Multiparameter MRI Predictors of Long-Term Survival in Glioblastoma Multiforme

Olya Stringfield,Kristin R Swanson,Noah C Peeri,Nicolas G Rognin,Kamala R Clark-Swanson,Robert A Gatenby,Natarajan Raghunand,Yoganand Balagurunathan,Kathleen M Egan,Sandra K Johnston,Pamela R Jackson,John A Arrington

doi:10.18383/j.tom.2018.00052

Abstract

Standard-of-care multiparameter magnetic resonance imaging (MRI) scans of the brain were used to objectively subdivide glioblastoma multiforme (GBM) tumors into regions that correspond to variations in blood flow, interstitial edema, and cellular density. We hypothesized that the distribution of these distinct tumor ecological “habitats” at the time of presentation will impact the course of the disease. We retrospectively analyzed initial MRI scans in 2 groups of patients diagnosed with GBM, a long-term survival group comprising subjects who survived >36 month postdiagnosis, and a short-term survival group comprising subjects who survived ≤19 month postdiagnosis. The single-institution discovery cohort contained 22 subjects in each group, while the multi-institution validation cohort contained 15 subjects per group. MRI voxel intensities were calibrated, and tumor voxels clustered on contrast-enhanced T1-weighted and fluid-attenuated inversion-recovery (FLAIR) images into 6 distinct “habitats” based on low- to medium- to high-contrast enhancement and low–high signal on FLAIR scans. Habitat 6 (high signal on calibrated contrast-enhanced T1-weighted and FLAIR sequences) comprised a significantly higher volume fraction of tumors in the long-term survival group (discovery cohort, 35% ± 6.5%; validation cohort, 34% ± 4.8%) compared with tumors in the short-term survival group (discovery cohort, 17% ± 4.5%, P < .03; validation cohort, 16 ± 4.0%, P < .007). Of the 6 distinct MRI-defined habitats, the fractional tumor volume of habitat 6 at diagnosis was significantly predictive of long- or short-term survival. We discuss a possible mechanistic basis for this association and implications for habitat-driven adaptive therapy of GBM.

Highlights

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological spatial scales [1]
Machine learning on patterns in standard brain magnetic resonance imaging (MRI) images, and parameter maps from diffusion tensor imaging, and dynamic susceptibility contrast-enhanced (DSC)-MRI have been reported to correlate with molecular subtype and survival in newly diagnosed patients with GBM [6]
(mean Ϯ S.E.M. ϭ 35% Ϯ 6.5%; n ϭ 22) compared with short-term survivors (Figure 4A). This finding was replicated in the validation cohort (P Ͻ .007), with habitat 6 comprising 34% Ϯ 4.8% (n ϭ 15) of the tumor volume in long-term survival (LTS) subjects compared with 16% Ϯ 4.0% (n ϭ 15) of the tumor volume in short-term survival (STS) subjects (Figure 4B)

Summary

Introduction

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological spatial scales [1]. Canoll et al used RNA-sequencing and histological analysis of image-guided biopsies to show differences in cellular and molecular markers between tissue taken from the contrast-enhancing (CE) core and that from the nonenhancing (NE) margins of GBM tumors [4]. Characteristic metabolic differences between the CE and NE regions in GBM have been identified by 1H magnetic resonance spectroscopy [5]. Radiogenomic analysis informed by spatially localized biopsies has identified spatially complex distributions of molecularly distinct subpopulations in GBMs [7]. Such spatial variations in expression of molecular and pathologic markers, metabolism, and radiologic imaging patterns are known to exist in all solid tumors, the origin and the clinical significance of this heterogeneity remain subjects of investigation

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