Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models

Abstract

PurposeThe purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test.MethodsDCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV).ResultsThe model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable.ConclusionsThe majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.

Application of Distributed Parameter Model to Assessment of Glioma IDH Mutation Status by Dynamic Contrast-Enhanced Magnetic Resonance Imaging

“…In comparison, these two transports are modeled using one parameter, transfer constant ( K trans ), in the Tofts or extended Tofts model. Interested readers could refer to [ 31 , 32 ] for a review on the topic.…”

Assessing Tumour Haemodynamic Heterogeneity and Response to Choline Kinase Inhibition Using Clustered Dynamic Contrast Enhanced MRI Parameters in Rodent Models of Glioblastoma

“…A non-linear least-square fitting ‘lsqcurvefit’ routine in MATLAB was used in all models. For model fitting, the initial parameter values were based on the literature values [ 38 , 39 ]. A hybrid bi-exponential and gamma variate model fitting of the AIF was performed prior to kinetic model analysis [ 40 , 41 ].…”

The LEGATOS technique: A new tissue‐validated dynamic contrast‐enhanced MRI method for whole‐brain, high‐spatial resolution parametric mapping

“…The ETM has been adopted in this study as previous application of this model with low-spatial resolution HT data sets derived from DTR DCE-MRI was found to give both tissue-validated and clinically relevant kinetic parameter estimates. 4,5,30 Recent studies have also shown through a model selection process using the Akaike information criteria, that the ETM was one of the optimal models in pharmacokinetic analysis of DCE-MRI data in brain tumors 42 and papillary thyroid carcinoma. 43 One limitation of the ETM is that the derived parameter K trans is a hybrid parameter reflecting both capillary blood flow and permeability effects, 44,45 and future studies incorporating the high spatiotemporal resolution DCE data provided by the LEGATOS method and other DCE-MRI models, which seek to further separate out such effects should, therefore, be undertaken.…”