The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II.

Wei Huang,Charles M Laymon,James M Mountz,Bachir Taouli,Andriy Fedorov,Cecilia Besa,Xin Li,Xia Li,Mark Muzi,Brendan Moloney,Alina Tudorica,Paul E Kinahan,Peter S Laviolette,Aneela Afzal,Yue Cao,Matthew J Oborski,Thomas L Chenevert,Guido H Jajamovich,Thomas E Yankeelov,Kourosh Jafari-Khouzani,Richard G Abramson,Yiyi Chen,Dariya I Malyarenko,Kathleen M Schmainda ,Jayashree Kalpathy–Cramer ,Fiona M Fennessy ,Madhava Aryal ,Sandeep Gupta ,Finbarr O’sullivan

doi:10.18383/j.tom.2018.00027

Abstract

This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.

Highlights

As a noninvasive method to measure tissue microvascular perfusion and permeability, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used in oncologic imaging for cancer diagnosis and therapeutic monitoring [1, 2].DCE-MRI generally involves the serial acquisition of heavily T1-weighted images before, during, and after the injection of a paramagnetic contrast agent (CA)
Variations in arterial input function (AIF) Determination For each data set, substantial variations in both the amplitude and shape of the Cp(t) time-course can be observed as a result of direct AIF measurement from the DCE-MRI data by the 9 Quantitative Imaging Network (QIN) centers using site-specific methods
Our findings are in agreement with a recent study comparing fully automated and semiautomated AIF determination approaches for prostate DCE-MRI data analysis [7], showing that Ktrans variation owing to AIF uncertainty is the most prominent compared with other PK parameters

Summary

Introduction

As a noninvasive method to measure tissue microvascular perfusion and permeability, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used in oncologic imaging for cancer diagnosis and therapeutic monitoring [1, 2].DCE-MRI generally involves the serial acquisition of heavily T1-weighted images before, during, and after the injection of a paramagnetic contrast agent (CA). The accuracy and precision of the derived PK parameters can be largely affected by the selection of the PK model for data fitting [3,4,5], errors in quantification of the native tissue T1 value [3, 4, 6], and variance in determination of arterial input function (AIF; the time-course of CA plasma concentration) [3, 4, 7,8,9] These challenges lead to substantial variations in the reported PK parameter values for the same disease and are fundamental obstacles in translating quantitative DCE-MRI into multicenter clinical trials and general clinical practice. It is important for the DCE-MRI community to investigate the impact of variations/errors in different steps of PK data analysis on the estimated parameter values, establish ways to reduce parameter variance, and identify those parameters that are less sensitive to certain variations in data analysis and, the more robust imaging biomarkers for multicenter studies

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Discussion

Conclusion