Shih-Li Chao1, Thierry Metens2, Marc Lemort1. 1. Department of Radiology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium. 2. Department of Radiology, Hôpital Erasme CUB, Ecole Polytechnique & Faculté de Médecine Université Libre de Bruxelles, Brussels, Belgium.
Abstract
BACKGROUND: A reliable analysis methodology is needed to provide valuable imaging biomarkers for clinical trials, with particular regards to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) application using pharmacokinetic (PK) model analysis. In order to address this scientific challenge, we provided a comprehensive analysis solution that could overcome the impediments to clinical research and routine use. METHODS: TumourMetrics has been designed to meet the Quantitative Imaging Biomarkers Alliance (QIBA) v.1.0 profile. The quality performance was assessed using the QIBA test data and our customizable numeric phantom. The analysis workflow is made customizable to facilitate standardization of optimized protocol across centers. RESULTS: Our quantification workflow estimated the PK model parameters accurately. The method is robust, almost fully automatic and allows a direct integration of the results into the diagnostic workflow. CONCLUSIONS: The analysis is easy-to-use and accessible for routine implementation of DCE-MRI into clinical practice.
BACKGROUND: A reliable analysis methodology is needed to provide valuable imaging biomarkers for clinical trials, with particular regards to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) application using pharmacokinetic (PK) model analysis. In order to address this scientific challenge, we provided a comprehensive analysis solution that could overcome the impediments to clinical research and routine use. METHODS: TumourMetrics has been designed to meet the Quantitative Imaging Biomarkers Alliance (QIBA) v.1.0 profile. The quality performance was assessed using the QIBA test data and our customizable numeric phantom. The analysis workflow is made customizable to facilitate standardization of optimized protocol across centers. RESULTS: Our quantification workflow estimated the PK model parameters accurately. The method is robust, almost fully automatic and allows a direct integration of the results into the diagnostic workflow. CONCLUSIONS: The analysis is easy-to-use and accessible for routine implementation of DCE-MRI into clinical practice.
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