Fibroblast activation protein expression in tumour microenvironment is crucial in differentiation and survival prediction of recurrent gliomas: a head-to-head comparison of 68Ga-FAPI-04 and 18F-FET in PET/CT imaging

Fibroblast activation protein expression in tumour microenvironment is crucial in differentiation and survival prediction of recurrent gliomas: a head-to-head comparison of 68Ga-FAPI-04 and 18F-FET in PET/CT imaging | 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 Fibroblast activation protein expression in tumour microenvironment is crucial in differentiation and survival prediction of recurrent gliomas: a head-to-head comparison of 68Ga-FAPI-04 and 18F-FET in PET/CT imaging Tao Hua, Qi Huang, Weiyan Zhou, Jianbo Wen, Fang Xie, ming li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6678369/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Aug, 2025 Read the published version in EJNMMI Radiopharmacy and Chemistry → Version 1 posted 5 You are reading this latest preprint version Abstract Background —The accurate differentiation of recurrent glioma from treatment-related changes, such as pseudoprogression or radiation necrosis, is crucial for treatment planning and remains a critical challenge. Fibroblast activation protein (FAP) expressed by cancer-associated fibroblasts can be targeted with PET tracers for in vivo visualization and quantification. This research aims to evaluate the diagnostic and survival predictive efficacy of FAP expression in possible recurrent glioma patients with a head-to-head comparison of [gallium-68] FAP inhibitor-04 and [fluoride-18] fluoroethyl-L-tyrosine PET/CT imaging. 30 post-treatment glioma patients with possible recurrent signs under regular MRI follow-up were enrolled. PET-based semiquantitative parameters and clinical factors were obtained for analysis. Results —Univariate logistic regression indicated the initial pathological diagnosis has a borderline differential efficacy (P=0.053). In Multivariate logistic regression analysis of PET-based semiquantitative parameters, MTV FAPI :MTV FET ratio showed borderline differential efficacy (P=0.094). When including PET parameters and initial pathological diagnosis, the effectiveness of initial pathological diagnosis was significant (P = 0.045), and the MTV FAPI :MTV FET ratio enhanced the area under the receiver operating characteristic curve (AUC) (P=0.040). When the initial diagnosis was replaced with the WHO grade, both the MTV FAPI :MTV FET ratio (P=0.081) and the WHO grade (P=0.086) showed borderline efficacy, while the MTV FAPI :MTV FET ratio improved the AUC (P=0.016). After factoring in age and gender, the initial pathological diagnosis remained significant (P=0.038). Three parameters, including gender (P=0.057), MTV FAPI :MTV FET ratio (P=0.076), and MTV-FAPI (P=0.093), demonstrated borderline efficacy, with the MTV FAPI :MTV FET ratio enhancing the AUC (P=0.039). Similarly, after replacing the initial pathological diagnosis with the initial WHO grade, the initial WHO grade (P=0.072), MTV FAPI :MTV FET ratio (P=0.079), and gender (P=0.089) presented with borderline differential efficacy, and the MTV FAPI :MTV FET ratio similarly significantly enhanced the AUC of the model (P=0.046). The survival analysis indicated that MTV-FAPI significantly affects the overall survival (P=0.027, hazard ratio=1.103, 95% CI: 1.011-1.204). Conclusions —This head-to-head study illustrated FAP expression volume percentage of the post-treatment glioma patients has potential in the differentiation between glioma recurrence and treatment-related changes. Glioma FAP expression volume is an independent risk factor that could significantly influence the overall survival of this glioma cohort. glioma recurrence fibroblast activation protein positron emission tomography fibroblast activation protein inhibitor differentiation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Gliomas are the most frequent central nervous system malignancy. Standard treatment strategies, including maximum surgical resection followed by radiotherapy and chemotherapy, have been applied for decades. However, the substantial tumoural heterogeneity of gliomas leads to inevitable tumour recurrence of most gliomas [ 1 , 2 ]. Accurate diagnosis of glioma recurrence is essential for optimizing patient survival and quality of life, as timely detection enables tailored therapeutic strategies to enhance survival rates and prevent unnecessary treatments. Unfortunately, challenges persist due to the similar imaging manifestations between tumour recurrence and treatment-related changes such as pseudoprogression or radiation necrosis [ 3 ]. Radio-labelled amino acid tracers such as [fluoride-18] fluoroethyl-L-tyrosine ( 18 F-FET) could target the overexpressed L-type amino acid transporters in glioma cells and contribute to the differential diagnosis, prognostication, treatment strategy planning, and treatment effects monitoring [ 4 – 7 ]. With the satisfactory diagnostic efficacy of both non-contrast and contrast gliomas, the Response Assessment in Neuro-Oncology (RANO) working group recommends amino acid tracers PET imaging as a valuable complement to magnetic resonance image (MRI) in all stages of glioma management [ 8 , 9 ]. Despite the satisfactory performance of radio-labelled amino acid tracers, the inflammation tissues around the treatment area could also present with abnormal amino acid tracer uptake to some degree and impose uncertainty of recurrent glioma diagnosis. Benign cells in the tumour microenvironment (TME) complicate glioma heterogeneity. The interactions between glioma cells and adjacent cells in tumour stroma can promote tumour proliferation, migration, angiogenesis, and recurrence through various mechanisms [ 10 , 11 ]. Cancer-associated fibroblasts (CAFs), the components of TME, are actively involved in the crosstalk between tumour cells and stromal cells via the secretion of growth factors and inflammatory cytokines. CAFs may overexpress a transmembrane glycoprotein known as fibroblast activation protein (FAP) in the tumour microenvironment [ 12 , 13 ]. Radio-labelled fibroblast activation protein inhibitors (FAPI) have demonstrated efficacy in imaging FAP overexpression across a range of solid tumours with satisfactory results [ 14 – 16 ]. As there is significant FAP accumulation in the stroma of malignant tumours and satisfactory tissue contrast, FAP-targeted imaging has efficacy in malignant tumour detection, tumour delineation, and radiotherapy planning [ 17 – 20 ]. Our previous studies explored the overexpression patterns of FAP in an untreated intracranial tumour cohort, including a series of glioma subtypes, medulloblastoma, and brain metastasis. The results demonstrated that more malignant intracranial tumours, including brain metastasis, glioblastoma, and medulloblastoma, presented with more FAP overexpression [ 21 ]. Besides, the ratio of MTV FAPI to MTV FET (MTV FAPI :MTV FET ratio), an index that could describe the interaction of glioma and cancer-associated fibroblasts in the tumour microenvironment, has shown the potential to enhance differential efficacy in untreated intracranial tumours. Based on these findings, whether the FAP overexpression could benefit the differentiation of glioma recurrence from pseudoprogression or radiation necrosis is of clinical significance. This prospective, head-to-head study applied [gallium-68] FAP inhibitor-04 ( 68 Ga-FAPI-04) and 18 F-FET PET/CT imaging to post-treatment glioma patients with tumour recurrence signs under regular MRI follow-up for the investigation of 68 Ga-FAPI-04 efficacy. A quantification analysis of PET-based semiquantitative parameters and important clinical factors was employed to explore the relationship between tumour and CAFs interactions and glioma recurrence. Methods Study design Structural MRI follow-up and neurosurgical specialist consultation of post-treatment glioma patients were regularly applied in the outpatient department of Huashan Hospital, Fudan University. The patients were enrolled for further investigation until the suspected recurrent signs were shown. Enrolled post-treatment glioma patients received 68 Ga-FAPI-04 and 18 F-FET PET/CT brain imaging from October 2022 to May 2024 in the Nuclear Medicine & PET Center of Huashan Hospital, Fudan University. PET-based semiquantitative imaging parameters and clinical information were obtained to evaluate this head-to-head study. Written informed consent from patients was obtained. PET/CT i maging protocols 68 Ga-FAPI-04 and 18 F-FET tracers were synthesized in the Department of Nuclear Medicine & PET Center of Huashan Hospital, Fudan University. The two radio-labelled tracers PET scans were applied with at least a 24-hour interval for each patient in our cohort. For 18 F-FET PET/CT imaging, patients fasted for at least 4 hours before imaging. A 20-minute static scan was conducted in 3-dimensional mode with a Biograph mCT Flow Edge 128 PET/CT system (Siemens Healthineers, Erlangen, Germany) 20 minutes after intravenous bolus injection of 18 F-FET (182 ± 17.5 MBq). Attenuation correction was performed using low-dose CT (tube current = 150 mAs, voltage = 120 kV, acquisition = 64 × 0.6 mm, convolution kernel = H30s, slice thickness = 5 mm, interslice gap = 1.5 mm) before the emission scan. Post-acquisition, PET images were reconstructed using the ordered subset expectation maximization (OSEM) algorithm with a Gaussian filter and a full width at half maximum of 3.5 mm at the center of the field of view. In 68 Ga-FAPI-04 PET/CT imaging, 30 minutes after intravenous bolus injection of 68 Ga-FAPI-04 (176 ± 19.2 MBq), a 30-minute static scan was conducted in 3D mode with a uMI510 PET/CT (United Imaging, Shanghai, China). Attenuation correction was similarly performed using low-dose CT before the emission scan. PET images were also reconstructed using the OSEM algorithm with a Gaussian filter and the same full width at half maximum after acquisition. PET/CT i mage analysis PET/CT images were analyzed with a syngo.via workstation (Siemens Healthineers). Two experienced nuclear medicine physicians (WXZ and TH, with over 6 and 13 years of experience, respectively) performed blinded 68 Ga-FAPI-04 and 18 F-FET PET/CT positive lesion judgment and lesion delineation before surgical treatment. Structural MRI was initially reviewed for lesion location before PET/CT lesion delineation. For 18 F-FET PET/CT imaging, the mean standardized uptake value (SUVmean) of the brain background was measured in a crescent-shaped area, encompassing gray and white matter on the lesion’s contralateral hemisphere [22]. Subsequently, 1.6 times of background SUVmean was used for lesion delineation, and the maximal standardized uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion tracer uptake (TLU) were obtained. The maximal tumour-to-brain ratio (TBRmax) was calculated by dividing the intracranial lesion SUVmax with the background SUVmean. For 68 Ga-FAPI-04 imaging, the background SUVmean was measured similarly to that of 18 F-FET PET. Lesion SUVmax and TBRmax were measured and calculated. Due to the lack of guidelines for a FAPI-positive lesion delineation and the experience of our previous investigation, we applied a 20% isocontour volumetric threshold of lesion SUVmax for the delineation and measurement of lesion MTV FAP I and TLU FAP I [21]. For the quantification description of FAP expression in the glioma microenvironment, a series of semi-quantitative parameters, including MTV FAPI :MTV FET ratio and TLU FAPI :TLU FET ratio, were obtained for further analysis. Owing to the concerns that too small lesions could not fully describe the interactions between FAP expressed in glioma TME, those patients with MTV-FET less than 1 cm 3 were excluded from the analysis. Diagnosis protocol For patients who received the following surgical treatment, including surgical resection or stereotactic surgical biopsy, in the Neurosurgery department of Huashan Hospital, Fudan University, histopathological diagnosis results were obtained via our facility's medical record system. For patients who received treatment strategies, including radiosurgery, radiotherapy, chemotherapy, or other therapies, expert specialists’ consultation opinions were collected as authorized diagnoses. Regular follow-ups were applied and the overall survival for this patient cohort was collected. Statistical analyses Descriptive statistics are expressed as the mean and standard deviation or median and range. The t- test and one-way analysis of variance were used to compare continuous variables. The Wilcoxon signed rank or Kruskal-Wallis test was performed if a normal distribution of variables was not met. Linear regression analysis investigated the relationship between pathological diagnosis and PET parameters. The variance inflation factor was used to control multicollinearity. Logistic regression was used to explore the diagnostic efficacy of the demographic and PET parameters. The area under the receiver operating curve (AUC) was used to observe the diagnostic efficacy. Cox regression and the Kaplan-Meier method were applied to observe the relationship between the overall survival (OS) and the parameters. Intraclass correlation coefficients (ICCs) for PET parameter measurements were assessed, and the results were classified as poor (less than 0.2), fair (0.21–0.4), moderate (0.41–0.6), good (0.61–0.8), and very good (0.8–1.0). All statistical analyses were performed with Stata version 17 (College Station, TX, USA). In all analyses, P < 0.05 indicated a statistically significant difference. Results Patient cohort characteristics 30 adult post-treatment glioma patients were enrolled for research, including 20 males and 10 females. The cohort's median age was 51 years (range 17-67 years). The illustrative workflow diagram of the inclusion and exclusion criteria can be seen in Fig. 1. The initial diagnosis of this patient cohort included juvenile low-grade glioma, oligodendroglioma, astrocytoma, anaplastic oligodendroglioma, anaplastic astrocytoma, glioblastoma multiforme, and diffuse intrinsic pontine glioma. There were 1, 3, 5, and 21 patients from grades 1 through 4 in the WHO grade stratification. The follow-up results showed that 21 patients were diagnosed with glioma recurrence, and 9 patients were diagnosed with treatment-related changes, including radiation necrosis and pseudoprogression. Demographic details of the cohort are provided in Table 1. PET-based lesions semiquantitative parameters comparison of 68 Ga-FAPI-04 and 18 F-FET imaging ICCs showed very good agreement between the different lesions for 18 F-FET and 68 Ga-FAPI-04 PET/CT semiquantitative parameter measurements (ICC>0.96, P0.91, P <0.001), and the results of reader one (TH) were used for analysis. For 68 Ga-FAPI-04 PET images, the median of lesion SUVmax was 2.79 (range 0.77, 13.18), and the median of lesion TBRmax was 60.15 (range 15.40, 270.80). The median of lesion MTV was 10.32 (range 2.33, 46.02), and the median of lesion TLU was 9.70 (range 2.01, 71.38). While for 18 F–FET PET imaging, the median of lesion SUVmax was 3.11 (range 1.70, 5.02), and the median of lesion TBRmax was 3.51 (range 2.32, 5.88). The median of lesion MTV was 20.93 (range 3.27, 226.46), and the median of lesion TLU was 37.32 (range 5.53, 362.34), respectively. The details of the semi-quantitative parameters originating from the two PET tracers imaging were provided in supplementary material. The illustrative comparison of lesion semiquantitative parameters from two PET tracers was provided in Fig. 2. Diagnostic efficacy analysis of PET-based semiquantitative parameters and clinical features Univariate logistic regression was initially applied to all the semiquantitative parameters and clinical features; only the initial pathological diagnosis demonstrated borderline significant diagnostic efficacy (P = 0.053). The supplementary material provided details of the univariate logistic regression statistical results. Afterwards, multivariate logistic regression was utilized to build the diagnostic model. The investigation took three steps: first, the efficacy of PET-based semiquantitative parameters was assessed; then, the initial pathological diagnosis or initial WHO grade was included in the observation model; and finally, age and gender were incorporated into the model for evaluation. In the PET-based semiquantitative logistic regression analysis that included TBRmax-FET, TBRmax-FAPI, MTV-FET, and MTV-FAPI, no statistical significance was found for these semiquantitative parameters, and the AUC of the four-parameter model was 0.598 (0.385-0.811, 95% confidence interval, CI). We further explored the efficacy of the MTV FAPI :MTV FET ratio as the interaction index between the tumour and the TME. After introducing this parameter into the current semiquantitative parameters model, the logistic regression results indicated that the MTV FAPI :MTV FET ratio showed borderline diagnostic significance, with a P value of 0.094, and the AUC of the current model improved to 0.767 (95%CI: 0.593-0.942). The DeLong test results indicated no significant difference between the AUCs of these two models (P = 0.126). Results details are provided in the supplementary material. Then, the pathological information of the patient cohort was incorporated into our investigation. In addition to TBRmax-FET, TBRmax-FAPI, MTV-FET, and MTV-FAPI, we included the initial pathological diagnosis in the model. The regression results indicated that the initial pathological diagnosis has significant diagnostic efficacy, with a P-value of 0.033 and an AUC of 0.709 (95% CI: 0.465-0.953). Next, we evaluated the effectiveness of the MTV FAPI :MTV FET ratio in this diagnostic model. The regression results showed that the initial pathological diagnosis continued to demonstrate statistical significance, with a P-value of 0.045. The AUC for the model at this stage increased to 0.847 (95% CI: 0.689-1.000). The DeLong test results indicated a significant difference between the two models, with a P-value of 0.040. The supplementary material provided multivariate logistic regression results details, and Fig. 3A compares the AUC with and without the MTV FAPI :MTV FET ratio in the current stage. As planned, we incorporated the cohort's age and gender status into our analysis. In the logistic regression model that included TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, initial pathological diagnosis, age, and gender, the results showed that the initial pathological diagnosis significantly differentiates recurrent glioma in our cohort, with a P-value of 0.044. The current AUC for the diagnostic model was 0.841 (95% CI: 0.677-1.000). Similarly, we focused on the MTV FAPI :MTV FET ratio for our observations. The analysis revealed that one parameter had significant efficacy, while three parameters showed borderline diagnostic efficacy in differentiating recurrent gliomas. These parameters included the initial pathological diagnosis (P = 0.038), gender (P = 0.057), MTV FAPI :MTV FET ratio (P = 0.076), and MTV-FAPI (P = 0.093). The current AUC for this model was 0.963 (95% CI: 0.887-1.000). The DeLong test indicated a significant difference between the two models, with a P-value of 0.039. The details of the results are provided in the supplementary material, and the comparison of AUC with and without the MTV FAPI :MTV FET ratio can be seen in Fig. 3B. Due to discrepancies between the pathological diagnosis and the WHO grade, we replaced the initial pathological diagnosis with the initial WHO grade for our investigation. In the diagnostic model that included TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, and the initial WHO grade, we found no statistically significant results, with the model's AUC at 0.640 (95% CI: 0.400-0.880). We subsequently introduced the MTV FAPI :MTV FET ratio into the model. The regression results indicated that two parameters approached significance, the MTV FAPI :MTV FET ratio (P = 0.081) and the initial WHO grade (P = 0.086). With this addition, the AUC improved to 0.852 (95% CI: 0.715-0.988). The DeLong test results demonstrated a significant difference between the two models (P = 0.016). The multivariate logistic regression results are available in the supplementary material, and the AUC comparison with and without the MTV FAPI :MTV FET ratio can be viewed in Fig. 3C. We then introduced age and gender status with the initial WHO grade for our exploration. In the regression model with TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, initial WHO grade, age, and gender, we found no statistical significance. The model had an AUC of 0.762 (95% CI: 0.532-0.992). Then the effectiveness of the MTV FAPI :MTV FET ratio within this model was assessed. Upon introduction of this ratio, three parameters displayed borderline diagnostic efficacy, including initial WHO grade (P = 0.072), MTV FAPI :MTV FET ratio (P = 0.079), and gender (P = 0.089). The AUC of this revised model was 0.942 (0.850-1.000, 95% CI). Results from the DeLong test indicated a significant difference between these two models (P = 0.046). The supplementary material provided multivariate logistic regression results details, and the AUC comparison with or without MTV FAPI :MTV FET ratio can be seen in Fig. 3D. The univariate and multivariate logistic regression results of the cohort were provided in Table 2. The variance inflation factor control excluded multicollinearity in the above-mentioned investigations, and results were provided in the supplementary material. Survival analysis results of patient-based PET semi-quantitative parameters In the survival analysis of parameters including TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, MTV FAPI :MTV FET ratio, initial WHO grade, age and gender, Cox regression results indicated that MTV-FAPI has statistical significance (P = 0.027, hazard ratio = 1.103, 1.011-1.204, 95% CI). After Cox regression analysis, the proportional hazards assumption test was applied, and the P value was 0.875, indicating the results do not violate the proportional hazards assumption. The detailed results of Cox regression were provided in the supplementary material. The visualization of the relationship between MTV-FAPI and cohort overall survival can be seen in Fig. 4A and the Kaplan-Meier plot of MTV-FAPI after Cox regression in this cohort can be seen in Fig. 4B. Typical illustrative cases and related clinical information and parameters are shown in Fig. 5. Discussion Based on the distinct FAP expression patterns across a wide range of carcinomas and our experience in the untreated intracranial tumour cohort, we further explore the diagnostic efficacy of 68 Ga-FAPI-04 PET imaging in this post-treatment glioma cohort. Our study found that in the logistic regression analysis of PET-based semiquantitative parameters, the MTV FAPI :MTV FET ratio showed borderline diagnostic efficacy (P = 0.094). Additionally, when assessing PET-based semiquantitative parameters alongside the initial pathological diagnosis, this initial diagnosis demonstrated statistical significance (P = 0.045). Furthermore, incorporating the MTV FAPI :MTV FET ratio significantly improved the model's AUC from 0.709 to 0.847 (P = 0.040). In the analysis of PET-based semiquantitative parameters, initial pathological diagnosis, age, and gender status, the initial pathological diagnosis was found to have a statistically significant diagnostic effect (P = 0.038). Additionally, three parameters showed borderline diagnostic significance, including gender (P = 0.057), MTV FAPI :MTV FET ratio (P = 0.076), and MTV-FAPI (P = 0.093). Notably, the MTV FAPI :MTV FET ratio significantly improved the model’s AUC from 0.841 to 0.963 (P = 0.039). When we replaced the initial pathological diagnosis with the initial WHO grade for the cohort, the regression results revealed that two parameters exhibited borderline diagnostic efficacy, including the MTV FAPI :MTV FET ratio (P = 0.081) and initial WHO grade (P = 0.086). The MTV FAPI :MTV FET ratio significantly elevated the model AUC from 0.640 to 0.852 (P = 0.016). In the analysis of PET-based semiquantitative parameters, initial WHO grade, age, and gender status, results identified three parameters with borderline diagnostic efficacy, including initial WHO grade (P = 0.072), MTV FAPI :MTV FET ratio (P = 0.079), and gender (P = 0.089). Similarly, the MTV FAPI :MTV FET ratio also significantly enhanced the model AUC from 0.762 to 0.942 (P = 0.046). Survival analysis results showed that MTV-FAPI is an independent risk factor for overall survival in the cohort (P = 0.027, hazard ratio = 1.103, 95% CI: 1.011–1.204). This implies that for each additional cubic centimeter increase in the lesion 68 Ga-FAPI-04 metabolic volume in this post-treatment glioma cohort, the risk of death increases by a factor of 1.103. The interactions of genetic background and tumour microenvironment lead to the progression of gliomas [ 4 , 23 ]. FAP expression status in glioma patients could be used to evaluate the crosstalk of glioma cells and cancer-associated fibroblasts. TME cell-targeting tracers can offer various perspectives in assessing the heterogeneities of gliomas in recurrent gliomas besides classical glioma cell-targeted imaging. With the consideration of the notorious intra- and inter-tumoural heterogeneity, our head-to-head investigations from both glioma parenchyma cells and TME cells perspectives explored the relationship between glioma recurrence and FAP overexpression patterns. The potential that MTV FAPi :MTV FET ratio could benefit glioma recurrence differentiation indicated that FAP overexpression is actively involved in the microenvironment during the glioma recurrent process, and as a result, the metabolic volume of FAP overexpression in glioma TME significantly influences the survival of post-treatment glioma patients. These findings will contribute to a better understanding of CAFs effects in recurrent gliomas. Our study expanded the scope of FAP overexpression patterns in post-treatment glioma patients for more accurate differentiation of tumour recurrence. The malignant prognosis of most glioma patients has raised a huge challenge for treatment, especially for glioblastoma patients. The investigation of FAP expression patterns in our cohort could provide valuable evidence for future FAP-targeted theragnostic procedures as complements to classical treatment strategies. Lesion delineation is vital for treatment planning. Radio-labelled amino acid tracer imaging could well outline lesions with the guidelines' recommendations. However, no recommendation for brain lesion delineation in FAP-targeted imaging is available yet. In our previous FAP-targeting imaging research, we explored lesion delineation with the consideration of FAPI imaging characteristics in pre-treatment intracranial tumour cohort; different percentages of lesion SUVmax were used as thresholds to control the influence of delineation fluctuation owing to the extremely low uptake of the normal brain background. In the current research, we applied the same 20% lesion SUVmax as an isocontour volumetric threshold for lesion delineation. More evidence-based suggestions should be necessary for a more accurate delineation of solid tumours on FAPI PET imaging. Structural MRI is the most widely used imaging modality in glioma diagnosis. Regular MRI follow-up could locate the newly developed abnormal enhanced lesion and provide alert signs for possible glioma recurrence. Considering the complexities of post-treatment gliomas, MRI RANO 2.0 guidelines contribute to better calibrating recurrent glioma patients [ 24 ]. As an efficient complement to MRI imaging, PET RANO 1.0-based amino acid tracer PET imaging could contribute to better recognizing and evaluating post-treatment glioma patients [ 11 ]. Further research should be applied to differentiate glioma recurrence from treatment-associated changes under these guidelines and the latest WHO glioma classification [ 25 ]. Besides those, the TME cells-targeting tracer PET investigations will definitely provide additional and valuable evidence for both diagnosis and possible intervention direction. Researchers from other groups reported perfect diagnostic efficacy of FAPI PET for recurrent glioma [ 26 ], while in our cohort, the results are more complicated. Treatment-related changes could also present with high 68 Ga-FAPI-04 uptake in some post-treatment glioma patients in this cohort and untreated patients with intracranial lesions in our previous investigation. Combined with the findings of 68 Ga-FAPI uptake in non-cancerous diseases, including autoimmune disease, cardiovascular disease, and wound healing [ 27 – 30 ], caution must be kept in diagnosing positive 68 Ga-FAPI-04 lesions. Characteristic time activity curve patterns from dynamic 18 F-FET PET imaging could help to differentiate non-cancerous lesions besides classical semi-quantitative parameters. Certain limitations should be addressed in our research. First, this head-to-head study enrolled a relatively small size, and the results of this exploratory investigation need to be confirmed with a more robust patient cohort. Second, this study mainly concentrated on cohort 68 Ga-FAPI-04 and 18 F-FET PET/CT imaging analysis, the combination of MRI parameters with those examined will certainly complement these results. More investigations, including histopathological validation of the different FAPI imaging threshold-based lesion delineations and imaging quantification analysis combined with MRI parameters, will doubtlessly contribute to a more profound understanding of the FAP expressed by CAFs in recurrent glioma patients. Conclusion Our study found the 68 Ga-FAPI-04 uptake in this post-treatment glioma cohort would enhance the differential efficacy of glioma recurrence. The MTV FAPi :MTV FET ratio, an index that illustrates the FAP overexpression from CAFs in glioma microenvironment, has shown the potential in the differentiation of recurrent glioma from treatment-related changes such as radiation necrosis or pseudoprogression. Besides, the metabolic volume of FAP expression in the glioma microenvironment demonstrated a significant influence on the overall survival of this glioma cohort, suggesting the crucial impact of FAP expression in post-treatment glioma patients and providing more evidence for the FAP-related intervention procedures. Abbreviations 18 F-FET: [fluoride-18] fluoroethyl-L-tyrosine MRI: magnetic resonance image RANO: the Response Assessment in Neuro-Oncology TME: tumour microenvironment CAF: Cancer-associated fibroblast FAP: fibroblast activation protein FAPI: fibroblast activation protein inhibitors MTV FAPI : metabolic tumor volume of fibroblast activation protein inhibitor MTV FET : metabolic tumor volume of fluoroethyl-L-tyrosine MTV FAPI :MTV FET ratio: the ratio of tumor MTV FAPI in tumor MTV FET 68 Ga-FAPI-04: [gallium-68] FAP inhibitor-04 PET/CT: positron emission tomography/computed tomography SUVmean: mean standardized uptake value SUVmax: maximal standardized uptake value TLU: total lesion tracer uptake TBRmax: maximal tumour-to-brain ratio TLU FAPI : total lesion tracer uptake of fibroblast activation protein inhibitor TLU FET : total lesion tracer uptake of fluoroethyl-L-tyrosine TLU FAPI :TLU FET ratio: the ratio of tumor TLU FAPI in tumor TLU FET AUC: the area under the receiver operating curve ICC: intraclass correlation coefficients WHO: world health organization Declarations Ethics approval and consent to participate : This study was conducted according to the Declaration of Helsinki (revised in 2013). Ethical approval of our previously written study protocol and consequent analytical design was obtained from the Ethics Committee of Huashan Hospital, Fudan University (No. 2021-891). Written informed consent was obtained from the participants of this study. Consent for publication : The consent of publication of the individual’s data included in this manuscript have been obtained from the persons or parent. Availability of data and materials : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interest : All authors confirmed that no conflicts of interest to declare. Funding: This work was supported by the Science and Technology Commission of Shanghai Municipality (grant No. 18411952100) and the AI for Science Foundation of Fudan University (grant No. FudanX24AI064). Authors Contributions TH and DXZ designed this research. ML, YHG and JBW provided necessary administrative support. ML, FX and DXZ provided study materials and initiated patient enrollment work. TH, ML and DXZ completed data collection. TH, QH and WYZ completed data analysis and interpretation. All authors read and approved the final manuscript. Acknowledgments : We appreciate the support of the pharmacists, nurses, and technicians of the Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University. References Kim H, Zheng S, Amini SS, Virk SM, Mikkelsen T, Brat DJ, et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumour evolution. Genome Res. 2015 Mar;25(3):316-27. https://doi.org/10.1101/gr.180612.114. Mahlokozera T, Vellimana AK, Li T, Mao DD, Zohny ZS, Kim DH, et al. Biological and therapeutic implications of multisector sequencing in newly diagnosed glioblastoma. Neuro Oncol. 2018 Mar 27;20(4):472-483. https://doi.org/10.1093/neuonc/nox232. Qi D, Li J, Quarles CC, Fonkem E, Wu E. 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Tables Table 1 Demographic characteristics of glioma patient cohort Patient No. Age (years) Gender Location Initial Pathology WHO Grade Result Treatment after PET investigations 1 17 F Cerebellar vermis juvenile LGG 1 TRC Chemotherapy 2 18 F Basal ganglia, right GBM 4 TRC Chemotherapy & resection 3 34 M Frontal, left AO 3 TRC Chemotherapy 4 59 M Frontotemporal, left AA 3 TRC Wait-and-see strategy 5 59 M Basal ganglia, right GBM 4 TRC Chemotherapy 6 66 M Frontotemporal, left GBM 4 TRC Chemotherapy 7 56 M Temporal, right AA 3 RE Radiation & chemotherapy & resection 8 53 M Temporal, left GBM 4 RE Radiation & chemotherapy 9 49 F Frontotemporal, right GBM 4 RE Resection 10 51 M Temporal, left GBM 4 RE Radiosurgery 11 39 M Frontal, left AO 3 RE Chemotherapy 12 52 F Frontal, left Oligodendroglioma 2 RE Chemotherapy 13 53 M Frontal, right; Temporal, left GBM 4 RE Resection & chemotherapy 14 34 M Thalamus, right DMG 4 RE Chemotherapy 15 50 M Frontal, right GBM 4 RE Chemotherapy 16 67 F Parietaloccipital, left Astrocytoma 2 RE Resection & chemotherapy 17 59 M Occipital, left GBM 4 RE Chemotherapy 18 51 M Temporal, left GBM 4 RE Resection & chemotherapy 19 51 F Temporal, left GBM 4 RE Resection & chemotherapy 20 51 F Frontal, left GBM 4 RE Chemotherapy 21 41 M Temporal, left GBM 4 RE Resection 22 22 M Thalamus, right GBM 4 RE Radiosurgery & chemotherapy 23 59 M Parietaloccipital, left GBM 4 RE Resection 24 61 M Frontal, right GBM 4 RE Wathc-and-wait strategy 25 57 M Frontal, left Astrocytoma 2 TRC Chemotherapy & TCM 26 46 F Temporal, left GBM 4 TRC Bevacizumab 27 50 F Frontal, right GBM 4 TRC Wait-and-see strategy 28 62 M Parietal, right GBM 4 RE Chemotherapy 29 50 F Parietal, right AO 3 RE Radiation & chemotherapy 30 57 M Basal ganglia, right GBM 4 RE Radiosurgery M, male; F, female; LGG, low grade glioma; TRC, treatment related changes; GBM, glioblastoma; AO, anaplastic oligodendroglioma; AA, anaplastic Table 2 Statistical results of clinical features and PET-based semi-quantitative parameters of glioma patient cohort Item Median (range) Univariate Logistic Regression P value (95% CI) Multivariate Logistic Regression P value (95% CI) Multivariate Logistic Regression P value (95% CI) Age 51 (17-67) 0.272 (0.974, 1.098) 0.248 (0.942, 1.262) 0.551 (0.913, 1.185) Gender N/A 0.402 (0.396, 10.108) 0.057 (0.866, 34090.980) ** 0.089 (0.421, 212721.200) ** Initial pathology N/A 0.053 (0.991, 3.664) ** 0.038 (1.193, 496.059) * N/A*** Initial WHO grade N/A 0.187 (0.733, 4.893) N/A*** 0.072 (0.672, 9393.982) ** TBRmax-FAPI 60.15 (15.40-270.80) 0.544 (0.538, 3.241) 0.443 (0.984, 1.037) 0.965 (0.979, 1.021) MTV-FAPI 10.32 (2.33-46.02) 0.944 (0.934, 1.066) 0.093 (0.402, 1.073) ** 0.188 (0.664, 1.084) TBRmax-FET 3.51 (2.32-5.88) 0.544 (0.538, 3.241) 0.102 (0.587, 375.751) 0.100 (0.480, 4309.016) MTV-FET 20.93 (3.27-226.46) 0.856 (0.981, 1.016) 0.244 (0.931, 1.323) 0.883 (0.926, 1.094) MTV FAPI :MTV FET ratio 0.49 (0.05-3.13) 0.217 (0.533, 15.942) 0.076 (0.143, 1.38e+17) ** 0.079 (0.354, 1.52e+08) ** N/A, non-applicable; TBRmax, maximal tumor-to-brain ratio; FAPI, fibroblast activation protein inhibitor; MTV, metabolic tumor volume; FET, fluoroethyl-L-tyrosine; MTV FAPI :MTV FET ratio, the ratio of MTV FAPI and MTV FET 。 * P < 0.05. ** P < 0.1. *** Initial pathology and initial WHO grade were used separately in multivariate logistic regression to control possible multicollinearity. 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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-6678369","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469072959,"identity":"ea64595f-6490-4c8c-9bbc-d802796b49be","order_by":0,"name":"Tao Hua","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Hua","suffix":""},{"id":469072960,"identity":"194ad993-5de4-42fd-80c9-fde8295fef47","order_by":1,"name":"Qi Huang","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Huang","suffix":""},{"id":469072961,"identity":"774aa536-bf8d-4994-b9f6-f9d761269df8","order_by":2,"name":"Weiyan Zhou","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Weiyan","middleName":"","lastName":"Zhou","suffix":""},{"id":469072962,"identity":"0db7f293-9fbf-465a-8e79-baf36f3aa601","order_by":3,"name":"Jianbo Wen","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Jianbo","middleName":"","lastName":"Wen","suffix":""},{"id":469072963,"identity":"94278d3a-6261-4890-a6e7-477e0b9ef6dc","order_by":4,"name":"Fang Xie","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Xie","suffix":""},{"id":469072964,"identity":"82596515-ec57-4820-b881-aa0e53cf6280","order_by":5,"name":"ming li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACZiB+YABmNT5IqKghUksCWAtjs8GDM8eItCkBTDK2ST5sYSasWred9/CLhAIGeX72xraKxAY2Bv727gS8WswO86VZAB1mOLPnYNuNxB0yDBJnzm4goIXHzACoJcHgRiJQyxk2BgOJXBK0FCS2MROlxfgBTAsDsVrMGKB+aZZIOHOMh7Bfzp8x/vDhDyjEmg9+/FFRI8ff3otfCxCwSTAw/IfzeAgpBwHmD8SoGgWjYBSMghEMAEesRq2gAsxGAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3255-8490","institution":"Huashan Hospital Fudan University","correspondingAuthor":true,"prefix":"","firstName":"ming","middleName":"","lastName":"li","suffix":""},{"id":469072965,"identity":"f0eb93e4-66ae-4388-b403-baba31e869e6","order_by":6,"name":"Yihui Guan","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yihui","middleName":"","lastName":"Guan","suffix":""},{"id":469072966,"identity":"b2734e4a-b218-4c03-bfa0-db61ca1bbd5b","order_by":7,"name":"Dongxiao Zhuang","email":"","orcid":"","institution":"Huashan Hospital Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Dongxiao","middleName":"","lastName":"Zhuang","suffix":""}],"badges":[],"createdAt":"2025-05-16 07:42:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6678369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6678369/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s41181-025-00378-z","type":"published","date":"2025-08-26T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84545213,"identity":"8422f575-e1bf-42a7-9fff-cc1aaa5baf62","added_by":"auto","created_at":"2025-06-13 09:12:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":529998,"visible":true,"origin":"","legend":"\u003cp\u003eIllustrative workflow diagram of the inclusion and exclusion criteria of our post-treatment glioma patient cohort.\u003c/p\u003e","description":"","filename":"Fig.1inclusionandexclusion.png","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/1d73f6383b27aaee1e21a7c8.png"},{"id":84545216,"identity":"f9612dee-ecef-4cb1-8833-be485abb2d7b","added_by":"auto","created_at":"2025-06-13 09:12:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1413773,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of the median of PET-based semiquantitative parameters from \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 and \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT imaging. A-D are the median comparisons of SUVmax, TBRmax, MTV, and TUL of two tracers.\u003c/p\u003e\n\u003cp\u003eSUVmax, maximal standardized uptake value; FAPI, fibroblast activation protein inhibitor; FET fluoroethyl-L-tyrosine; TBRmax, maximal tumour-to-brain ratio; MTV, metabolic tumour volume; TLU, total lesion tracer uptake.\u003c/p\u003e","description":"","filename":"Fig.2semicomparisons.png","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/2eb6d17bec5f70da9d5130a6.png"},{"id":84545219,"identity":"164392e9-af17-4a5c-92e4-79765e22a3cf","added_by":"auto","created_at":"2025-06-13 09:12:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2557050,"visible":true,"origin":"","legend":"\u003cp\u003eThe area under the receiver operating characteristic curve (AUC) comparisons of different predictive models with or without MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio inclusion.\u003c/p\u003e\n\u003cp\u003eA. AUC comparison of MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio inclusion based on the predictive model consists of TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, and initial pathological diagnosis.\u003c/p\u003e\n\u003cp\u003eB. AUC comparison of MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio inclusion based on the predictive model consists of TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, initial pathological diagnosis, age and gender.\u003c/p\u003e\n\u003cp\u003eC. AUC comparison of MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio inclusion based on the predictive model consists of TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI and initial WHO grade.\u003c/p\u003e\n\u003cp\u003eD. AUC comparison of MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio inclusion based on the predictive model consists of TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, initial WHO grade, age and gender.\u003c/p\u003e\n\u003cp\u003eMTV, metabolic tumour volume; FAPI, fibroblast activation protein inhibitor; FET, fluoroethyl-L-tyrosine.\u003c/p\u003e","description":"","filename":"Fig.3AUC.png","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/ae5fcf329887d5b5f6bdc77e.png"},{"id":84545217,"identity":"617c2a5b-58b6-42a0-8420-d83f09904734","added_by":"auto","created_at":"2025-06-13 09:12:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2103292,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the survival analysis results.\u003c/p\u003e\n\u003cp\u003eA. Visualization of the relationship between MTV-FAPI and cohort overall survival.\u003c/p\u003e\n\u003cp\u003eB. Kaplan-Meier plot after Cox regression including TBRmax-FAPI, TBRmax-FET, MTV-FAPI, MTV-FET, MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio, and initial WHO grade. Kaplan-Meier plot was applied after the dichotomization of the MTV-FAPI median of the cohort.\u003c/p\u003e\n\u003cp\u003eMTV, metabolic tumour volume; FAPI, fibroblast activation protein inhibitor; TBRmax, maximal tumour-to-brain ratio; FET, fluoroethyl-L-tyrosine.\u003c/p\u003e","description":"","filename":"Fig.4Survival.png","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/75d78a8c66337568148fb323.png"},{"id":84545227,"identity":"b3d4edf4-1280-45ef-bace-d499626290f8","added_by":"auto","created_at":"2025-06-13 09:12:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":20448441,"visible":true,"origin":"","legend":"\u003cp\u003eIllustration of the MRI contrast, \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04, \u003csup\u003e18\u003c/sup\u003eF-FET, and CT images of three representative cases in our cohort.\u003c/p\u003e\n\u003cp\u003eUpper row. 18-year-old female patient with initial right basal glioblastoma. The TBRmax-FAPI, TBRmax-FET, MTV-FAPI, MTV-FET, and MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio of the patient was 44.75, 3.49, 11.51, 16.53, 0.70 respectively. The follow-up diagnosis after PET investigations was pseudoprogression.\u003c/p\u003e\n\u003cp\u003eMiddle row. 59-year-old male patient with initial right basal glioblastoma. The TBRmax-FAPI, TBRmax-FET, MTV-FAPI, MTV-FET, and MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio of the patient was 47.33, 3.26, 16.35, 34.88, 0.47 respectively. The follow-up diagnosis after PET investigations was radiation necrosis.\u003c/p\u003e\n\u003cp\u003eLower row. 51-year-old female patient with initial left temporal glioblastoma. The TBRmax-FAPI, TBRmax-FET, MTV-FAPI, MTV-FET, and MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio of the patient was 64.75, 5.24, 7.09, 6.72, 1.06 respectively. The follow-up diagnosis after PET investigations was glioma recurrence.\u003c/p\u003e","description":"","filename":"Fig.5cases.png","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/9dfe9bbcd139415122b1d626.png"},{"id":90344906,"identity":"292381de-126a-46b0-a070-fb691bcedaf8","added_by":"auto","created_at":"2025-09-01 16:07:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":25289459,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/e6a2ea35-07b6-4812-a053-e7a68b321878.pdf"},{"id":84546091,"identity":"70400101-68d4-45a5-8e55-82ccff6e1c2b","added_by":"auto","created_at":"2025-06-13 09:20:59","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":44184,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6678369/v1/67b5716aef26d9cb9481a969.docx"}],"financialInterests":"","formattedTitle":"Fibroblast activation protein expression in tumour microenvironment is crucial in differentiation and survival prediction of recurrent gliomas: a head-to-head comparison of 68Ga-FAPI-04 and 18F-FET in PET/CT imaging","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGliomas are the most frequent central nervous system malignancy. Standard treatment strategies, including maximum surgical resection followed by radiotherapy and chemotherapy, have been applied for decades. However, the substantial tumoural heterogeneity of gliomas leads to inevitable tumour recurrence of most gliomas [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Accurate diagnosis of glioma recurrence is essential for optimizing patient survival and quality of life, as timely detection enables tailored therapeutic strategies to enhance survival rates and prevent unnecessary treatments. Unfortunately, challenges persist due to the similar imaging manifestations between tumour recurrence and treatment-related changes such as pseudoprogression or radiation necrosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRadio-labelled amino acid tracers such as [fluoride-18] fluoroethyl-L-tyrosine (\u003csup\u003e18\u003c/sup\u003eF-FET) could target the overexpressed L-type amino acid transporters in glioma cells and contribute to the differential diagnosis, prognostication, treatment strategy planning, and treatment effects monitoring [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. With the satisfactory diagnostic efficacy of both non-contrast and contrast gliomas, the Response Assessment in Neuro-Oncology (RANO) working group recommends amino acid tracers PET imaging as a valuable complement to magnetic resonance image (MRI) in all stages of glioma management [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite the satisfactory performance of radio-labelled amino acid tracers, the inflammation tissues around the treatment area could also present with abnormal amino acid tracer uptake to some degree and impose uncertainty of recurrent glioma diagnosis.\u003c/p\u003e \u003cp\u003eBenign cells in the tumour microenvironment (TME) complicate glioma heterogeneity. The interactions between glioma cells and adjacent cells in tumour stroma can promote tumour proliferation, migration, angiogenesis, and recurrence through various mechanisms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Cancer-associated fibroblasts (CAFs), the components of TME, are actively involved in the crosstalk between tumour cells and stromal cells via the secretion of growth factors and inflammatory cytokines. CAFs may overexpress a transmembrane glycoprotein known as fibroblast activation protein (FAP) in the tumour microenvironment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Radio-labelled fibroblast activation protein inhibitors (FAPI) have demonstrated efficacy in imaging FAP overexpression across a range of solid tumours with satisfactory results [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As there is significant FAP accumulation in the stroma of malignant tumours and satisfactory tissue contrast, FAP-targeted imaging has efficacy in malignant tumour detection, tumour delineation, and radiotherapy planning [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our previous studies explored the overexpression patterns of FAP in an untreated intracranial tumour cohort, including a series of glioma subtypes, medulloblastoma, and brain metastasis. The results demonstrated that more malignant intracranial tumours, including brain metastasis, glioblastoma, and medulloblastoma, presented with more FAP overexpression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Besides, the ratio of MTV\u003csub\u003eFAPI\u003c/sub\u003e to MTV\u003csub\u003eFET\u003c/sub\u003e (MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio), an index that could describe the interaction of glioma and cancer-associated fibroblasts in the tumour microenvironment, has shown the potential to enhance differential efficacy in untreated intracranial tumours. Based on these findings, whether the FAP overexpression could benefit the differentiation of glioma recurrence from pseudoprogression or radiation necrosis is of clinical significance.\u003c/p\u003e \u003cp\u003eThis prospective, head-to-head study applied [gallium-68] FAP inhibitor-04 (\u003csup\u003e68\u003c/sup\u003eGa-FAPI-04) and \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT imaging to post-treatment glioma patients with tumour recurrence signs under regular MRI follow-up for the investigation of \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 efficacy. A quantification analysis of PET-based semiquantitative parameters and important clinical factors was employed to explore the relationship between tumour and CAFs interactions and glioma recurrence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eStudy design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStructural MRI follow-up and neurosurgical specialist consultation of post-treatment glioma patients were regularly applied in the outpatient department of Huashan Hospital, Fudan University. The patients were enrolled for further investigation until the suspected recurrent signs were shown. Enrolled post-treatment glioma patients received \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 and \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT brain imaging from October 2022 to May 2024\u0026nbsp;in the Nuclear Medicine \u0026amp; PET Center of Huashan Hospital, Fudan University. PET-based semiquantitative imaging parameters and clinical information were obtained to evaluate this head-to-head study. Written informed consent from patients was obtained.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePET/CT i\u003c/em\u003e\u003cem\u003emaging protocols\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 and \u003csup\u003e18\u003c/sup\u003eF-FET tracers were synthesized in the Department of Nuclear Medicine \u0026amp; PET Center of Huashan Hospital, Fudan University. The two radio-labelled tracers PET\u0026nbsp;scans were applied with at least a 24-hour interval for each patient in our cohort.\u003c/p\u003e\n\u003cp\u003eFor \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT imaging, patients fasted for at least 4 hours before imaging. A 20-minute static scan was conducted in 3-dimensional mode with a Biograph mCT Flow Edge 128 PET/CT system (Siemens Healthineers, Erlangen, Germany) 20 minutes after intravenous bolus injection of \u003csup\u003e18\u003c/sup\u003eF-FET (182 \u0026plusmn; 17.5 MBq). Attenuation correction was performed using low-dose CT (tube current = 150 mAs, voltage = 120 kV, acquisition = 64 \u0026times;\u0026nbsp;0.6 mm, convolution kernel = H30s, slice thickness = 5 mm, interslice gap = 1.5 mm) before the emission scan. Post-acquisition, PET images were reconstructed using the ordered subset expectation maximization (OSEM) algorithm with a Gaussian filter and a full width at half maximum of 3.5 mm at the center of the field of view.\u003c/p\u003e\n\u003cp\u003eIn \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 PET/CT imaging, 30 minutes after intravenous bolus injection of \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 (176 \u0026plusmn; 19.2 MBq), a 30-minute static scan was conducted in 3D mode with a uMI510 PET/CT (United Imaging, Shanghai, China). Attenuation correction was similarly performed using low-dose CT before the emission scan. PET images were also reconstructed using the OSEM algorithm with a Gaussian filter and the same full width at half maximum after acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePET/CT i\u003c/em\u003e\u003cem\u003emage analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePET/CT images were analyzed with a syngo.via workstation (Siemens Healthineers). Two experienced nuclear medicine physicians (WXZ\u0026nbsp;and\u0026nbsp;TH,\u0026nbsp;with over 6 and 13 years of experience, respectively) performed\u0026nbsp;blinded\u0026nbsp;\u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 and \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT positive lesion judgment and lesion delineation\u0026nbsp;before surgical treatment.\u003c/p\u003e\n\u003cp\u003eStructural MRI was initially reviewed for lesion location before PET/CT lesion delineation. For \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT imaging, the mean standardized uptake value (SUVmean) of the brain background was measured in a crescent-shaped area, encompassing gray and white matter on the lesion\u0026rsquo;s contralateral hemisphere\u0026nbsp;[22]. Subsequently, 1.6 times of background SUVmean was used for lesion delineation, and the maximal standardized uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion tracer uptake (TLU) were obtained.\u0026nbsp;The maximal tumour-to-brain ratio (TBRmax) was calculated by dividing the intracranial lesion SUVmax with the background SUVmean.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 imaging, the background SUVmean was measured similarly to that of \u003csup\u003e18\u003c/sup\u003eF-FET PET. Lesion SUVmax and TBRmax were measured and calculated. Due to the lack of guidelines for a FAPI-positive lesion delineation and the experience of our previous investigation, we applied a 20% isocontour volumetric threshold of lesion SUVmax for the delineation and measurement of lesion MTV\u003csub\u003eFAP\u003c/sub\u003e\u003csub\u003eI\u003c/sub\u003e and TLU\u003csub\u003eFAP\u003c/sub\u003e\u003csub\u003eI\u0026nbsp;\u003c/sub\u003e[21].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the quantification description of FAP expression in the glioma microenvironment, a series of semi-quantitative parameters, including MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio and TLU\u003csub\u003eFAPI\u003c/sub\u003e:TLU\u003csub\u003eFET\u003c/sub\u003e ratio, were obtained for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOwing to the concerns that too small lesions could not fully describe the interactions between FAP expressed in glioma TME, those patients with\u0026nbsp;MTV-FET less than 1 cm\u003csup\u003e3\u003c/sup\u003e were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiagnosis protocol\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor patients who received the following surgical treatment, including surgical resection or stereotactic surgical biopsy, in the Neurosurgery department of Huashan Hospital, Fudan University, histopathological diagnosis results were obtained via our facility\u0026apos;s medical record system.\u003c/p\u003e\n\u003cp\u003eFor patients who received treatment strategies, including radiosurgery, radiotherapy, chemotherapy, or other therapies, expert specialists\u0026rsquo; consultation opinions were collected as authorized diagnoses.\u003c/p\u003e\n\u003cp\u003eRegular follow-ups were applied and the overall survival for this patient cohort was collected.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analyses\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics are expressed as the mean and standard deviation or median and range. The \u003cem\u003et-\u003c/em\u003etest and one-way analysis of variance were used to compare continuous variables. The Wilcoxon signed rank or Kruskal-Wallis test was performed if a normal distribution of variables was not met. Linear regression analysis investigated the relationship between pathological diagnosis and PET parameters. The variance inflation factor was used to control multicollinearity. Logistic regression was used to explore the diagnostic efficacy of the demographic and PET parameters. The area under the receiver operating curve (AUC) was used to observe the diagnostic efficacy. Cox regression and the Kaplan-Meier method were applied to observe the relationship between the overall survival (OS) and the parameters. Intraclass correlation coefficients (ICCs) for PET parameter measurements were assessed, and the results were classified as poor (less than 0.2), fair (0.21\u0026ndash;0.4), moderate (0.41\u0026ndash;0.6), good (0.61\u0026ndash;0.8), and very good (0.8\u0026ndash;1.0). All statistical analyses were performed with Stata version 17 (College Station, TX, USA). In all analyses, P \u0026lt; 0.05 indicated a statistically significant difference.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatient cohort characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e30 adult post-treatment glioma patients were enrolled for research, including 20 males and 10 females. The cohort\u0026apos;s median age was\u0026nbsp;51 years (range 17-67 years). The illustrative workflow diagram of the inclusion and exclusion criteria can be seen in Fig. 1. The initial diagnosis of this patient cohort included juvenile low-grade glioma, oligodendroglioma, astrocytoma, anaplastic oligodendroglioma, anaplastic astrocytoma, glioblastoma multiforme, and diffuse intrinsic pontine glioma. There were 1, 3, 5, and 21 patients from grades 1 through 4 in the WHO grade stratification. The follow-up results showed that 21 patients were diagnosed with glioma recurrence, and 9 patients were diagnosed with treatment-related changes, including radiation necrosis and pseudoprogression. Demographic details of the cohort are provided in\u0026nbsp;Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePET-based lesions semiquantitative parameters comparison of \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 and \u003csup\u003e18\u003c/sup\u003eF-FET imaging\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eICCs showed very good agreement between the different lesions for \u003csup\u003e18\u003c/sup\u003eF-FET and \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 PET/CT semiquantitative parameter measurements (ICC\u0026gt;0.96, P\u0026lt;0.001; ICC\u0026gt;0.91, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and the results of reader one (TH) were used for analysis.\u003c/p\u003e\n\u003cp\u003eFor \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 PET images, the median of lesion SUVmax was 2.79 (range 0.77, 13.18), and the median of lesion TBRmax was 60.15 (range 15.40, 270.80). The median of lesion MTV was 10.32 (range 2.33, 46.02), and the median of lesion TLU was 9.70 (range 2.01, 71.38). While for \u003csup\u003e18\u003c/sup\u003eF\u0026ndash;FET PET imaging, the median of lesion SUVmax was 3.11 (range 1.70, 5.02), and the median of lesion TBRmax was 3.51 (range 2.32, 5.88). The median of lesion MTV was 20.93 (range 3.27, 226.46), and the median of lesion TLU was 37.32 (range 5.53, 362.34), respectively. The details of the semi-quantitative parameters originating from the two PET tracers imaging were provided in supplementary material. The illustrative comparison of lesion semiquantitative parameters from two PET tracers was provided in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDiagnostic efficacy analysis of PET-based semiquantitative parameters and clinical features\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression was initially applied to all the semiquantitative parameters and clinical features; only the initial pathological diagnosis demonstrated borderline significant diagnostic efficacy (P = 0.053). The supplementary material provided details of the univariate logistic regression statistical results.\u003c/p\u003e\n\u003cp\u003eAfterwards, multivariate logistic regression was utilized to build the diagnostic model. The investigation took three steps: first, the efficacy of PET-based semiquantitative parameters was assessed; then, the initial pathological diagnosis or initial WHO grade was included in the observation model; and finally, age and gender were incorporated into the model for evaluation.\u003c/p\u003e\n\u003cp\u003eIn the PET-based semiquantitative logistic regression analysis that included TBRmax-FET, TBRmax-FAPI, MTV-FET, and MTV-FAPI, no statistical significance was found for these semiquantitative parameters, and the AUC of the four-parameter model was 0.598 (0.385-0.811, 95% confidence interval, CI). We further explored the efficacy of the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio as the interaction index between the tumour and the TME. After introducing this parameter into the current semiquantitative parameters model, the logistic regression results indicated that the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio showed borderline diagnostic significance, with a P value of 0.094, and the AUC of the current model improved to 0.767 (95%CI: 0.593-0.942). The DeLong test results indicated no significant difference between the AUCs of these two models (P = 0.126). Results details are provided in the supplementary material.\u003c/p\u003e\n\u003cp\u003eThen, the pathological information of the patient cohort was incorporated into our investigation. In addition to TBRmax-FET, TBRmax-FAPI, MTV-FET, and MTV-FAPI, we included the initial pathological diagnosis in the model. The regression results indicated that the initial pathological diagnosis has significant diagnostic efficacy, with a P-value of 0.033 and an AUC of 0.709 (95% CI: 0.465-0.953). Next, we evaluated the effectiveness of the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio in this diagnostic model. The regression results showed that the initial pathological diagnosis continued to demonstrate statistical significance, with a P-value of 0.045. The AUC for the model at this stage increased to 0.847 (95% CI: 0.689-1.000). The DeLong test results indicated a significant difference between the two models, with a P-value of 0.040. The supplementary material provided multivariate logistic regression results details, and Fig. 3A compares the AUC with and without the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio in the current stage.\u003c/p\u003e\n\u003cp\u003eAs planned, we incorporated the cohort\u0026apos;s age and gender status into our analysis. In the logistic regression model that included TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, initial pathological diagnosis, age, and gender, the results showed that the initial pathological diagnosis significantly differentiates recurrent glioma in our cohort, with a P-value of 0.044. The current AUC for the diagnostic model was 0.841 (95% CI: 0.677-1.000). Similarly, we focused on the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio for our observations. The analysis revealed that one parameter had significant efficacy, while three parameters showed borderline diagnostic efficacy in differentiating recurrent gliomas. These parameters included the initial pathological diagnosis (P = 0.038), gender (P = 0.057), MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P = 0.076), and MTV-FAPI (P = 0.093). The current AUC for this model was 0.963 (95% CI: 0.887-1.000). The DeLong test indicated a significant difference between the two models, with a P-value of 0.039. The details of the results are provided in the supplementary material, and the comparison of AUC with and without the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio can be seen in Fig. 3B.\u003c/p\u003e\n\u003cp\u003eDue to discrepancies between the pathological diagnosis and the WHO grade, we replaced the initial pathological diagnosis with the initial WHO grade for our investigation. In the diagnostic model that included TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, and the initial WHO grade, we found no statistically significant results, with the model\u0026apos;s AUC at 0.640 (95% CI: 0.400-0.880). We subsequently introduced the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio into the model. The regression results indicated that two parameters approached significance, the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P = 0.081) and the initial WHO grade (P = 0.086). With this addition, the AUC improved to 0.852 (95% CI: 0.715-0.988). The DeLong test results demonstrated a significant difference between the two models (P = 0.016). The multivariate logistic regression results are available in the supplementary material, and the AUC comparison with and without the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio can be viewed in Fig. 3C.\u003c/p\u003e\n\u003cp\u003eWe then introduced age and gender status with the initial WHO grade for our exploration. In the regression model with TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, initial WHO grade, age, and gender, we found no statistical significance. The model had an AUC of 0.762 (95% CI: 0.532-0.992).\u0026nbsp;Then the effectiveness of the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio within this model was assessed. Upon introduction of this ratio, three parameters displayed borderline diagnostic efficacy, including initial WHO grade (P = 0.072), MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P = 0.079), and gender (P = 0.089). The AUC of this revised model was 0.942 (0.850-1.000, 95% CI). Results from the DeLong test indicated a significant difference between these two models (P = 0.046). The supplementary material provided multivariate logistic regression results details, and the AUC comparison with or without MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio can be seen in Fig. 3D.\u003c/p\u003e\n\u003cp\u003eThe univariate and multivariate logistic regression results of the cohort were provided in Table 2. The variance inflation factor control excluded multicollinearity in the above-mentioned investigations, and results were provided in the supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSurvival analysis results of patient-based PET semi-quantitative parameters\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the survival analysis of parameters including TBRmax-FET, TBRmax-FAPI, MTV-FET, MTV-FAPI, MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio, initial WHO grade, age and gender, Cox regression results indicated that MTV-FAPI has statistical significance (P = 0.027, hazard ratio = 1.103, 1.011-1.204, 95% CI). After Cox regression analysis, the proportional hazards assumption test was applied, and the P value was 0.875, indicating the results do not violate the proportional hazards assumption. The detailed results of Cox regression were provided in the supplementary material. The visualization of the relationship between MTV-FAPI and cohort overall survival can be seen in Fig. 4A and the Kaplan-Meier plot of MTV-FAPI after Cox regression in this cohort can be seen in Fig. 4B.\u003c/p\u003e\n\u003cp\u003eTypical illustrative cases and related clinical information and parameters are shown in Fig. 5.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on the distinct FAP expression patterns across a wide range of carcinomas and our experience in the untreated intracranial tumour cohort, we further explore the diagnostic efficacy of \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 PET imaging in this post-treatment glioma cohort. Our study found that in the logistic regression analysis of PET-based semiquantitative parameters, the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio showed borderline diagnostic efficacy (P\u0026thinsp;=\u0026thinsp;0.094). Additionally, when assessing PET-based semiquantitative parameters alongside the initial pathological diagnosis, this initial diagnosis demonstrated statistical significance (P\u0026thinsp;=\u0026thinsp;0.045). Furthermore, incorporating the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio significantly improved the model's AUC from 0.709 to 0.847 (P\u0026thinsp;=\u0026thinsp;0.040). In the analysis of PET-based semiquantitative parameters, initial pathological diagnosis, age, and gender status, the initial pathological diagnosis was found to have a statistically significant diagnostic effect (P\u0026thinsp;=\u0026thinsp;0.038). Additionally, three parameters showed borderline diagnostic significance, including gender (P\u0026thinsp;=\u0026thinsp;0.057), MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P\u0026thinsp;=\u0026thinsp;0.076), and MTV-FAPI (P\u0026thinsp;=\u0026thinsp;0.093). Notably, the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio significantly improved the model\u0026rsquo;s AUC from 0.841 to 0.963 (P\u0026thinsp;=\u0026thinsp;0.039). When we replaced the initial pathological diagnosis with the initial WHO grade for the cohort, the regression results revealed that two parameters exhibited borderline diagnostic efficacy, including the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P\u0026thinsp;=\u0026thinsp;0.081) and initial WHO grade (P\u0026thinsp;=\u0026thinsp;0.086). The MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio significantly elevated the model AUC from 0.640 to 0.852 (P\u0026thinsp;=\u0026thinsp;0.016). In the analysis of PET-based semiquantitative parameters, initial WHO grade, age, and gender status, results identified three parameters with borderline diagnostic efficacy, including initial WHO grade (P\u0026thinsp;=\u0026thinsp;0.072), MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P\u0026thinsp;=\u0026thinsp;0.079), and gender (P\u0026thinsp;=\u0026thinsp;0.089). Similarly, the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio also significantly enhanced the model AUC from 0.762 to 0.942 (P\u0026thinsp;=\u0026thinsp;0.046). Survival analysis results showed that MTV-FAPI is an independent risk factor for overall survival in the cohort (P\u0026thinsp;=\u0026thinsp;0.027, hazard ratio\u0026thinsp;=\u0026thinsp;1.103, 95% CI: 1.011\u0026ndash;1.204). This implies that for each additional cubic centimeter increase in the lesion \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 metabolic volume in this post-treatment glioma cohort, the risk of death increases by a factor of 1.103.\u003c/p\u003e \u003cp\u003eThe interactions of genetic background and tumour microenvironment lead to the progression of gliomas [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. FAP expression status in glioma patients could be used to evaluate the crosstalk of glioma cells and cancer-associated fibroblasts. TME cell-targeting tracers can offer various perspectives in assessing the heterogeneities of gliomas in recurrent gliomas besides classical glioma cell-targeted imaging. With the consideration of the notorious intra- and inter-tumoural heterogeneity, our head-to-head investigations from both glioma parenchyma cells and TME cells perspectives explored the relationship between glioma recurrence and FAP overexpression patterns. The potential that MTV\u003csub\u003eFAPi\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio could benefit glioma recurrence differentiation indicated that FAP overexpression is actively involved in the microenvironment during the glioma recurrent process, and as a result, the metabolic volume of FAP overexpression in glioma TME significantly influences the survival of post-treatment glioma patients. These findings will contribute to a better understanding of CAFs effects in recurrent gliomas. Our study expanded the scope of FAP overexpression patterns in post-treatment glioma patients for more accurate differentiation of tumour recurrence. The malignant prognosis of most glioma patients has raised a huge challenge for treatment, especially for glioblastoma patients. The investigation of FAP expression patterns in our cohort could provide valuable evidence for future FAP-targeted theragnostic procedures as complements to classical treatment strategies.\u003c/p\u003e \u003cp\u003eLesion delineation is vital for treatment planning. Radio-labelled amino acid tracer imaging could well outline lesions with the guidelines' recommendations. However, no recommendation for brain lesion delineation in FAP-targeted imaging is available yet. In our previous FAP-targeting imaging research, we explored lesion delineation with the consideration of FAPI imaging characteristics in pre-treatment intracranial tumour cohort; different percentages of lesion SUVmax were used as thresholds to control the influence of delineation fluctuation owing to the extremely low uptake of the normal brain background. In the current research, we applied the same 20% lesion SUVmax as an isocontour volumetric threshold for lesion delineation. More evidence-based suggestions should be necessary for a more accurate delineation of solid tumours on FAPI PET imaging.\u003c/p\u003e \u003cp\u003eStructural MRI is the most widely used imaging modality in glioma diagnosis. Regular MRI follow-up could locate the newly developed abnormal enhanced lesion and provide alert signs for possible glioma recurrence. Considering the complexities of post-treatment gliomas, MRI RANO 2.0 guidelines contribute to better calibrating recurrent glioma patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. As an efficient complement to MRI imaging, PET RANO 1.0-based amino acid tracer PET imaging could contribute to better recognizing and evaluating post-treatment glioma patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Further research should be applied to differentiate glioma recurrence from treatment-associated changes under these guidelines and the latest WHO glioma classification [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Besides those, the TME cells-targeting tracer PET investigations will definitely provide additional and valuable evidence for both diagnosis and possible intervention direction.\u003c/p\u003e \u003cp\u003eResearchers from other groups reported perfect diagnostic efficacy of FAPI PET for recurrent glioma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], while in our cohort, the results are more complicated. Treatment-related changes could also present with high \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 uptake in some post-treatment glioma patients in this cohort and untreated patients with intracranial lesions in our previous investigation. Combined with the findings of \u003csup\u003e68\u003c/sup\u003eGa-FAPI uptake in non-cancerous diseases, including autoimmune disease, cardiovascular disease, and wound healing [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], caution must be kept in diagnosing positive \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 lesions. Characteristic time activity curve patterns from dynamic \u003csup\u003e18\u003c/sup\u003eF-FET PET imaging could help to differentiate non-cancerous lesions besides classical semi-quantitative parameters.\u003c/p\u003e \u003cp\u003eCertain limitations should be addressed in our research. First, this head-to-head study enrolled a relatively small size, and the results of this exploratory investigation need to be confirmed with a more robust patient cohort. Second, this study mainly concentrated on cohort \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 and \u003csup\u003e18\u003c/sup\u003eF-FET PET/CT imaging analysis, the combination of MRI parameters with those examined will certainly complement these results. More investigations, including histopathological validation of the different FAPI imaging threshold-based lesion delineations and imaging quantification analysis combined with MRI parameters, will doubtlessly contribute to a more profound understanding of the FAP expressed by CAFs in recurrent glioma patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study found the \u003csup\u003e68\u003c/sup\u003eGa-FAPI-04 uptake in this post-treatment glioma cohort would enhance the differential efficacy of glioma recurrence. The MTV\u003csub\u003eFAPi\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio, an index that illustrates the FAP overexpression from CAFs in glioma microenvironment, has shown the potential in the differentiation of recurrent glioma from treatment-related changes such as radiation necrosis or pseudoprogression. Besides, the metabolic volume of FAP expression in the glioma microenvironment demonstrated a significant influence on the overall survival of this glioma cohort, suggesting the crucial impact of FAP expression in post-treatment glioma patients and providing more evidence for the FAP-related intervention procedures.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-FET: [fluoride-18] fluoroethyl-L-tyrosine\u003c/p\u003e\n\u003cp\u003eMRI: magnetic resonance image\u003c/p\u003e\n\u003cp\u003eRANO: the Response Assessment in Neuro-Oncology\u003c/p\u003e\n\u003cp\u003eTME: tumour microenvironment\u003c/p\u003e\n\u003cp\u003eCAF: Cancer-associated fibroblast\u003c/p\u003e\n\u003cp\u003eFAP: fibroblast activation protein\u003c/p\u003e\n\u003cp\u003eFAPI: fibroblast activation protein inhibitors\u003c/p\u003e\n\u003cp\u003eMTV\u003csub\u003eFAPI\u003c/sub\u003e: metabolic tumor volume of fibroblast activation protein inhibitor\u003c/p\u003e\n\u003cp\u003eMTV\u003csub\u003eFET\u003c/sub\u003e: metabolic tumor volume of fluoroethyl-L-tyrosine\u003c/p\u003e\n\u003cp\u003eMTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio: the ratio of tumor MTV\u003csub\u003eFAPI\u0026nbsp;\u003c/sub\u003ein tumor MTV\u003csub\u003eFET\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e68\u003c/sup\u003eGa-FAPI-04: [gallium-68] FAP inhibitor-04\u003c/p\u003e\n\u003cp\u003ePET/CT: positron emission tomography/computed tomography\u003c/p\u003e\n\u003cp\u003eSUVmean: mean standardized uptake value\u003c/p\u003e\n\u003cp\u003eSUVmax: maximal standardized uptake value\u003c/p\u003e\n\u003cp\u003eTLU: total lesion tracer uptake\u003c/p\u003e\n\u003cp\u003eTBRmax: maximal tumour-to-brain ratio\u003c/p\u003e\n\u003cp\u003eTLU\u003csub\u003eFAPI\u003c/sub\u003e: total lesion tracer uptake\u0026nbsp;of fibroblast activation protein inhibitor\u003c/p\u003e\n\u003cp\u003eTLU\u003csub\u003eFET\u003c/sub\u003e: total lesion tracer uptake\u0026nbsp;of\u0026nbsp;fluoroethyl-L-tyrosine\u003c/p\u003e\n\u003cp\u003eTLU\u003csub\u003eFAPI\u003c/sub\u003e:TLU\u003csub\u003eFET\u003c/sub\u003e ratio:\u0026nbsp;the ratio of tumor TLU\u003csub\u003eFAPI\u0026nbsp;\u003c/sub\u003ein tumor TLU\u003csub\u003eFET\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eAUC: the area under the receiver operating curve\u003c/p\u003e\n\u003cp\u003eICC: intraclass correlation coefficients\u003c/p\u003e\n\u003cp\u003eWHO: world health organization\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: This study was conducted according to the Declaration of Helsinki (revised in 2013). Ethical approval of our previously written study protocol and consequent analytical design was obtained from the Ethics Committee of Huashan Hospital, Fudan University (No. 2021-891). Written informed consent was obtained from the participants of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: The consent of publication of the individual\u0026rsquo;s data included in this manuscript have been obtained from the persons or parent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest\u003c/strong\u003e: All authors confirmed that no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Science and Technology Commission of Shanghai Municipality (grant No. 18411952100) and the AI for Science Foundation of Fudan University (grant No. FudanX24AI064).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTH and DXZ designed this research. ML, YHG and JBW provided necessary administrative support. ML, FX and DXZ provided study materials and initiated patient enrollment work. TH, ML and DXZ completed data collection. TH, QH and WYZ completed data analysis and interpretation. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: We appreciate the support of the pharmacists, nurses, and technicians of the Department of Nuclear Medicine \u0026amp; PET Center, Huashan Hospital, Fudan University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKim H, Zheng S, Amini SS, Virk SM, Mikkelsen T, Brat DJ, et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumour evolution. Genome Res. 2015 Mar;25(3):316-27. https://doi.org/10.1101/gr.180612.114.\u003c/li\u003e\n\u003cli\u003eMahlokozera T, Vellimana AK, Li T, Mao DD, Zohny ZS, Kim DH, et al. 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Clin Nucl Med. 2021 May;46(5):419\u0026ndash;21. https://doi.org/10.1097/RLU.0000000000003552.\u003c/li\u003e\n\u003cli\u003eKessler L, Kupusovic J, Ferdinandus J, Hirmas N, Umutlu L, Zarrad F, et al. Visualization of fibroblast activation after myocardial infarction using 68Ga-FAPI PET. Clin Nucl Med. 2021 Oct;46(10):807\u0026ndash;13. https://doi.org/10.1097/RLU.0000000000003745.\u003c/li\u003e\n\u003cli\u003eZhou Y, Yang X, Liu H, Luo W, Liu H, Lv T, et al. Value of [(68) Ga]Ga-FAPI-04 imaging in the diagnosis of renal fibrosis. Eur J Nucl Med Mol Imaging. 2021 Oct;48(11):3493\u0026ndash;3501. https://doi.org/10.1007/s00259-021-05343-x.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e Demographic characteristics of glioma patient cohort\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003ePatient No.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eInitial Pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eWHO Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eResult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eTreatment after PET investigations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eCerebellar vermis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003ejuvenile LGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBasal ganglia, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy \u0026amp; resection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontotemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWait-and-see strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBasal ganglia, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontotemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRadiation \u0026amp; chemotherapy \u0026amp; resection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRadiation \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontotemporal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRadiosurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eOligodendroglioma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, right; Temporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThalamus, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eDMG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eParietaloccipital, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAstrocytoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOccipital, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eThalamus, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRadiosurgery \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eParietaloccipital, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eResection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWathc-and-wait strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAstrocytoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy \u0026amp; TCM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eTemporal, left\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eBevacizumab\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eFrontal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eTRC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWait-and-see strategy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eParietal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eParietal, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRadiation \u0026amp; chemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBasal ganglia, right\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eGBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003eRE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRadiosurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eM, male; F, female; LGG, low grade glioma; TRC, treatment related changes; GBM, glioblastoma; AO, anaplastic oligodendroglioma; AA, anaplastic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Statistical results of clinical features and PET-based semi-quantitative parameters of glioma patient cohort\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eUnivariate Logistic Regression\u003c/p\u003e\n \u003cp\u003eP value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\n \u003cp\u003eP value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eMultivariate Logistic Regression\u003c/p\u003e\n \u003cp\u003eP value (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e51 (17-67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.272 (0.974, 1.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.248 (0.942, 1.262)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.551 (0.913, 1.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.402 (0.396, 10.108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.057 (0.866, 34090.980) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.089 (0.421, 212721.200) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eInitial pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.053 (0.991, 3.664) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.038 (1.193, 496.059) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003eN/A***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eInitial WHO grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.187 (0.733, 4.893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003eN/A***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.072 (0.672, 9393.982) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTBRmax-FAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e60.15 (15.40-270.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.544 (0.538, 3.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.443 (0.984, 1.037)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.965 (0.979, 1.021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMTV-FAPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e10.32 (2.33-46.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.944 (0.934, 1.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.093 (0.402, 1.073) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.188 (0.664, 1.084)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eTBRmax-FET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e3.51 (2.32-5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.544 (0.538, 3.241)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.102 (0.587, 375.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.100 (0.480, 4309.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMTV-FET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e20.93 (3.27-226.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.856 (0.981, 1.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.244 (0.931, 1.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.883 (0.926, 1.094)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e0.49 (0.05-3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e0.217 (0.533, 15.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 217px;\"\u003e\n \u003cp\u003e0.076 (0.143, 1.38e+17) **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e0.079 (0.354, 1.52e+08) **\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN/A, non-applicable; TBRmax, maximal tumor-to-brain ratio; FAPI, fibroblast activation protein inhibitor; MTV, metabolic tumor volume; FET, fluoroethyl-L-tyrosine;\u0026nbsp;MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio, the ratio of MTV\u003csub\u003eFAPI\u003c/sub\u003e and MTV\u003csub\u003eFET\u003c/sub\u003e\u003csub\u003e。\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e* \u0026nbsp; P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e** \u0026nbsp;P \u0026lt; 0.1.\u003c/p\u003e\n\u003cp\u003e*** Initial pathology and initial WHO grade were used separately in multivariate logistic regression to control possible multicollinearity.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-radiopharmacy-and-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"erpc","sideBox":"Learn more about [EJNMMI Radiopharmacy and Chemistry](http://ejnmmipharmchem.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/erpc/default.aspx","title":"EJNMMI Radiopharmacy and Chemistry","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"glioma recurrence, fibroblast activation protein, positron emission tomography, fibroblast activation protein inhibitor, differentiation","lastPublishedDoi":"10.21203/rs.3.rs-6678369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6678369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e—The accurate differentiation of recurrent glioma from treatment-related changes, such as pseudoprogression or radiation necrosis, is crucial for treatment planning and remains a critical challenge. Fibroblast activation protein (FAP) expressed by cancer-associated fibroblasts can be targeted with PET tracers for in vivo visualization and quantification. This research aims to evaluate the diagnostic and survival predictive efficacy of FAP expression in possible recurrent glioma patients with a head-to-head comparison of [gallium-68] FAP inhibitor-04 and [fluoride-18] fluoroethyl-L-tyrosine PET/CT imaging. 30 post-treatment glioma patients with possible recurrent signs under regular MRI follow-up were enrolled. PET-based semiquantitative parameters and clinical factors were obtained for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e—Univariate logistic regression indicated the initial pathological diagnosis has a borderline differential efficacy (P=0.053). In Multivariate logistic regression analysis of PET-based semiquantitative parameters, MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio showed borderline differential efficacy (P=0.094). When including PET parameters and initial pathological diagnosis, the effectiveness of initial pathological diagnosis was significant (P = 0.045), and the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET \u003c/sub\u003eratio enhanced the area under the receiver operating characteristic curve (AUC) (P=0.040). When the initial diagnosis was replaced with the WHO grade, both the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET \u003c/sub\u003eratio (P=0.081) and the WHO grade (P=0.086) showed borderline efficacy, while the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio improved the AUC (P=0.016). After factoring in age and gender, the initial pathological diagnosis remained significant (P=0.038). Three parameters, including gender (P=0.057), MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P=0.076), and MTV-FAPI (P=0.093), demonstrated borderline efficacy, with the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET \u003c/sub\u003eratio enhancing the AUC (P=0.039). Similarly, after replacing the initial pathological diagnosis with the initial WHO grade, the initial WHO grade (P=0.072), MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio (P=0.079), and gender (P=0.089) presented with borderline differential efficacy, and the MTV\u003csub\u003eFAPI\u003c/sub\u003e:MTV\u003csub\u003eFET\u003c/sub\u003e ratio similarly significantly enhanced the AUC of the model (P=0.046). The survival analysis indicated that MTV-FAPI significantly affects the overall survival (P=0.027, hazard ratio=1.103, 95% CI: 1.011-1.204).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e—This head-to-head study illustrated FAP expression volume percentage of the post-treatment glioma patients has potential in the differentiation between glioma recurrence and treatment-related changes. Glioma FAP expression volume is an independent risk factor that could significantly influence the overall survival of this glioma cohort.\u003c/p\u003e","manuscriptTitle":"Fibroblast activation protein expression in tumour microenvironment is crucial in differentiation and survival prediction of recurrent gliomas: a head-to-head comparison of 68Ga-FAPI-04 and 18F-FET in PET/CT imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 09:12:54","doi":"10.21203/rs.3.rs-6678369/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-06-30T02:38:18+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-06-10T08:26:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T08:23:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-02T15:45:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"EJNMMI Radiopharmacy and Chemistry","date":"2025-05-30T07:01:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"ejnmmi-radiopharmacy-and-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"erpc","sideBox":"Learn more about [EJNMMI Radiopharmacy and Chemistry](http://ejnmmipharmchem.springeropen.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/erpc/default.aspx","title":"EJNMMI Radiopharmacy and Chemistry","twitterHandle":"@officialEANM","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"86226d00-8b27-491c-8ece-da373165a27c","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T16:01:32+00:00","versionOfRecord":{"articleIdentity":"rs-6678369","link":"https://doi.org/10.1186/s41181-025-00378-z","journal":{"identity":"ejnmmi-radiopharmacy-and-chemistry","isVorOnly":false,"title":"EJNMMI Radiopharmacy and Chemistry"},"publishedOn":"2025-08-26 15:57:05","publishedOnDateReadable":"August 26th, 2025"},"versionCreatedAt":"2025-06-13 09:12:54","video":"","vorDoi":"10.1186/s41181-025-00378-z","vorDoiUrl":"https://doi.org/10.1186/s41181-025-00378-z","workflowStages":[]},"version":"v1","identity":"rs-6678369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6678369","identity":"rs-6678369","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}