Glioblastoma Recurrence Versus Radiotherapy Injury

Abstract

PurposeTo evaluate the diagnostic potential of decision-tree model of diffusion kurtosis imaging (DKI) and 11C-methionine (11C-MET) PET, for the differentiation of radiotherapy (RT) injury from glioblastoma recurrence.MethodsEighty-six glioblastoma cases with suspected lesions after RT were retrospectively enrolled. Based on histopathology or follow-up, 48 patients were diagnosed with local glioblastoma recurrence, and 38 patients had RT injury between April 2014 and December 2019. All the patients underwent PET/MRI examinations. Multiple parameters were derived based on the ratio of tumor to normal control (TNR), including SUVmax and SUVmean, mean value of kurtosis and diffusivity (MK, MD) from DKI, and histogram parameters. The diagnostic models were established by decision trees. Receiver operating characteristic analysis was used for evaluating the diagnostic accuracy of each independent parameter and all the diagnostic models.ResultsThe intercluster correlations of DKI, PET, and texture parameters were relatively weak, whereas the intracluster correlations were strong. Compared with models of DKI alone (sensitivity =1.00, specificity = 0.70, area under the curve [AUC] = 0.85) and PET alone (sensitivity = 0.83, specificity = 0.90, AUC = 0.89), the combined model demonstrated the best diagnostic accuracy (sensitivity = 1.00, specificity = 0.90, AUC = 0.95).ConclusionsDiffusion kurtosis imaging, 11C-MET PET, and histogram parameters provide complementary information about tissue. The decision-tree model combined with these parameters has the potential to further increase diagnostic accuracy for the discrimination between RT injury and glioblastoma recurrence over the standard Response Assessment in Neuro-Oncology criteria. 11C-MET PET/MRI may thus contribute to the management of glioblastoma patients with suspected lesions after RT.

Hybrid PET/MRI in Cerebral Glioma: Current Status and Perspectives

“…In this study, the 18 F-FET PET-guided evaluation of kurtosis achieved an AUC of 0.87 (MK-C90), 18 F-FET uptake an AUC of 0.77 (TBR max ), and the combination of the two methods achieved an AUC of 0.97 to differentiate recurrent tumours from treatment-related changes (Figures 2 and 3). These data were confirmed by a recent study including 87 patients with suspected recurrent glioblastoma using 11 C-MET [110]. In that study, combined 11 C-MET PET and DKI achieved an AUC of 0.95 to differentiate glioblastoma recurrence from radiation injury compared with an AUC of 0.89 for PET or 0.85 for DKI alone.…”

Hybrid18F-Fluoroethyltyrosine PET and MRI with Perfusion to Distinguish Disease Progression from Treatment-Related Change in Malignant Brain Tumors: The Quest to Beat the Toughest Cases

“…Overall, the institutional diagnostic accuracy mirrors that of 18 F-FET PET alone. Hybrid imaging exceeded the performance of perfusion MRI alone, but the impact of other useful MRI techniques, such as diffusionweighted imaging, MR spectroscopy, and kurtosis imaging, was not evaluated in this study (19,(39)(40)(41).…”

A comparison study of dynamic [18F]Alfatide II imaging and [11C]MET in orthotopic rat models of glioblastoma

“…Firstly, [ 11 C]MET faces significant logistical challenges in clinical applications due to its short half-life (approximately 20 min), limiting its widespread use in medical centers without onsite radioisotope production facilities. Secondly, both [ 11 C]MET and [ 18 F]FET struggle to differentiate between tumor recurrence and post-radiation changes, as both conditions may show enhanced uptake, increasing the risk of misdiagnosis (Dang et al 2022 ). Moreover, [ 11 C]MET may also demonstrate increased uptake in non-tumorous lesions, such as inflammation or infection, further complicating its interpretation (Hotta et al 2019 ; Mattoli et al 2021 ).…”

“…Additionally, the increased uptake of [ 11 C]MET can also be observed in areas of inflammation, infection, and even in some benign brain lesions, which may lead to false positive diagnosis of GBM (Kim et al 2022 ). This is also a challenge in accurately assessing treatment response and identifying tumor recurrence, particularly in radiation encephalopathy or pseudoprogression (Dang et al 2022 ; Pessina et al 2021 ; van Dijken et al 2022 ).…”