Dynamic contrast‐enhanced MRI: Study of inter‐software accuracy and reproducibility using simulated and clinical data

Abstract

Disclosure of fundingLuc Beuzit received a grant from Rennes University of Medicine for this work ("Prix jeune chercheur") Short titleInter-software variability of DCE-MRI Conclusion. Significant errors were found in the calculated DCE-MR imaging pharmacokinetic parameters for the perfusion analysis SPs, resulting in poor inter-software reproducibility.

Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE‐MRI derived biomarkers in multicenter oncology trials

“…In the last decade, there have been major rapid advances in the field of magnetic resonance imaging (MRI), including advancements in hardware, acquisition pulse sequences, image reconstruction, and data analysis algorithms . These technological advances have fostered a timely focus on quantitative MRI (qMRI), which purports the ability to derive objective metrics from images that relate to specific physical or biophysical properties of the imaged tissue.…”

“…Applications of qMRI include detection of disease and its evolution in progression or response to therapies that affect the relevant biophysical property of tissue (eg, cytotoxic therapies that reduce cellularity) . These methods have been covered in excellent reviews detailing the technical aspects and their applications . As the quantitative measurements derived from DWI (eg, mean diffusivity) and DCE (eg, volume transfer constant) are utilized in clinical trials of new treatment strategies, or for precision medicine and personalized cancer care, the technical confidence of these measurements in repeatability and reproducibility is ever more critical .…”

Radiogenomics Monitoring in Breast Cancer Identifies Metabolism and Immune Checkpoints as Early Actionable Mechanisms of Resistance to Anti-angiogenic Treatment

“…Clearly when analysing whole tumor by imaging we can never sample to the same extent, but only conduct small number of biopsies. Furthermore, previous work from our (Ah-See et al, 2008) and other groups (Othman et al, 2016, Beuzit et al, 2016, Wang et al, 2015) has demonstrated DCE-MRI parameter estimation can depend on a variety of factors including normal physiological variability, MRI set-up and acquisition protocols, and different image processing methods. Strikingly, in spite of this, we observed strong correlations of functional imaging with molecular changes, regardless of the DCE-MRI method used to categorize the patients (binary response, continuous median K trans and total K trans ), reflecting heterogeneity of response and providing added molecular information.…”

“…Specifically, only patients showing 30% or greater reduction in median K trans were categorised as responders (Ah-See et al, 2008). This classification might seem conservative, however it considers the variability in DCE-MRI parameter estimation which has been discussed extensively in previous work (Othman et al, 2016, Beuzit et al, 2016, Wang et al, 2015). In particular, it defines as significant changes, hence response to treatment, only those changes in median K trans which are above the average level of noise observed in our (Ah-See et al, 2008) and other group's (Li et al, 2014) previous breast cancer studies.…”

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

“…At our knowledge, there was not QIBA test data validation using these clinical solutions that has been published to make the comparison. In a recent study, Beuzit et al compared the accuracy and reproducibility among five of mostly used clinical software using in-house simulated data and clinical data (32). Their results showed significant errors in calculated parameters and poor inter-software reproducibility.…”