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We propose a statistical multiscale mapping approach to identify microscopicand molecular heterogeneity across a tumor microenvironment usingmultiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MRfollowed by MR-guided stereotactic core biopsy. The locations of the biopsycores were identified in the pre-surgical images using stereotactic bitmapsacquired during surgery. Feature matrices mapped the multiparametric voxelvalues in the vicinity of the biopsy cores to the pathologic outcome variablesfor each patient and logistic regression tested the individual and collectivepredictive power of the MR contrasts. A non-parametric weighted k-nearestneighbor classifier evaluated the feature matrices in a leave-one-out crossvalidation design across patients. Resulting class membership probabilitieswere converted to chi-square statistics to develop full-brain parametric maps,implementing Gaussian random field theory to estimate inter-voxel dependencies.Corrections for family-wise error rates were performed using Benjamini-Hochbergand random field theory, and the resulting accuracies were compared. Thecombination of all five image contrasts correlated with outcome (P<.001) forall four microscopic variables. The probabilistic mapping method usingBenjamini-Hochberg generated statistically significant results (P<.05) forthree of the four dependent variables: 1) IDH1, 2) MGMT, and 3) microvascularproliferation, with an average classification accuracy of 0.984 +/- 0.02 and anaverage classification sensitivity of 1.567% +/- 0.967. The images corrected byrandom field theory demonstrated improved classification accuracy (0.989 +/-0.008) and classification sensitivity (5.967% +/- 2.857) compared withBenjamini-Hochberg. Microscopic and molecular tumor properties can be assessedwith statistical confidence across the brain from minimally-invasive, mp-MR.

Statistical multiscale mapping of IDH1, MGMT, and microvascular  proliferation in human brain tumors from multiparametric MR and  spatially-registered core biopsy