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

Leland S Hu, Shuluo Ning, Jennifer M Eschbacher, Nathan Gaw, Amylou C Dueck, Kris A Smith, Peter Nakaji, Jonathan Plasencia, Sara Ranjbar, Stephen J Price, Nhan Tran, Joseph Loftus, Robert Jenkins, Brian P O’Neill, William Elmquist, Leslie C Baxter, Fei Gao, David Frakes, John P Karis, Christine Zwart, Kristin R Swanson, Jann Sarkaria, Teresa Wu, J Ross Mitchell and Jing Li

PLOS ONE, 2015, vol. 10, issue 11, 1-14

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

Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141506 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 41506&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0141506

DOI: 10.1371/journal.pone.0141506

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone (plosone@plos.org).