Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO)

Abstract

Relative cerebral blood volume (rCBV) cannot be used as a response metric in clinical trials, in part, because of variations in biomarker consistency and associated interpretation across sites, stemming from differences in image acquisition and postprocessing methods (PMs). This study leveraged a dynamic susceptibility contrast magnetic resonance imaging digital reference object to characterize rCBV consistency across 12 sites participating in the Quantitative Imaging Network (QIN), specifically focusing on differences in site-specific imaging protocols (IPs; n = 17), and PMs (n = 19) and differences due to site-specific IPs and PMs (n = 25). Thus, high agreement across sites occurs when 1 managing center processes rCBV despite slight variations in the IP. This result is most likely supported by current initiatives to standardize IPs. However, marked intersite disagreement was observed when site-specific software was applied for rCBV measurements. This study's results have important implications for comparing rCBV values across sites and trials, where variability in PMs could confound the comparison of therapeutic effectiveness and/or any attempts to establish thresholds for categorical response to therapy. To overcome these challenges and ensure the successful use of rCBV as a clinical trial biomarker, we recommend the establishment of qualifying and validating site- and trial-specific criteria for scanners and acquisition methods (eg, using a validated phantom) and the software tools used for dynamic susceptibility contrast magnetic resonance imaging analysis (eg, using a digital reference object where the ground truth is known).

Keywords: DSC-MRI; multisite consistency; relative cerebral blood volume; reproducibility; standardization.

Figure 1.

Bland–Altman limits of agreement (LOA) against the standard imaging protocol (SIP) plotted for site-specific IP w/constant postprocessing method (PM) (A), constant IP w/site-specific PM (B), and site-specific IP w/site-specific PM (C). The vertical dashed line is the mean rCBV across 10 000 voxels for the SIP.

Figure 2.

The covariance (CV%) across all relative cerebral blood volume (rCBV) maps for each of the 10 000 voxels plotted across the mean rCBV of the voxels for site-specific IP w/constant PM (A), constant IP w/site-specific PM (B), and site-specific IP w/site-specific PM (C). Results from the K^trans = 0 (light gray) and K^trans > 0 (black) are included with their mean CV% across all 10 000 voxels indicated for the horizontal lines. For all 3 phases of this study, the largest variation in rCBV occurs at the low rCBV range for K^trans > 0, and CV% increases when more freedom was introduced in the choice of IPs and PMs.

Figure 3.

A bar plot of Lin's correlation coefficient (LCC) for each rCBV map for site-specific IP w/constant PM (black), constant IP w/site-specific PM (medium gray), and site-specific IP w/site-specific PM (light gray). Each phase is sorted by the resulting LCC from the highest to the lowest value. A horizontal bar at LCC = 0.8 is placed to evaluate agreement good agreement (LCC > 0.8).