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Tomography
Volume 6
Issue 2
10.18383/j.tom.2020.00012
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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.
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Article

Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge

by
Laura C. Bell
1,
Natenael Semmineh
1,
Hongyu An
2,
Cihat Eldeniz
2,
Richard Wahl
2,
Kathleen M. Schmainda
3,4,
Melissa A. Prah
4,
Bradley J. Erickson
5,
Panagiotis Korfiatis
5,
Chengyue Wu
6,
Anna G. Sorace
7,
Thomas E. Yankeelov
6,
Neal Rutledge
6,
Thomas L. Chenevert
8,
Dariya Malyarenko
8,
Yichu Liu
9,
Andrew Brenner
9,
Leland S. Hu
10,
Yuxiang Zhou
10,
Jerrold L. Boxerman
11,
Yi-Fen Yen
12,
Jayashree Kalpathy-Cramer
12,
Andrew L. Beers
12,
Mark Muzi
13,
Ananth J. Madhuranthakam
14,
Marco Pinho
14,
Brian Johnson
14,15 and
C. Chad Quarles
1,*add Show full author list remove Hide full author list
1
Division of Neuroimaging Research, Barrow Neurological Institute, 350WThomas Rd, Phoenix, AZ 85013-4409, USA
2
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
3
Departments of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
4
Departments of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
5
Department of Radiology, Mayo Clinic, Rochester, MN, USA
6
Oden Institute for Computational Engineering and Sciences, Departments of Biomedical Engineering, Diagnostic Medicine, and Oncology, Livestrong Cancer Institutes, University of Texas at Austin, Austin, TX, USA
7
Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA
8
Department of Radiology, University of Michigan, Ann Arbor, MI, USA
9
UT Health San Antonio, San Antonio, TX, USA
10
Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA
11
Department of Diagnostic Imaging, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, RI, USA
12
Department of Radiology, MGH—Martinos Center for Biomedical Imaging, Boston, MA, USA
13
Radiology, University of Washington, Seattle, WA, USA
14
Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
15
Philips Healthcare, Gainesville, FL, USA
 
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*
Author to whom correspondence should be addressed.
Tomography 2020, 6(2), 203-208; https://doi.org/10.18383/j.tom.2020.00012
Submission received: 7 March 2020 / Revised: 7 April 2020 / Accepted: 5 May 2020 / Published: 1 June 2020
Versions Notes

Abstract

We have previously characterized the reproducibility of brain tumor relative cerebral blood volume (rCBV) using a dynamic susceptibility contrast magnetic resonance imaging digital reference object across 12 sites using a range of imaging protocols and software platforms. As expected, reproducibility was highest when imaging protocols and software were consistent, but decreased when they were variable. Our goal in this study was to determine the impact of rCBV reproducibility for tumor grade and treatment response classification. We found that varying imaging protocols and software platforms produced a range of optimal thresholds for both tumor grading and treatment response, but the performance of these thresholds was similar. These findings further underscore the importance of standardizing acquisition and analysis protocols across sites and software benchmarking.
Keywords: DSC-MRI; digital reference object; relative cerebral blood volume; standardization; multisite consistency; tumor grading; treatment response DSC-MRI; digital reference object; relative cerebral blood volume; standardization; multisite consistency; tumor grading; treatment response

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MDPI and ACS Style

Bell, L.C.; Semmineh, N.; An, H.; Eldeniz, C.; Wahl, R.; Schmainda, K.M.; Prah, M.A.; Erickson, B.J.; Korfiatis, P.; Wu, C.; et al. Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge. Tomography 2020, 6, 203-208. https://doi.org/10.18383/j.tom.2020.00012

AMA Style

Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, Prah MA, Erickson BJ, Korfiatis P, Wu C, et al. Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge. Tomography. 2020; 6(2):203-208. https://doi.org/10.18383/j.tom.2020.00012

Chicago/Turabian Style

Bell, Laura C., Natenael Semmineh, Hongyu An, Cihat Eldeniz, Richard Wahl, Kathleen M. Schmainda, Melissa A. Prah, Bradley J. Erickson, Panagiotis Korfiatis, Chengyue Wu, and et al. 2020. "Evaluating the Use of rCBV as a Tumor Grade and Treatment Response Classifier Across NCI Quantitative Imaging Network Sites: Part II of the DSC-MRI Digital Reference Object (DRO) Challenge" Tomography 6, no. 2: 203-208. https://doi.org/10.18383/j.tom.2020.00012

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