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A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations.

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.

SUBMITTER: Joint Head and Neck Radiotherapy-MRI Development Cooperative

PROVIDER: S-EPMC5593829 | biostudies-other | 2017 Sep

REPOSITORIES: biostudies-other

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A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations.

Scientific reports 20170911 1

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, DC ...[more]

PMID: 28894197

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Project description:The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h-6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.

Project description:Key pointsThe blood-brain barrier (BBB) is an important and dynamic structure which contributes to homeostasis in the central nervous system. BBB permeability changes occur in health and disease but measurement of BBB permeability in humans is not straightforward. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to model the movement of gadolinium contrast into the brain, expressed as the influx constant Ki . Here evidence is provided that Ki as measured by DCE-MRI behaves as expected for a marker of overall BBB leakage. These results support the use of DCE-MRI for in vivo studies of human BBB permeability in health and disease.AbstractBlood-brain barrier (BBB) leakage can be measured using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) as the influx constant Ki . To validate this method we compared measured Ki with biological expectations, namely (1) higher Ki in healthy individual grey matter (GM) versus white matter (WM), (2) GM/WM cerebral blood volume (CBV) ratio close to the histologically established GM/WM vascular density ratio, (3) higher Ki in visibly enhancing multiple sclerosis (MS) lesions versus MS normal appearing white matter (NAWM), and (4) higher Ki in MS NAWM versus healthy individual NAWM. We recruited 13 healthy individuals and 12 patients with MS and performed whole-brain 3D DCE-MRI at 3 T. Ki and CBV were calculated using Patlak modelling for manual regions of interest (ROI) and segmented tissue masks. Ki was higher in control GM versus WM (P = 0.001). CBV was higher in GM versus WM (P = 0.005, mean ratio 1.9). Ki was higher in visibly enhancing MS lesions versus MS NAWM (P = 0.002), and in MS NAWM versus controls (P = 0.014). Bland-Altman analysis showed no significant difference between ROI and segmentation methods (P = 0.638) and an intra-class correlation coefficient showed moderate single measure consistency (0.610). Ki behaves as expected for a compound marker of permeability and surface area. The GM/WM CBV ratio measured by this technique is in agreement with the literature. This adds evidence to the validity of Ki measured by DCE-MRI as a marker of overall BBB leakage.

Dataset Information

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations.

Publications

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations.

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