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Author Notes:

Corresponding Author: Hyunsuk Shim, PhD Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1701 Uppergate Drive, C5018, Atlanta, GA 30322; E-mail: hshim@emory.edu

The spectroscopic sequence and MIDAS were provided by Andrew Maudsley at the University of Miami, and his group has been crucial to pipeline optimization.

Conflict of Interest: None reported.


Research Funding:

This work was supported by the National Institute of Health grant U01CA172027 (HKS/JJO/HS) and a predoctoral fellowship F31CA180319 (JSC).


  • biopsy planning
  • grade III glioma
  • image-histology correlation
  • quantitative histological image analysis
  • spectroscopic MRI

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

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Journal Title:

Tomography : a journal for imaging research


Volume 2, Number 2


, Pages 106-116

Type of Work:

Article | Final Publisher PDF


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.

Copyright information:

©2016 The Authors.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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