Richard C Semelka

UNCCH, School of Medicine, Radiology

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An automated method for nonparametric kinetic analysis of clinical DCE-MRI data: Application to glioblastoma treated with bevacizumab

Gregory Z. Ferl; Lu Xu; Michel Friesenhahn; Lisa J. Bernstein; Daniel P. Barboriak; Ruediger E. Port (Profiled Author: Daniel P. Barboriak)

Magnetic Resonance in Medicine. 2010;63(5):1366-1375.

Here, we describe an automated nonparametric method for evaluating gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA) kinetics, based on dynamic contrast-enhanced-MRI scans of glioblastoma patients taken before and after treatment with bevacizumab; no specific model or equation structure is assumed or used. Tumor and venous blood concentration-time profiles are smoothed, using a robust algorithm that removes artifacts due to patient motion, and then deconvolved, yielding an impulse response function. In addition to smoothing, robustness of the deconvolution operation is assured by excluding data that occur prior to the plasma peak; an exhaustive analysis was performed to demonstrate that exclusion of the prepeak plasma data does not significantly affect results. All analysis steps are executed by a single R script that requires blood and tumor curves as the sole input. Statistical moment analysis of the Impulse response function yields the area under the curve (AUC) and mean residence time (MRT). Comparison of deconvolution results to fitted Tofts model parameters suggests that AUC/MRT and AUC of the Impulse response function closely approximate fractional clearance from plasma to tissue (K ^trans) and fractional interstitial volume (v e). Intervisit variability is shown to be comparable when using the deconvolution method (11%[AUC/MRT] and 13%[AUC]) compared to the Tofts model (14%[K ^trans] and 24%[v e]). AUC and AUC/MRT both exhibit a statistically significant decrease (P < 0.005) 1 day after administration of bevacizumab. © 2010 Wiley-Liss, Inc.

PMID: 20432307    

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