Integrated molecular and multiparametric MRI mapping of high-grade glioma identifies regional biologic signatures.

Sampling restrictions have hindered the comprehensive study of invasive non-enhancing (NE) high-grade glioma (HGG) cell populations driving tumor progression. Here, we present an integrated multi-omic analysis of spatially matched molecular and multi-parametric magnetic resonance imaging (MRI) profiling across 313 multi-regional tumor biopsies, including 111 from the NE, across 68 HGG patients. Whole exome and RNA sequencing uncover unique genomic alterations to unresectable invasive NE tumor, including subclonal events, which inform genomic models predictive of geographic evolution. Infiltrative NE tumor is alternatively enriched with tumor cells exhibiting neuronal or glycolytic/plurimetabolic cellular states, two principal transcriptomic pathway-based glioma subtypes, which respectively demonstrate abundant private mutations or enrichment in immune cell signatures. These NE phenotypes are non-invasively identified through normalized K2 imaging signatures, which discern cell size heterogeneity on dynamic susceptibility contrast (DSC)-MRI. NE tumor populations predicted to display increased cellular proliferation by mean diffusivity (MD) MRI metrics are uniquely associated with EGFR amplification and CDKN2A homozygous deletion. The biophysical mapping of infiltrative HGG potentially enables the clinical recognition of tumor subpopulations with aggressive molecular signatures driving tumor progression, thereby informing precision medicine targeting.

Computer-aided detection of brain tumor invasion using multiparametric MRI.

PURPOSE: To determine the potential of using a computer-aided detection method to intelligently distinguish peritumoral edema alone from peritumor edema consisting of tumor using a combination of high-resolution morphological and physiological magnetic resonance imaging (MRI) techniques available on most clinical MRI scanners. MATERIALS AND METHODS: This retrospective study consisted of patients with two types of primary brain tumors: meningiomas (n = 7) and glioblastomas (n = 11). Meningiomas are typically benign and have a clear delineation of tumor and edema. Glioblastomas are known to invade outside the contrast-enhancing area. Four classifiers of differing designs were trained using morphological, diffusion-weighted, and perfusion-weighted features derived from MRI to discriminate tumor and edema, tested on edematous regions surrounding tumors, and assessed for their ability to detect nonenhancing tumor invasion. RESULTS: The four classifiers provided similar measures of accuracy when applied to the training and testing data. Each classifier was able to identify areas of nonenhancing tumor invasion supported with adjunct images or follow-up studies. CONCLUSION: The combination of features derived from morphological and physiological imaging techniques contains the information necessary for computer-aided detection of tumor invasion and allows for the identification of tumor invasion not previously visualized on morphological, diffusion-weighted, and perfusion-weighted images and maps. Further validation of this approach requires obtaining spatially coregistered tissue samples in a study with a larger sample size.