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Cordova JS, Gurbani SS, Olson JJ, et al. A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma. Tomography. 2016;2(2):106-116doi: 10.18383/j.tom.2016.00136.
Cordova, J. S., Gurbani, S. S., Olson, J. J., Liang, Z., Cooper, L. A., Shu, H. G., Schreibmann, E., Neill, S. G., Hadjipanayis, C. G., Holder, C. A., & Shim, H. (2016). A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma. Tomography (Ann Arbor, Mich.), 2(2), 106-116. https://doi.org/10.18383/j.tom.2016.00136
Cordova, J Scott, et al. "A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma." Tomography (Ann Arbor, Mich.) vol. 2,2 (2016): 106-116. doi: https://doi.org/10.18383/j.tom.2016.00136
Cordova JS, Gurbani SS, Olson JJ, Liang Z, Cooper LA, Shu HG, Schreibmann E, Neill SG, Hadjipanayis CG, Holder CA, Shim H. A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma. Tomography. 2016 Jun;2(2):106-116. doi: 10.18383/j.tom.2016.00136. PMID: 27489883; PMCID: PMC4968944.
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Tomography. 2016 Jun;2(2):106-116. doi: 10.18383/j.tom.2016.00136.

A systematic pipeline for the objective comparison of whole-brain spectroscopic MRI with histology in biopsy specimens from grade III glioma.

Tomography (Ann Arbor, Mich.)

J Scott Cordova, Saumya S Gurbani, Jeffrey J Olson, Zhongxing Liang, Lee A D Cooper, Hui-Kuo G Shu, Eduard Schreibmann, Stewart G Neill, Constantinos G Hadjipanayis, Chad A Holder, Hyunsuk Shim

Affiliations

  1. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
  2. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA; Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA.
  3. Department of Neurosurgery, Emory University School of Medicine; Winship Cancer Institute of Emory University.
  4. Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA; Department of Biomedical informatics, Emory University School of Medicine.
  5. Winship Cancer Institute of Emory University; Department of Radiation Oncology, Emory University School of Medicine.
  6. Department of Radiation Oncology, Emory University School of Medicine.
  7. Department of Pathology, Emory University School of Medicine.
  8. Department of Neurosurgery, Emory University School of Medicine; Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY.
  9. Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA; Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA; Winship Cancer Institute of Emory University.

PMID: 27489883 PMCID: PMC4968944 DOI: 10.18383/j.tom.2016.00136

Abstract

The diagnosis, prognosis, and management of patients with gliomas are largely dictated by the pathological analysis of tissue biopsied from a selected region within the lesion. However, due to the heterogeneous and infiltrative nature of gliomas, identifying the optimal region for biopsy with conventional magnetic resonance imaging (MRI) can be quite difficult. This is especially true for low grade gliomas, which often are non-enhancing tumors. To improve the management of patients with these tumors, the field of neuro-oncology requires an imaging modality that can specifically identify a tumor's most anaplastic/aggressive region(s) for biopsy targeting. The addition of metabolic mapping using spectroscopic MRI (sMRI) to supplement conventional MRI could improve biopsy targeting and, ultimately, diagnostic accuracy. Here, we describe a pipeline for the integration of state-of-the-art, high-resolution whole-brain 3D sMRI maps into a stereotactic neuronavigation system for guiding biopsies in gliomas with nonenhancing components. We also outline a machine-learning method for automated histology analysis that generates normalized, quantitative metrics describing tumor infiltration in immunohistochemically-stained tissue specimens. As a proof of concept, we describe the combination of these two techniques in a small cohort of grade III glioma patients. In this work, we aim to set forth a systematic pipeline to stimulate histopathology-image validation of advanced MRI techniques, such as sMRI.

Keywords: biopsy planning; grade III glioma; image-histology correlation; quantitative histological image analysis; spectroscopic MRI

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