Abstract
PurposeTo compare semi-quantitative (SQ) and pharmacokinetic (PK) parameters for analysis of dynamic contrast enhanced MR data (DCE-MRI) and investigate error-propagation in SQ parameters.MethodsClinical data was collected from five patients with type 2-neurofibromatosis (NF2) receiving anti-angiogenic therapy for rapidly growing vestibular schwannoma (VS). There were 7 VS and 5 meningiomas. Patients were scanned prior to therapy and at days 3 and 90 of treatment. Data was collected using a dual injection technique to permit direct comparison of SQ and PK parameters. Monte Carlo modeling was performed to assess potential measurement errors in SQ parameters in persistent, washout, and weakly enhancing tissues. The simulation predictions for five semi-quantitative parameters were tested using the clinical DCE-MRI data.ResultsIn VS, SQ parameters and Ktrans showed close correlation and demonstrated similar therapy induced reductions. In meningioma, only the denoised Signal Enhancement Ratio (Rse1/se2(DN)) showed a significant therapy induced reduction (p<0.05). Simulation demonstrated: 1) Precision of SQ metrics normalized to the pre-contrast-baseline values (MSErel and ∑MSErel) is improved by use of an averaged value from multiple baseline scans; 2) signal enhancement ratio Rmse1/mse2 shows considerable susceptibility to noise; 3) removal of outlier values to produce a new parameter, Rmse1/mse2(DN), improves precision and sensitivity to therapy induced changes. Direct comparison of in-vivo analysis with Monte Carlo simulation supported the simulation predicted error distributions of semi-quantitative metrics.ConclusionPK and SQ parameters showed similar sensitivity to anti-angiogenic therapy induced changes in VS. Modeling studies confirmed the benefits of averaging baseline signal from multiple images for normalized SQ metrics and demonstrated poor noise tolerance in the widely used signal enhancement ratio, which is corrected by removal of outlier values.
Highlights
Analysis of dynamic contrast enhanced MRI (DCE-MRI) data is commonly performed by applying pharmacokinetic (PK) models to changes in contrast agent concentration derived from observed changes in signal intensity (SI)
Almost all early DCE-MRI studies employed simple SQ metrics derived by mathematical analysis of observed SI-time course data (SI-TC)
Despite widespread clinical adoption SQ parameters are commonly avoided for clinical trial applications in the belief that they are less biologically specific and more prone to variability than parameters derived from PK modeling, [2,4] no detailed study of the behavior of SQ parameters or direct comparison of SQ and PK parameters has been presented
Summary
Analysis of dynamic contrast enhanced MRI (DCE-MRI) data is commonly performed by applying pharmacokinetic (PK) models to changes in contrast agent concentration derived from observed changes in signal intensity (SI). With the growth of PK approaches the field has become dichotomized with consensus groups recommending PK analysis [1,2,3,4] whilst, at the same time clinical radiologists are far more likely to use SQ metrics which have become essential clinical tools across a range of oncological applications [5,6,7,8,9,10,11, 12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Many studies have shown significant problems associated with PK derived parameters arising from the need for high temporal resolution sampling, accurate arterial input function definition and problems associated with curve fitting based analysis approaches which are limiting widespread implementation of DCE-MRI, into multi-center studies [4,28,29,30]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
