TumourMetrics: a comprehensive clinical solution for the standardization of DCE-MRI analysis in research and routine use

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.

Total choline quantification measured by 1H MR spectroscopy as early predictor of response after neoadjuvant treatment for locally advanced breast cancer: The impact of immunohistochemical status

“…In‐house software was used for DCE‐MRI analysis and interpretation . The quantification was performed on a voxel‐by‐voxel basis using a two‐compartment Tofts pharmacokinetic model and the Weinman standard input function .…”

“…In-house software was used for DCE-MRI analysis and interpretation. 23 The quantification was performed on a voxel-by-voxel basis using a two-compartment Tofts pharmacokinetic model and the Weinman standard input function. 24 From this analysis, the vascular permeability parameters K trans (plasma to extravascular volume transfer constant) and K ep (extravascular to plasma volume transfer constant) were defined as additional functional biomarkers.…”

Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors

“…In order to apply any pharmacokinetic model to a series of MR images, we use a mapping between the pixel intensity (in an image), and the contrast concentration in the corresponding volume [8]. This procedure exploits the CA's magnetic relaxivity (specific for the used agent), the value of patient's haematocrit (HTC), and the pre-contrast T10 relaxation times of the scanned tissue.…”

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: First voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data

“…d. Signal processing: the 4D-PET data were smoothed with an 8 mm Gaussian lter (library nilearn), and the DCE imaging data were converted to Gadolinium plasma concentration C(t) (libraries nilearn, numpy, pandas) using the following equation [43]: See formula 1 in the supplementary les.…”