Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities

Abstract

Glioblastoma (GBM) is the most common and highly lethal primary malignant brain tumor in adults. There is a dire need for easily accessible, noninvasive biomarkers that can delineate underlying molecular activities and predict response to therapy. To this end, we sought to identify subtypes of GBM, differentiated solely by quantitative MR imaging features, that could be used for better management of GBM patients. Quantitative image features capturing the shape, texture, and edge sharpness of each lesion were extracted from MR images of 121 patients with de novo, solitary, unilateral GBM. Three distinct phenotypic “clusters” emerged in the development cohort using consensus clustering with 10,000 iterations on these image features. These three clusters—pre-multifocal, spherical, and rim-enhancing, names reflecting their image features—were validated in an independent cohort consisting of 144 multi-institution patients with similar tumor characteristics from The Cancer Genome Atlas (TCGA). Each cluster mapped to a unique set of molecular signaling pathways using pathway activity estimates derived from analysis of TCGA tumor copy number and gene expression data with the PARADIGM algorithm. Distinct pathways, such as c-Kit and FOXA, were enriched in each cluster, indicating differential molecular activities as determined by image features. Each cluster also demonstrated differential probabilities of survival, indicating prognostic importance. Our imaging method offers a noninvasive approach to stratify GBM patients and also provides unique sets of molecular signatures to inform targeted therapy and personalized treatment of GBM.

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

“…Moreover, characteristics of noninvasive magnetic resonance (MR) imaging (most importantly, the apparent diffusion coefficient [ADC] derived from diffusion-weighted MR imaging and the relative cerebral blood volume [CBV] derived from perfusion-weighted MR imaging) also have been shown to be reliable prognostic biomarkers for stratification of patients with glioblastoma (4,5). More recently, the field of radiomics has been introduced to extend the noninvasive study of oncologic tissue beyond established MR imaging metrics, and a large number of quantitative descriptors that reflect textural variations in image intensity, among other features, have been derived from imaging data (6)(7)(8)(9)(10)(11). In the present study, we adopted such a radiomics approach and extracted a large number of radiomic features NEURORADIOLOGY: Radiomic Profiling of Glioblastoma Kickingereder et al and dynamic susceptibility-weighted MR imaging raw data were thresholded to determine regions of prohibitive signal intensity loss near susceptibility interfaces such as the skull base, and the T1 subtraction volume tumor segmentation was modified by means of removal of low-signal regions.…”

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

“…Numerous correlative studies have evaluated other imaging features as potential biomarkers of genetic status. 5,11,[13][14][15][16][17][18][19][20][21] Yet, most have used nonlocalizing biopsies (typically from a small representative subregion) to determine a single genetic profile for an entire tumor. Unfortunately, this technique fails to inform of intratumoral heterogeneity as a whole since the genetic profiles from one biopsy location may not accurately reflect those from other tumor subregions.…”

“…A major reason is that most groups use nonlocalizing biopsies to determine a single representative profile for an entire tumor. 11,[13][14][15][16][17][18][19][20][21] By definition, this does not account for the genetic diversity throughout the various tumor subregions. Also, most biopsies originate from MRI enhancement per routine surgical practice, so tumor profiles from the nonenhancing BAT are typically under-represented.…”

“…The textural patterns between voxel intensities and their surrounding neighbors provide further insight to tissue microstructure and the local environment. 11,12 Numerous studies have correlated both MRI signal and texture analysis with genetic profiles in GBM, 5,11,[13][14][15][16][17][18][19][20][21] yet these have been limited in resolving the challenge of GBM's intratumoral heterogeneity. A major reason is that most groups use nonlocalizing biopsies to determine a single representative profile for an entire tumor.…”

Contrast-enhanced MR Imaging versus Contrast-enhanced US: A Comparison in Glioblastoma Surgery by Using Intraoperative Fusion Imaging

“…All lesions of tumor shapes and many concavities along the tumor outlines, regular and well-circumscribed edges, and central hypointensity encompassed by a hyperintense rim (29) (Figs 1-3 and E1-E3 [online]). The mean maximum lesion diameter was 40 mm (range, 24-63 mm) (Table).…”