Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging

Yuan Li; Michelle M Kim; Daniel R Wahl; Theodore S Lawrence; Hemant Parmar; Yue Cao

doi:10.3389/fonc.2021.690036

Survival Prediction Analysis in Glioblastoma With Diffusion Kurtosis Imaging

Front Oncol. 2021 Jul 14:11:690036. doi: 10.3389/fonc.2021.690036. eCollection 2021.

Authors

Yuan Li^{1

2}, Michelle M Kim¹, Daniel R Wahl¹, Theodore S Lawrence¹, Hemant Parmar³, Yue Cao^{1

2

3}

Affiliations

¹ Departments of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.
² Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States.
³ Department of Radiology, University of Michigan, Ann Arbor, MI, United States.

Abstract

Simple summary: Glioblastoma (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.

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 the 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 OS and PFS.

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 and 90 percentile kurtosis values were significant predictors (p ≤ 0.05) for progression free survival (PFS).

Conclusion: The 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.

Keywords: diffusion MRI; diffusion kurtosis imaging; glioblastoma; imaging analysis; survival prediction.