Next Article in Journal
Hypoxia Imaging with PET Correlates with Antitumor Activity of the Hypoxia-Activated Prodrug Evofosfamide (TH-302) in Rodent Glioma Models
Previous Article in Journal
Combination of an Integrin-Targeting NIR Tracer and an Ultrasensitive Spectroscopic Device for Intraoperative Detection of Head and Neck Tumor Margins and Metastatic Lymph Nodes
 
 
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 3
10.18383/j.tom.2016.00250
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

Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy

by
Sean D. McGarry
1,
Sarah L. Hurrell
1,
Amy L. Kaczmarowski
1,
Elizabeth J. Cochran
2,
Jennifer Connelly
3,
Scott D. Rand
1,
Kathleen M. Schmainda
1,4 and
Peter S. LaViolette
1,4,*
1
Departments of Radiology, Medical College of Wisconsin, Milwaukee, Milwaukee, WI 53226, USA
2
Departments of Pathology, Medical College of Wisconsin, Milwaukee, Milwaukee, WI 53226, USA
3
Departments of Neurology, Medical College of Wisconsin, Milwaukee, Milwaukee, WI 53226, USA
4
Departments of Biophysics, Medical College of Wisconsin, Milwaukee, Milwaukee, WI 53226, USA
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(3), 223-228; https://doi.org/10.18383/j.tom.2016.00250
Submission received: 11 June 2016 / Revised: 6 July 2016 / Accepted: 2 August 2016 / Published: 1 September 2016
Versions Notes

Abstract

Magnetic resonance imaging (MRI) is used to diagnose and monitor brain tumors. Extracting additional information from medical imaging and relating it to a clinical variable of interest is broadly defined as radiomics. Here, multiparametric MRI radiomic profiles (RPs) of de novo glioblastoma (GBM) brain tumors is related with patient prognosis. Clinical imaging from 81 patients with GBM before surgery was analyzed. Four MRI contrasts were aligned, masked by margins defined by gadolinium contrast enhancement and T2/fluid attenuated inversion recovery hyperintensity, and contoured based on image intensity. These segmentations were combined for visualization and quantification by assigning a 4-digit numerical code to each voxel to indicate the segmented RP. Each RP volume was then compared with overall survival. A combined classifier was then generated on the basis of significant RPs and optimized volume thresholds. Five RPs were predictive of overall survival before therapy. Combining the RP classifiers into a single prognostic score predicted patient survival better than each alone (P < .005). Voxels coded with 1 RP associated with poor prognosis were pathologically confirmed to contain hypercellular tumor. This study applies radiomic profiling to de novo patients with GBM to determine imaging signatures associated with poor prognosis at tumor diagnosis. This tool may be useful for planning surgical resection or radiation treatment margins.
Keywords: radiomics; glioblastoma; prognosis; autopsy radiomics; glioblastoma; prognosis; autopsy

Share and Cite

MDPI and ACS Style

McGarry, S.D.; Hurrell, S.L.; Kaczmarowski, A.L.; Cochran, E.J.; Connelly, J.; Rand, S.D.; Schmainda, K.M.; LaViolette, P.S. Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. Tomography 2016, 2, 223-228. https://doi.org/10.18383/j.tom.2016.00250

AMA Style

McGarry SD, Hurrell SL, Kaczmarowski AL, Cochran EJ, Connelly J, Rand SD, Schmainda KM, LaViolette PS. Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. Tomography. 2016; 2(3):223-228. https://doi.org/10.18383/j.tom.2016.00250

Chicago/Turabian Style

McGarry, Sean D., Sarah L. Hurrell, Amy L. Kaczmarowski, Elizabeth J. Cochran, Jennifer Connelly, Scott D. Rand, Kathleen M. Schmainda, and Peter S. LaViolette. 2016. "Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy" Tomography 2, no. 3: 223-228. https://doi.org/10.18383/j.tom.2016.00250

APA Style

McGarry, S. D., Hurrell, S. L., Kaczmarowski, A. L., Cochran, E. J., Connelly, J., Rand, S. D., Schmainda, K. M., & LaViolette, P. S. (2016). Magnetic Resonance Imaging-Based Radiomic Profiles Predict Patient Prognosis in Newly Diagnosed Glioblastoma Before Therapy. Tomography, 2(3), 223-228. https://doi.org/10.18383/j.tom.2016.00250

Article Metrics

Back to TopTop