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Article

The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow

by
Saumya Gurbani
1,2,
Brent Weinberg
3,
Lee Cooper
2,4,
Eric Mellon
5,
Eduard Schreibmann
1,
Sulaiman Sheriff
6,
Andrew Maudsley
6,
Mohammed Goryawala
6,
Hui-Kuo Shu
1 and
Hyunsuk Shim
1,2,3,*
1
Department of Radiation Oncology, Emory University, Atlanta, GA 30322, USA
2
Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, USA
3
Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA 30322, USA
4
Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, USA
5
Department of Radiation Oncology, University of Miami Miller School of Medicine, Miami, FL 33101, USA
6
Department of Radiology, University of Miami Miller School of Medicine, Miami, FL 33101, USA
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 184-191; https://doi.org/10.18383/j.tom.2018.00028
Submission received: 1 February 2019 / Revised: 8 February 2019 / Accepted: 22 February 2019 / Published: 1 March 2019

Abstract

Glioblastoma has poor prognosis with inevitable local recurrence despite aggressive treatment with surgery and chemoradiation. Radiation therapy (RT) is typically guided by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) for defining the high-dose target and T2-weighted fluid-attenuation inversion recovery MRI for defining the moderate-dose target. There is an urgent need for improved imaging methods to better delineate tumors for focal RT. Spectroscopic MRI (sMRI) is a quantitative imaging technique that enables whole-brain analysis of endogenous metabolite levels, such as the ratio of choline-to-N-acetylaspartate. Previous work has shown that choline-to-N-acetylaspartate ratio accurately identifies tissue with high tumor burden beyond what is seen on standard imaging and can predict regions of metabolic abnormality that are at high risk for recurrence. To facilitate efficient clinical implementation of sMRI for RT planning, we developed the Brain Imaging Collaboration Suite (BrICS; https://brainimaging.emory.edu/brics-demo), a cloud platform that integrates sMRI with standard imaging and enables team members from multiple departments and institutions to work together in delineating RT targets. BrICS is being used in a multisite pilot study to assess feasibility and safety of dose-escalated RT based on metabolic abnormalities in patients with glioblastoma (Clinicaltrials.gov NCT03137888). The workflow of analyzing sMRI volumes and preparing RT plans is described. The pipeline achieved rapid turnaround time by enabling team members to perform their delegated tasks independently in BrICS when their clinical schedules allowed. To date, 18 patients have been treated using targets created in BrICS and no severe toxicities have been observed.
Keywords: spectroscopic MRI; radiation therapy; cloud platform spectroscopic MRI; radiation therapy; cloud platform

Share and Cite

MDPI and ACS Style

Gurbani, S.; Weinberg, B.; Cooper, L.; Mellon, E.; Schreibmann, E.; Sheriff, S.; Maudsley, A.; Goryawala, M.; Shu, H.-K.; Shim, H. The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow. Tomography 2019, 5, 184-191. https://doi.org/10.18383/j.tom.2018.00028

AMA Style

Gurbani S, Weinberg B, Cooper L, Mellon E, Schreibmann E, Sheriff S, Maudsley A, Goryawala M, Shu H-K, Shim H. The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow. Tomography. 2019; 5(1):184-191. https://doi.org/10.18383/j.tom.2018.00028

Chicago/Turabian Style

Gurbani, Saumya, Brent Weinberg, Lee Cooper, Eric Mellon, Eduard Schreibmann, Sulaiman Sheriff, Andrew Maudsley, Mohammed Goryawala, Hui-Kuo Shu, and Hyunsuk Shim. 2019. "The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow" Tomography 5, no. 1: 184-191. https://doi.org/10.18383/j.tom.2018.00028

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