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PLoS One. 2015 Nov 24;10(11):e0141506. doi: 10.1371/journal.pone.0141506. eCollection 2015.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma.

Author information

1
Department of Radiology, Mayo Clinic, Phoenix, Arizona, United States of America.
2
Department of Radiology, Barrow Neurological Institute, Phoenix, Arizona, United States of America.
3
School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America.
4
Department of Pathology, Barrow Neurological Institute, Phoenix, Arizona, United States of America.
5
Department of Biostatistics, Mayo Clinic, Phoenix, Arizona, United States of America.
6
Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, United States of America.
7
School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America.
8
Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.
9
Department of Cancer and Cell Biology, Translational Genomics Research Institute, Phoenix, Arizona, United States of America.
10
Department of Cancer and Cell Biology, Mayo Clinic, Scottsdale, AZ, United States of America.
11
Department of Pathology, Mayo Clinic, Rochester, Minnesota, United States of America.
12
Department of Neuro-oncology, Mayo Clinic, Rochester, Minnesota, United States of America.
13
Department of Pharmacology, University of Minnesota, Minneapolis, Minnesota, United States of America.
14
Department of Neurosurgery, Mayo Clinic, Phoenix, Arizona, United States of America.
15
Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, United States of America.

Abstract

BACKGROUND:

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.

METHODS:

We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.

RESULTS:

We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).

CONCLUSION:

Multi-parametric MRI and texture analysis can help characterize and visualize GBM's spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

PMID:
26599106
PMCID:
PMC4658019
DOI:
10.1371/journal.pone.0141506
[Indexed for MEDLINE]
Free PMC Article

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