Next Article in Journal
An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features
Previous Article in Journal
Test–Retest Data for Radiomics Feature Stability Analysis: Generalizable or Study-Specific?
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Tomography
Volume 2
Issue 4
10.18383/j.tom.2016.00187
tomography-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma

by
J. Scott Cordova
1,
Shravan Kandula
2,3,
Saumya Gurbani
1,6,
Jim Zhong
2,
Mital Tejani
2,
Oluwatosin Kayode
2,
Kirtesh Patel
2,
Roshan Prabhu
4,
Eduard Schreibmann
2,
Ian Crocker
2,5,
Chad A. Holder
1,
Hyunsuk Shim
1,2,5,6 and
Hui-Kuo Shu
2,5,*
1
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA
2
Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA, USA
3
Florida Hospital Medical Group, Radiation Oncology Associates, Orlando, FL, USA
4
SE Radiation Oncology Group, Levine Cancer Institute, Charlotte, NC, USA
5
Winship Cancer Institute, Atlanta, GA, USA
6
Department of Biomedical Engineering, GA Institute of Technology, Atlanta, GA, USA
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 366-373; https://doi.org/10.18383/j.tom.2016.00187
Submission received: 7 September 2016 / Revised: 5 October 2016 / Accepted: 9 November 2016 / Published: 1 December 2016
Versions Notes

Abstract

Due to glioblastoma's infiltrative nature, an optimal radiation therapy (RT) plan requires targeting infiltration not identified by anatomical magnetic resonance imaging (MRI). Here, high-resolution, whole-brain spectroscopic MRI (sMRI) is used to describe tumor infiltration alongside anatomical MRI and simulate the degree to which it modifies RT target planning. In 11 patients with glioblastoma, data from preRT sMRI scans were processed to give high-resolution, whole-brain metabolite maps normalized by contralateral white matter. Maps depicting choline to N-Acetylaspartate (Cho/NAA) ratios were registered to contrast-enhanced T1-weighted RT planning MRI for each patient. Volumes depicting metabolic abnormalities (1.5-, 1.75-, and 2.0-fold increases in Cho/NAA ratios) were compared with conventional target volumes and contrast-enhancing tumor at recurrence. sMRI-modified RT plans were generated to evaluate target volume coverage and organ-at-risk dose constraints. Conventional clinical target volumes and Cho/NAA abnormalities identified significantly different regions of microscopic infiltration with substantial Cho/NAA abnormalities falling outside of the conventional 60 Gy isodose line (41.1, 22.2, and 12.7 cm3, respectively). Clinical target volumes using Cho/NAA thresholds exhibited significantly higher coverage of contrast enhancement at recurrence on average (92.4%, 90.5%, and 88.6%, respectively) than conventional plans (82.5%). sMRI-based plans targeting tumor infiltration met planning objectives in all cases with no significant change in target coverage. In 2 cases, the sMRI-modified plan exhibited better coverage of contrast-enhancing tumor at recurrence than the original plan. Integration of the high-resolution, whole-brain sMRI into RT planning is feasible, resulting in RT target volumes that can effectively target tumor infiltration while adhering to conventional constraints.
Keywords: glioblastoma; magnetic resonance spectroscopy; spectroscopic MRI; radiation therapy planning; metabolic regions-of-interest glioblastoma; magnetic resonance spectroscopy; spectroscopic MRI; radiation therapy planning; metabolic regions-of-interest

Share and Cite

MDPI and ACS Style

Cordova, J.S.; Kandula, S.; Gurbani, S.; Zhong, J.; Tejani, M.; Kayode, O.; Patel, K.; Prabhu, R.; Schreibmann, E.; Crocker, I.; et al. Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma. Tomography 2016, 2, 366-373. https://doi.org/10.18383/j.tom.2016.00187

AMA Style

Cordova JS, Kandula S, Gurbani S, Zhong J, Tejani M, Kayode O, Patel K, Prabhu R, Schreibmann E, Crocker I, et al. Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma. Tomography. 2016; 2(4):366-373. https://doi.org/10.18383/j.tom.2016.00187

Chicago/Turabian Style

Cordova, J. Scott, Shravan Kandula, Saumya Gurbani, Jim Zhong, Mital Tejani, Oluwatosin Kayode, Kirtesh Patel, Roshan Prabhu, Eduard Schreibmann, Ian Crocker, and et al. 2016. "Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma" Tomography 2, no. 4: 366-373. https://doi.org/10.18383/j.tom.2016.00187

APA Style

Cordova, J. S., Kandula, S., Gurbani, S., Zhong, J., Tejani, M., Kayode, O., Patel, K., Prabhu, R., Schreibmann, E., Crocker, I., Holder, C. A., Shim, H., & Shu, H. -K. (2016). Simulating the Effect of Spectroscopic MRI as a Metric for Radiation Therapy Planning in Patients with Glioblastoma. Tomography, 2(4), 366-373. https://doi.org/10.18383/j.tom.2016.00187

Article Metrics

Back to TopTop