;1015\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebreczeni-Mate Z, Freihat O, Toro I et al (2024) Value of 11C-Methionine PET Imaging in High-Grade Gliomas: A Narrative Review. Cancers (Basel) 16:3200\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim W, Acker G, Hardt J et al (2022) Dynamic (18)F-FET PET/CT to differentiate recurrent primary brain tumor and brain metastases from radiation necrosis after single-session robotic radiosurgery. Cancer Treat Res Commun 32:100583\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEkici S, Nye JA, Neill SG et al (2022) Glutamine Imaging: A New Avenue for Glioma Management. AJNR Am J Neuroradiol 43:11\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZinnhardt B, Muther M, Roll W et al (2020) TSPO imaging-guided characterization of the immunosuppressive myeloid tumor microenvironment in patients with malignant glioma. Neuro Oncol 22:1030\u0026ndash;1043\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Wang J, Wang K et al (2022) Differentiation of high-grade glioma and primary central nervous system lymphoma: Multiparametric imaging of the enhancing tumor and peritumoral regions based on hybrid (18)F-FDG PET/MRI. Eur J Radiol 150:110235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNorikane T, Mitamura K, Yamamoto Y et al (2024) Comparative evaluation of (11)C-methionine and (18)F-fluorodeoxyglucose positron emission tomography for distinguishing between primary central nervous system lymphoma and isocitrate dehydrogenase-wildtype glioblastoma. J Neurooncol 166:195\u0026ndash;201\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuharic M, Jankovic D, Splavski B et al (2018) Hemangioblastomas of the Posterior Cranial Fossa in Adults: Demographics, Clinical, Morphologic, Pathologic, Surgical Features, and Outcomes. Syst Rev World Neurosurg 110:e1049\u0026ndash;e1062\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim EH, Moon JH, Kang SG et al (2020) Diagnostic challenges of posterior fossa hemangioblastomas: Refining current radiological classification scheme. Sci Rep 10:6267\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi Y, Nishio A, Yamamoto D et al (2018) Usefulness of 18F-fluorodeoxyglucose-Positron Emission Tomography in Comparison with Methionine-Positron Emission Tomography in Differentiating Solid Hemangioblastoma from Adult Cerebellar Tumors. World Neurosurg 110:e648\u0026ndash;e652\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoda RA, Cimino PJ (2022) Neuropathologic features of central nervous system hemangioblastoma. J Pathol Transl Med 56:115\u0026ndash;125\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTateishi K, Miyake Y, Kawazu M et al (2020) A Hyperactive RelA/p65-Hexokinase 2 Signaling Axis Drives Primary Central Nervous System Lymphoma. Cancer Res 80:5330\u0026ndash;5343\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhandani AH, Dunphy CH, Meteesatien P et al (2009) Glut1 and Glut3 expression in lymphoma and their association with tumor intensity on 18F-fluorodeoxyglucose positron emission tomography. Nucl Med Commun 30:594\u0026ndash;601\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamauchi M, Okada T, Okada T et al (2017) Differential diagnosis of posterior fossa brain tumors: Multiple discriminant analysis of Tl-SPECT and FDG-PET. Med (Baltim) 96:e7767\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua T, Chen M, Fu P et al (2024) Heterogeneity of fibroblast activation protein expression in the microenvironment of an intracranial tumor cohort: head-to-head comparison of gallium-68 FAP inhibitor-04 ((68)Ga-FAPi-04) and fluoride-18 fluoroethyl-L-tyrosine ((18)F-FET) in positron emission tomography-computed tomography imaging. Quant Imaging Med Surg 14:4450\u0026ndash;4463\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArunRaj ST, Kumar A, Kp H et al (2021) Metastatic Medulloblastoma: 18F-FDG and 68Ga-DOTANOC PET/CT in Response Evaluation. Clin Nucl Med 46:e262\u0026ndash;e263\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZukotynski K, Fahey F, Kocak M et al (2014) 18F-FDG PET and MR imaging associations across a spectrum of pediatric brain tumors: a report from the pediatric brain tumor consortium. J Nucl Med 55:1473\u0026ndash;1480\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoc ZP, Kara PO, Dagtekin A (2018) Detection of unknown primary tumor in patients presented with brain metastasis by F-18 fluorodeoxyglucose positron emission tomography/computed tomography. CNS Oncol 7:CNS12\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen H, Wei Z, Zhou D et al (2018) Primary extra-nodal diffuse large B-cell lymphoma: A prognostic analysis of 141 patients. Oncol Lett 16:1602\u0026ndash;1614\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTogawa A, Ueno M, Yamaoka M et al (2025) Glioblastoma Arising in Lynch-like Syndrome after Repeated Development of Colorectal Cancers. Intern Med 64:1189\u0026ndash;1193\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBinder ZA, Johnson MW, Joshi A et al (2011) Glioblastoma multiforme in the Muir-Torre syndrome. Clin Neurol Neurosurg 113:411\u0026ndash;415\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeuriat PA, Flaus A, Portefaix A et al (2024) Preoperative 11 C-Methionine PET-MRI in Pediatric Infratentorial Tumors. Clin Nucl Med 49:381\u0026ndash;386\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":"Cerebellar neoplasms, 18F-FDG, PET, differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8759459/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8759459/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eThe present study aims to evaluate the value of whole-body \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in distinguishing among different cerebellar tumor types.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT images of 86 patients with histologically confirmed cerebellar tumors, including 25 metastases, 17 lymphomas, 9 low-grade gliomas, 13 high-grade gliomas, 16 hemangioblastomas, and 6 medulloblastomas. Tumors were initially classified as PET-positive or PET-negative by visual assessment. For PET-positive cases, semiquantitative parameters\u0026mdash;including the maximum, mean, and peak tumor-to-normal-brain ratios (TNRmax, TNRmean, and TNRpeak, respectively), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)\u0026mdash;were measured and compared pairwise. Parameters significant for differential diagnosis were evaluated using the area under the receiver operating characteristic curve (AUC) and accuracy. Additionally, the detection of suspicious extracranial malignancy on torso PET/CT was recorded, and its diagnostic value for metastasis was assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNearly all hemangioblastomas were PET-negative, with visual assessment achieving a diagnostic accuracy of 97.67% for this tumor type. Lymphomas presented the highest TNRmax, TNRmean, and TNRpeak values, whereas low-grade gliomas presented the lowest values. For distinguishing lymphoma, the AUCs for TNRmax, TNRmean, and TNRpeak were 0.881, 0.889, and 0.898, respectively. When optimal cutoff values of \u0026gt;\u0026thinsp;1.67, \u0026gt;\u0026thinsp;0.94, and \u0026gt;\u0026thinsp;1.37 were used, the diagnostic accuracies were 71.01%, 75.36%, and 85.51%, respectively. For identifying low-grade glioma, the same parameters yielded AUCs of 0.904, 0.916, and 0.869, respectively. With optimal cutoff values of \u0026lt;\u0026thinsp;0.77, \u0026lt;\u0026thinsp;0.52, and \u0026lt;\u0026thinsp;0.62, the accuracies were 92.75%, 91.30%, and 89.86%, respectively. Medulloblastoma demonstrated the highest MTV and TLG. MTV yielded an AUC of 0.847 for differentiating medulloblastoma from other cerebellar tumors, and an optimal cutoff value of \u0026gt;\u0026thinsp;12.85 provided an accuracy of 89.86%. Torso PET/CT for detecting suspicious extracranial malignancy excelled at diagnosing metastasis, with an accuracy of 96.51%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eVisual analysis and metabolic parameters from brain \u003csup\u003e18\u003c/sup\u003eF-FDG PET are valuable for differentiating hemangioblastoma, lymphoma, low-grade glioma, and medulloblastoma. Whole-body PET/CT contributes to the diagnosis of metastasis by identifying suspicious extracranial malignancies.\u003c/p\u003e","manuscriptTitle":"Usefulness of whole-body 18F-FDG PET/CT in the presurgical discrimination of cerebellar tumors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 14:01:05","doi":"10.21203/rs.3.rs-8759459/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":"91c5e403-2043-4cb0-9f93-0d135b1ea2b8","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T14:01:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 14:01:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8759459","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8759459","identity":"rs-8759459","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}