Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.
Highlights
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models
The perfusion and permeability metrics estimated from pharmacokinetic modeling of DCE-MRI data may provide an indirect measure of tumor hypoxia, a condition associated with poor prognosis in HNSCC3,4
The accuracy and precision of these quantitative metrics can be influenced by arterial input function (AIF) quantification, temporal resolution in data acquisition, signal-to-noise ratio (SNR), and pharmacokinetic model selection[12,13,14,15,16,17,18,19,20,21,22]
Summary
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. To the best of our knowledge, its use as a prognostic tool to inform treatment decisions for HNSCC has yet to be investigated in a large multisite prospective trial Before such trials can begin, DCE-MRI inter-algorithm comparisons must be conducted to ensure consistency of output parameter maps for collating data during the multi-institution trial. Cron et al.[27] found that the percentage of nonphysical values (e.g. ve values greater than 1) in the quantitative metrics increased as noise increased when they tested using three software packages This noise dependence and inter-algorithm variance in quantitative DCE-MRI metrics are large obstacles to the clinical implementation of DCE-MRI and must be thoroughly investigated before proceeding with large multisite clinical trials using DCE-MRI in HNSCC patients
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