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Impact of Software Modeling on the Accuracy of Perfusion MRI in Glioma
L S Hu1, Z Kelm2, P Korfiatis2
1From the Department of Radiology (L.S.H.) Keller Center for Imaging Innovation (L.S.H., C.E., J.P.K., L.C.B.) Hu.Leland@Mayo.Edu.
Background And Purpose:
Relative cerebral blood volume, as measured by T2*-weighted dynamic susceptibility-weighted contrast-enhanced MRI, represents the most robust and widely used perfusion MR imaging metric in neuro-oncology. Our aim was to determine whether differences in modeling implementation will impact the correction of leakage effects (from blood-brain barrier disruption) and the accuracy of relative CBV calculations as measured on T2*-weighted dynamic susceptibility-weighted contrast-enhanced MR imaging at 3T field strength.
Materials And Methods:
This study included 52 patients with glioma undergoing DSC MR imaging. Thirty-six patients underwent both non-preload dose- and preload dose-corrected DSC acquisitions, with 16 patients undergoing preload dose-corrected acquisitions only. For each acquisition, we generated 2 sets of relative CBV metrics by using 2 separate, widely published, FDA-approved commercial software packages: IB Neuro and nordicICE. We calculated 4 relative CBV metrics within tumor volumes: mean relative CBV, mode relative CBV, percentage of voxels with relative CBV > 1.75, and percentage of voxels with relative CBV > 1.0 (fractional tumor burden). We determined Pearson (r) and Spearman (ρ) correlations between non-preload dose- and preload dose-corrected metrics. In a subset of patients with recurrent glioblastoma (n = 25), we determined receiver operating characteristic area under the curve for fractional tumor burden accuracy to predict the tissue diagnosis of tumor recurrence versus posttreatment effect. We also determined correlations between rCBV and microvessel area from stereotactic biopsies (n = 29) in 12 patients.
Results:
With IB Neuro, relative CBV metrics correlated highly between non-preload dose- and preload dose-corrected conditions for fractional tumor burden (r = 0.96, ρ = 0.94), percentage > 1.75 (r = 0.93, ρ = 0.91), mean (r = 0.87, ρ = 0.86), and mode (r = 0.78, ρ = 0.76). These correlations dropped substantially with nordicICE. With fractional tumor burden, IB Neuro was more accurate than nordicICE in diagnosing tumor versus posttreatment effect (area under the curve = 0.85 versus 0.67) (P < .01). The highest relative CBV-microvessel area correlations required preload dose and IB Neuro (r = 0.64, ρ = 0.58, P = .001).
Conclusions:
Different implementations of perfusion MR imaging software modeling can impact the accuracy of leakage correction, relative CBV calculation, and correlations with histologic benchmarks.
Insights
Software used for dynamic susceptibility-weighted contrast-enhanced MRI impacts relative cerebral blood volume (rCBV) calculations. IB Neuro software showed higher accuracy in leakage correction and predicting tumor recurrence compared to nordicICE.
Area of Science:
- Neuro-oncology
- Radiology
- Medical Imaging
Background:
- Relative cerebral blood volume (rCBV) is a key perfusion MRI metric in neuro-oncology.
- T2*-weighted dynamic susceptibility-weighted contrast-enhanced (DSC) MRI at 3T is widely used.
- Accurate rCBV calculation requires effective correction for blood-brain barrier disruption (leakage effects).
Purpose of the Study:
- To evaluate the impact of different software modeling implementations on leakage correction and rCBV accuracy.
- To compare two commercial software packages (IB Neuro and nordicICE) for rCBV analysis at 3T.
- To assess the diagnostic performance of rCBV metrics in differentiating tumor recurrence from posttreatment effects.
Main Methods:
- 52 glioma patients underwent DSC MRI with varying preload dose protocols.
- Relative CBV metrics (mean, mode, fractional tumor burden) were calculated using IB Neuro and nordicICE.
- Correlations between software, preload conditions, and histologic microvessel area were analyzed.
- Receiver operating characteristic analysis assessed diagnostic accuracy for tumor recurrence.
Main Results:
- IB Neuro demonstrated high correlations between preload and non-preload corrected rCBV metrics.
- nordicICE showed substantially lower correlations.
- IB Neuro's fractional tumor burden was more accurate in distinguishing tumor recurrence (AUC=0.85) than nordicICE (AUC=0.67).
- Preload dose with IB Neuro yielded the strongest correlation with microvessel area (r=0.64).
Conclusions:
- Software implementation significantly affects leakage correction and rCBV accuracy in DSC MRI.
- IB Neuro demonstrates superior performance in rCBV calculation and diagnostic accuracy for glioma.
- Accurate rCBV quantification is crucial for reliable neuro-oncology assessments and correlation with histologic findings.
