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.
Highlights
The National Cancer Institute’s Quantitative Imaging Network (QIN), Radiological Society of North America’s Quantitative Imaging Biomarkers Alliance (QIBA), and the National Brain Tumor Society’s (NBTS) Jumpstarting Brain Tumor Drug Development Coalition all have initiatives aiming to standardize Dynamic Susceptibility Contrast (DSC) MRI protocols and postprocessing methods
In a previously published study involving 12 sites within the NCI’s Quantitative Imaging Network (QIN), variable imaging protocols (IPs) and postprocessing methods (PMs) were found to reduce relative cerebral blood volume (rCBV) reproducibility [6]. Another QIN study showed that if acquisition and preprocessing steps were held constant, the variability between sites greatly diminished such that a global threshold to distinguish low- from high-grade tumor could be identified [7]. This study extends these previous investigations by evaluating the potential impact of variable IPs and PMs on 2 clinical use cases, namely, classification of brain tumor grade and treatment response assessment
MATERIALS AND METHODS The previously validated population-based digital reference object (DRO) used in this study encompasses 10 000 unique DSC-MRI tumor voxels and was simulated for each IP provided by the 12 participating QIN sites [6, 8]
Summary
The National Cancer Institute’s Quantitative Imaging Network (QIN), Radiological Society of North America’s Quantitative Imaging Biomarkers Alliance (QIBA), and the National Brain Tumor Society’s (NBTS) Jumpstarting Brain Tumor Drug Development Coalition all have initiatives aiming to standardize Dynamic Susceptibility Contrast (DSC) MRI protocols and postprocessing methods. Standardization of relative cerebral blood volume (rCBV) as a quantitative biomarker for glioma care is warranted because of the increased adoption of rCBV into multisite clinical trials and protocol variability could impact its use as a reliable biomarker of response [1,2,3]. A recent systematic meta-analysis of 26 published studies found that DSC-MRI accurately distinguishes tumor recurrence from post-treatment radiation effects within a given study, inconsistency of DSC-MRI protocols between institutions led to substantial variability in reported optimal thresholds. These resulting inconsistencies emphasize the need for greater consistency before a specific quantitative DSC-MRI strategy is adopted across institutions for routine clinical use [4].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
