Abstract
Simple SummaryGlioblastoma (GBM) is the most common and aggressive primary brain tumor. Diffusion kurtosis imaging (DKI) has characterized non-Gaussian diffusion behaviors in brain normal tissue and gliomas, but there are very limited efforts in investigating treatment responses of kurtosis in GBM. This study aimed to investigate whether any parameter derived from the DKI is a significant predictor of overall survival (OS). We found that the large mean, 80 and 90 percentile kurtosis values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1-weighted images pre-RT were significantly associated with reduced OS. In the multivariate Cox model, the mean kurtosis Gd-GTV pre-RT after considering effects of age, extent of surgery, and methylation were significant predictors of OS. In addition, the 80 and 90 percentile kurtosis values in Gd-GTV post RT were significantly associated with progression free survival (PFS). The DKI model demonstrates the potential to predict outcomes in the patients with GBM.PurposeNon-Gaussian diffusion behaviors in gliomas have been characterized by diffusion kurtosis imaging (DKI). But there are very limited efforts in investigating the kurtosis in glioblastoma (GBM) and its prognostic and predictive values. This study aimed to investigate whether any of the diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival.Methods and MaterialsThirty-three patients with GBM had pre-radiation therapy (RT) and mid-RT diffusion weighted (DW) images. Kurtosis and diffusion coefficient (DC) values in the contrast enhanced gross tumor volume (Gd-GTV) on post-Gd T1 weighted images pre-RT and mid-RT were calculated. Univariate and multivariate Cox models were used to evaluate the DKI parameters and clinical factors for prediction of OS and PFS.ResultsThe large mean kurtosis values in the Gd-GTV pre-RT were significantly associated with reduced OS (p = 0.02), but the values at mid-RT were not (p > 0.8). In the multivariate Cox model, the mean kurtosis in the Gd-GTV pre-RT (p = 0.009) was still a significant predictor of OS after adjusting effects of age, O6-Methylguanine-DNA Methyl transferase (MGMT) methylation and extent of resection. In Gd-GTV post-RT, 80 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).ConclusionThe DKI model demonstrates the potential to predict OS and PFS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.
Highlights
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults and has a poor prognosis with a median survival of approximately 14 months despite multimodality therapy with surgery, concurrent chemoradiation therapy, and adjuvant chemotherapy [1, 2]
The ten patients were treated based upon the institution protocol of concurrent chemoradiation therapy (CRT) following chemotherapy with a median dose of 60 Gy (40.05–72 Gy), and the 23 patients were enrolled on a prospective radiation boosting clinical trial and treated to 75 Gy (NCT02805179) [22]
Thirty-three patients who had newly diagnosed GBM treated between October 2012 and December 2018 and had the diffusion imaging scans patients with GBM before radiation therapy (pre-RT), mid-RT and post-RT as described in the section In Vivo MR Imaging were included in this analysis
Summary
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults and has a poor prognosis with a median survival of approximately 14 months despite multimodality therapy with surgery, concurrent chemoradiation therapy, and adjuvant chemotherapy [1, 2]. Standard clinical assessment of tumor progression or therapy response [3] is based primarily on post-contrast T1-weighted and fluid-attenuated inversion recovery (FLAIR) T2-weighted magnetic resonance images (MRI). There are some challenges to these conventional techniques. The contrast enhancement on the post-contrast T1-weighted MRI is affected by tumor growth, and radiation, anti-angiogenesis drugs, and chemotherapy, all of which can be attributed to blood–brain barrier disruption. Abnormality on T2 FLAIR images is influenced by T2 changes of tumor cells as well as by edema that co-exists within GBM or is affected by radiation therapy.
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