Statistical multiscale mapping of IDH1, MGMT, and microvascular proliferation in human brain tumors from multiparametric MR and spatially-registered core biopsy
We propose a statistical multiscale mapping approach to identify microscopic and molecular heterogeneity across a tumor microenvironment using multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR followed by MR-guided stereotactic core biopsy. The locations of the biopsy cor...
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Main Authors: | , , , , , , , , , |
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Format: | Journal Article |
Language: | English |
Published: |
25-07-2019
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Subjects: | |
Online Access: | Get full text |
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Summary: | We propose a statistical multiscale mapping approach to identify microscopic
and molecular heterogeneity across a tumor microenvironment using
multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR
followed by MR-guided stereotactic core biopsy. The locations of the biopsy
cores were identified in the pre-surgical images using stereotactic bitmaps
acquired during surgery. Feature matrices mapped the multiparametric voxel
values in the vicinity of the biopsy cores to the pathologic outcome variables
for each patient and logistic regression tested the individual and collective
predictive power of the MR contrasts. A non-parametric weighted k-nearest
neighbor classifier evaluated the feature matrices in a leave-one-out cross
validation design across patients. Resulting class membership probabilities
were 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-Hochberg
and random field theory, and the resulting accuracies were compared. The
combination of all five image contrasts correlated with outcome (P<.001) for
all four microscopic variables. The probabilistic mapping method using
Benjamini-Hochberg generated statistically significant results (P<.05) for
three of the four dependent variables: 1) IDH1, 2) MGMT, and 3) microvascular
proliferation, with an average classification accuracy of 0.984 +/- 0.02 and an
average classification sensitivity of 1.567% +/- 0.967. The images corrected by
random field theory demonstrated improved classification accuracy (0.989 +/-
0.008) and classification sensitivity (5.967% +/- 2.857) compared with
Benjamini-Hochberg. Microscopic and molecular tumor properties can be assessed
with statistical confidence across the brain from minimally-invasive, mp-MR. |
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DOI: | 10.48550/arxiv.1907.11161 |