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. 2020 Jun;6(2):203-208.
doi: 10.18383/j.tom.2020.00012.

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

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 , C Chad Quarles  1
Affiliations

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

Laura C Bell et al. Tomography. 2020 Jun.

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; multisite consistency; relative cerebral blood volume; standardization; treatment response; tumor grading.

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Figures

Figure 1.
Figure 1.
A flowchart of the steps involved to create the virtual tumors from the digital reference object (DRO).
Figure 2.
Figure 2.
Histogram distributions of mean relative cerebral blood volume (rCBV) the for The Cancer Imaging Archive (TCIA) data set (A) and the virtual tumors (B) for tumor grade: low-grade glioma (LGG) (blue) and high-grade glioma (HGG) (red). The rCBV distributions for both populations are similar (as listed within the legend), with slight deviations noted for very low and high mean rCBV tumors.
Figure 3.
Figure 3.
Histogram distributions of the mean rCBV, pre- (preTx) and posttreatment (postTx), for the clinical data set (A) and the virtual tumors (B): preTx (blue) and postTx (red). The rCBV distributions for both populations are similar (as listed within the legend).
Figure 4.
Figure 4.
Boxplots of the optimal rCBV threshold needed for tumor grade (A) and treatment response (B) classification grouped by the 3 phases of this study. Individual measurements are overlaid on the boxplot to better visualize the distribution. Outliers are indicated by red plus signs. Wider distributions of optimal thresholds are seen for phases II and III where sites use a variety of software packages for rCBV calculation.
Figure 5.
Figure 5.
Boxplots of the AUROC for tumor grade (A) and treatment response (B) classification grouped by the 3 phases of this study. Individual measurements are overlaid on the boxplot to better visualize the distribution. Outliers are indicated by red plus signs. Slightly wider distributions of area under the receiver operating characteristics (AUROCs) are seen for phases II and III where sites use a variety of software packages for rCBV calculation. Outliers consistently show a decrease in AUROC.
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