Background. Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments. Methods. We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV). Results. We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A,

Purpose This study investigates amide proton transfer (APT) and nuclear overhauser enhancement (NOE) in phantoms and 9L tumors in rat brains at 9.4 Tesla, using a recently developed method that can isolate different contributions to exchange. Methods Chemical exchange rotation transfer (CERT) was used to quantify APT and NOEs through subtraction of signals acquired at two irradiation flip angles, but with the same average irradiation power. Results CERT separates and quantifies specific APT and NOE signals without contamination from other proton pools, and thus overcomes a key shortcoming of conventional CEST asymmetry approaches. CERT thus has increased specificity, though at the cost of decreased signal strength. In vivo experiments show that the APT effect acquired with CERT in 9L rat tumors (3.1%) is relatively greater than that in normal tissue (2.5%), which is consistent with previous CEST asymmetry analysis. The NOE effect centered at −1.6 ppm shows substantial image contrast within the tumor and between the tumor and the surrounding tissue, while the NOE effect centered at −3.5 ppm shows little contrast. Conclusion CERT provides an image contrast that is more specific to chemical exchange than conventional APT by means of asymmetric CEST Z-spectra analysis.

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Advances in hardware and software have enabled the realization of clinically feasible, quantitative multimodality imaging of tissue pathophysiology. Earlier efforts relating to multimodality imaging of cancer have focused on the integration of anatomical and functional characteristics, such as PET–CT and single-photon emission CT (SPECT–CT), whereas more-recent advances and applications have involved the integration of multiple quantitative, functional measurements (for example, multiple PET tracers, varied MRI contrast mechanisms, and PET–MRI), thereby providing a more-comprehensive characterization of the tumour phenotype. The enormous amount of complementary quantitative data generated by such studies is beginning to offer unique insights into opportunities to optimize care for individual patients. Although important technical optimization and improved biological interpretation of multimodality imaging findings are needed, this approach can already be applied informatively in clinical trials of cancer therapeutics using existing tools. These concepts are discussed herein.

Dynamic susceptibility contrast (DSC) MRI methods rely on the compartmentalization of the contrast agent such that a susceptibility gradient can be induced between the contrast-containing compartment and adjacent spaces, such as between intravascular and extravascular spaces. When there is a disruption of the blood brain barrier, as is frequently the case with brain tumors, contrast agent leaks out of the vasculature, resulting in additional T1, T2, and T2∗ relaxation effects in the extravascular space, thereby affecting the signal intensity time course and reducing the reliability of the computed hemodynamic parameters. In this study, a theoretical model describing these dynamic intra- and extravascular T1, T2, and T2∗ relaxation interactions is proposed. The applicability of using the proposed model to investigate the influence of relevant MRI pulse sequence (e.g., echo time, flip angle), physical (e.g., susceptibility calibration factors, pre-contrast relaxation rates) and physiological parameters (e.g., permeability, blood flow, compartmental volume fractions) on DSC-MRI signal time curves is demonstrated. Such a model could yield important insights into the biophysical basis of contrast agent extravasastion induced effects on measured DSC-MRI signals and provide a means to investigate pulse sequence optimization and appropriate data analysis methods for the extraction of physiologically relevant imaging metrics.