Diagnostic accuracy of positron emission tomography tracers for the differentiation of tumor progression from treatment-related changes in high-grade glioma:a systematic review and meta-analysis

Background: Post-treatment high-grade gliomas are usually monitored with contrast-enhanced MRI, but its diagnostic accuracy is limited as it cannot adequately distinguish between true tumor progression and treatment-related changes. According to recent response assessment in neuro-oncology (RANO) recommendations PET overcomes this limitation. However, it is currently unknown which tracer yields the best results. Therefore, a systematic review and meta-analysis were performed to compare the diagnostic accuracy of the different PET tracers in differentiating tumor progression from treatment-related changes in high-grade glioma patients. Methods: Pubmed, Web of Science and Embase were searched systematically. Study selection, data extraction and quality assessment were performed independently by two authors. Meta-analysis was performed using a bivariate random effects model when ≥ 5 studies were included. Results: 39 studies (11 tracers) were included in the systematic review. 18F-FDG (12 studies, 171 lesions) showed a pooled sensitivity and specificity of 84% (95%CI 72-92) and 84% (69-93), respectively. 18F-FET (7 studies, 172 lesions) demonstrated a sensitivity of 90% (81-95) and specificity of 85% (71-93). 11C-MET (8 studies, 151 lesions) sensitivity was 93% (80-98) and specificity was 82% (68-91). The number of included studies for the other tracers were too low to combine, but sensitivity and specificity ranged between 93-100% and 0-100% for 18F-FLT, 85-100% and 72-100% for 18F-FDOPA and 100% and 70-88% for 11C-CHO, respectively. Conclusion:18F-FET and 11C-MET, both amino-acid tracers, showed a comparable higher sensitivity than 18F-FDG in the differentiation between tumor progression and treatment-related changes in high-grade glioma patients. The evidence for other tracers is limited, thus 18F-FET and 11C-MET are preferred when available. Our results support the incorporation of amino-acid PET tracers for the treatment evaluation of high-grade gliomas

11C-methyl-L-methionine PET measuring parameters for the diagnosis of tumour progression against radiation-induced changes in brain metastases

OBJECTIVES: Radiation-induced changes (RIC) secondary to focal radiotherapy can imitate tumour progression in brain metastases and make follow-up clinical decision making unreliable. 11C-methyl-L-methionine-PET (MET-PET) is widely used for the diagnosis of RIC in brain metastases, but minimal literature exists regarding the optimum PET measuring parameter to be used. We analysed the diagnostic performance of different MET-PET measuring parameters in distinguishing between RIC and tumour progression in a retrospective cohort of brain metastasis patients. METHODS: 26 patients with 31 metastatic lesions were included on the basis of having undergone a PET scan due to radiological uncertainty of disease progression. The PET images were analysed and methionine uptake quantified using standardised-uptake-values (SUV) and tumour-to-normal tissue (T/N) ratios, generated as SUVmean, SUVmax, SUVpeak, T/Nmean, T/Nmax-mean and T/Npeak-mean. Metabolic-tumour-volume and total-lesion methionine metabolism were also computed. A definitive diagnosis of either RIC or tumour progression was established by clinicoradiological follow-up of least 4 months subsequent to the investigative PET scan. RESULTS: All MET-PET parameters except metabolic-tumour-volume showed statistically significant differences between tumour progression and lesions with RIC. Receiver-operating-characteristic curve and area-under the-curve analysis demonstrated the highest value of 0.834 for SUVmax with a corresponding optimum threshold of 3.29. This associated with sensitivity, specificity, positive predictive and negative predictive values of 78.57, 70.59%, 74.32 and 75.25% respectively. CONCLUSIONS: MET-PET is a useful modality for the diagnosis of RIC in brain metastases. SUVmax was the PET parameter with the greatest diagnostic performance. ADVANCES IN KNOWLEDGE: More robust comparisons between SUVmax and SUVpeak could enhance follow-up treatment planning

Subventricular Zone Involvement Characterized by Diffusion Tensor Imaging in Glioblastoma.

BACKGROUND: Glioblastomas have a poor prognosis, possibly because of a subpopulation of therapy-resistant stem cells within the heterogeneous glioblastoma. Because the subventricular zone is the main source of neural stem cells, we aimed at characterizing the subventricular zone using diffusion tensor imaging (DTI) to show subventricular zone involvement in glioblastoma. METHODS: We prospectively included 93 patients with primary glioblastomas who underwent preoperative DTI. The nonenhancing high fluid-attenuated inversion recovery (FLAIR) signal was used to describe the infiltrative tumor margin. We used a 5-mm margin surrounding the lateral ventricles to define the subventricular zone. The subventricular zone with high FLAIR was compared with the subventricular zone without high FLAIR, control high FLAIR outside the subventricular zone and control contralateral normal-appearing white matter. Normalized DTI parameters were calculated and compared between the different regions. RESULTS: The subventricular zone with high FLAIR showed increased isotropic p values compared with the subventricular zone without high FLAIR (t126 = 3.9; P < 0.001) and control regions (t179 = 1.9; P = 0.046). Anisotropic q and fractional anisotropy values were lower in regions with high FLAIR compared with the subventricular zone without high FLAIR (t181 = 11.6, P < 0.001 and t184 =12.4, P < 0.001, respectively). CONCLUSION: DTI data showed that the subventricular zone is involved in glioblastoma with increased isotropic p values in the subventricular zone with high FLAIR, indicating tumor infiltration.This study was funded by a National Institute of Health Clinician Scientist Fellowship (S.P.), the Groningen University Fund (B.D.), the Marco Polo fund (B.D.), and grants from the Chang Gung Medical Foundation and Chang Gung Memorial Hospital, Keelung, Taiwan (J.Y.). The authors declare to have no conflicts of interest. This article presents independent research funded by the United Kingdom National Institute for Health Research. The views expressed are those of the author(s) and are not necessarily those of the United Kingdom National Health Service, the United Kingdom National Institute for Health Research, or the United Kingdom Department of Health

Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals

Glioblastoma is highly heterogeneous in microstructure and vasculature, creating various tumor microenvironments among patients, which may lead to different phenotypes. The purpose was to interrogate the interdependence of microstructure and vasculature using perfusion and diffusion imaging and to investigate the utility of this approach in tumor invasiveness assessment. A total of 115 primary glioblastoma patients were prospectively recruited for preoperative magnetic resonance imaging (MRI) and surgery. Apparent diffusion coefficient (ADC) was calculated from diffusion imaging, and relative cerebral blood volume (rCBV) was calculated from perfusion imaging. The empirical copula transform was applied to ADC and rCBV voxels in the contrast-enhancing tumor region to obtain their joint distribution, which was discretized to extract second-order features for an unsupervised hierarchical clustering. The lactate levels of patient subgroups, measured by MR spectroscopy, were compared. Survivals were analyzed using Kaplan-Meier and multivariate Cox regression analyses. The results showed that three patient subgroups were identified by the unsupervised clustering. These subtypes showed no significant differences in clinical characteristics but were significantly different in lactate level and patient survivals. Specifically, the subtype demonstrating high interdependence of ADC and rCBV displayed a higher lactate level than the other two subtypes (P = .016 and P = .044, respectively). Both subtypes of low and high interdependence showed worse progression-free survival than the intermediate (P = .046 and P = .009 respectively). Our results suggest that the interdependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness, providing overall measure of tumor microenvironment using multiparametric MRI

Decoding the Interdependence of Multiparametric Magnetic Resonance Imaging to Reveal Patient Subgroups Correlated with Survivals.

Glioblastoma is highly heterogeneous in microstructure and vasculature, creating various tumor microenvironments among patients, which may lead to different phenotypes. The purpose was to interrogate the interdependence of microstructure and vasculature using perfusion and diffusion imaging and to investigate the utility of this approach in tumor invasiveness assessment. A total of 115 primary glioblastoma patients were prospectively recruited for preoperative magnetic resonance imaging (MRI) and surgery. Apparent diffusion coefficient (ADC) was calculated from diffusion imaging, and relative cerebral blood volume (rCBV) was calculated from perfusion imaging. The empirical copula transform was applied to ADC and rCBV voxels in the contrast-enhancing tumor region to obtain their joint distribution, which was discretized to extract second-order features for an unsupervised hierarchical clustering. The lactate levels of patient subgroups, measured by MR spectroscopy, were compared. Survivals were analyzed using Kaplan-Meier and multivariate Cox regression analyses. The results showed that three patient subgroups were identified by the unsupervised clustering. These subtypes showed no significant differences in clinical characteristics but were significantly different in lactate level and patient survivals. Specifically, the subtype demonstrating high interdependence of ADC and rCBV displayed a higher lactate level than the other two subtypes (P = .016 and P = .044, respectively). Both subtypes of low and high interdependence showed worse progression-free survival than the intermediate (P = .046 and P = .009 respectively). Our results suggest that the interdependence between perfusion and diffusion imaging may be useful in stratifying patients and evaluating tumor invasiveness, providing overall measure of tumor microenvironment using multiparametric MRI

Low perfusion compartments in glioblastoma quantified by advanced magnetic resonance imaging and correlated with patient survival.

BACKGROUND AND PURPOSE: Glioblastoma exhibits profound intratumoral heterogeneity in perfusion. Particularly, low perfusion may induce treatment resistance. Thus, imaging approaches that define low perfusion compartments are crucial for clinical management. MATERIALS AND METHODS: A total of 112 newly diagnosed glioblastoma patients were prospectively recruited for maximal safe resection. The apparent diffusion coefficient (ADC) and relative cerebral blood volume (rCBV) were calculated from diffusion and perfusion imaging, respectively. Based on the overlapping regions of lowest rCBV quartile (rCBVL) with the lowest ADC quartile (ADCL) and highest ADC quartile (ADCH) in each tumor, two low perfusion compartments (ADCH-rCBVL and ADCL-rCBVL) were identified for volumetric analysis. Lactate and macromolecule/lipid levels were determined from multivoxel MR spectroscopic imaging. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier's and multivariate Cox regression analyses, to evaluate the effects of compartment volume and lactate level on survival. RESULTS: Two compartments displayed higher lactate and macromolecule/lipid levels compared to contralateral normal-appearing white matter (each P < 0.001). The proportion of the ADCL-rCBVL compartment in the contrast-enhancing tumor was associated with a larger infiltration on FLAIR (P < 0.001, rho = 0.42). The minimally invasive phenotype displayed a lower proportion of the ADCL-rCBVL compartment than the localized (P = 0.031) and diffuse phenotypes (not significant). Multivariate Cox regression showed higher lactate level in the ADCL-rCBVL compartment was associated with worsened survival (PFS: HR 2.995, P = 0.047; OS: HR 4.974, P = 0.005). CONCLUSIONS: Our results suggest that the ADCL-rCBVL compartment may potentially indicate a clinically measurable resistant compartment