Inglese, MariannaMariannaIngleseKatherine L, OrdidgeOrdidgeKatherine LLesley, HoneyfieldHoneyfieldLesleyTara D, BarwickBarwickTara DEric O, AboagyeAboagyeEric OAdam D, WaldmanWaldmanAdam DMatthew, Grech-SollarsGrech-SollarsMatthew2019-07-252019-12-01Neuroradiology, 2019, 61 (12), pp.1375-13860028-3940http://hdl.handle.net/10044/1/71933Purpose 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. Methods DCE-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). Results The 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. Conclusions The 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.© 2019 The Authors. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http:// creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.GliomaDCE-MRIshutter speed modelTofts modelprimary brain tumourJournal Articlehttps://www.dx.doi.org/10.1007/s00234-019-02265-2RDC04 795605098