BACKGROUND: Understanding how mechanical properties relate to functional changes in glioblastomas may help explain variation between patients.
PURPOSE: To map differences in biomechanical and functional properties between tumor and healthy tissue, to study their spatial distribution, and to assess any relationship between biomechanical and functional properties.
STUDY TYPE: Prospective.
SUBJECTS: Nine patients with glioblastoma, 17 healthy subjects
FIELD STRENGTH/SEQUENCE: 3T, MRE, DSC, DTI, ASL
ASSESSMENT: Stiffness and viscosity measurements G′ and G″, cerebral blood flow (CBF), apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were measured in patients′ contrast-enhancing tumor, necrosis, edema, and gray and white matter, and in gray and white matter for healthy subjects.
STATISTICAL TESTS: Voxel-wise regression analysis using a linear and a random forest model for CBF as a function of ADC, FA, G′ and G″. Model performance was evaluated by root-mean-square error with a leave-one-patient-out cross-validation strategy. A paired Wilcoxon signed-rank test was used for comparisons of different regions and models. A significance level of P<0.05 was assumed for all tests.
RESULTS: Median G′ and G″ in contrast-enhancing tumor were 15 % and 39 % lower than in normal-appearing white matter (cNAWM), respectively (P<0.01). FA was 53 % lower in tumor compared to cNAWM (P<0.01). ADC and CBF were 50 % and 2.9 times higher in tumor than in cNAWM, respectively (P<0.01). For both models, prediction of CBF was improved by adding MRE measurements to the model, compared to a baseline model with ADC and FA as predictors (P<0.05).
DATA CONCLUSION: Tumors differed from healthy tissue with regard to G′ and G″, CBF, ADC and FA, with heterogeneity both between patients and within tumors. Measurements approached values in normal-appearing tissue when moving outward from the tumor core, but abnormal tissue properties were still present in regions of normal-appearing tissue. The inclusion of MRE measurements in statistical models helped predict perfusion, with stiffer tissue associated with lower perfusion values.