AUTHOR=Li Yuan , Kim Michelle M. , Wahl Daniel R. , Lawrence Theodore S. , Parmar Hemant , Cao Yue 

TITLE=Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.690036

DOI=10.3389/fonc.2021.690036

ISSN=2234-943X

ABSTRACT=Purpose: Non-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 diffusion kurtosis parameters derived from DKI is a significant predictor of overall survival. Methods and Materials: Thirty-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 overall survival (OS). Results: The 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 percentile (p=0.03) and 90 percentile kurtosis (p=0.05) values were still significantly predictors with progression free survival (PFS).  Conclusion: The DKI model demonstrates the potential to predict OS in the patients with GBM. Further development and histopathological validation of the DKI model will warrant its role in clinical management of GBM.