Literature DB >> 16506143

Comparative study into the robustness of compartmental modeling and model-free analysis in DCE-MRI studies.

Caleb Roberts1, Basma Issa, Andrew Stone, Alan Jackson, John C Waterton, Geoffrey J M Parker.   

Abstract

PURPOSE: To evaluate and compare the reproducibility of the preferred phenomenological parameter IAUC60 (initial area under the time-concentration curve [IAUC] defined over the first 60 seconds postenhancement) with the preferred modeling parameter (K(trans)), as derived using two simple models, in abdominal and cerebral data collected in typical Phase I clinical trial conditions.
MATERIALS AND METHODS: Dynamic contrast enhanced MRI (DCE-MRI) time series were acquired at two imaging centers from a group of patients with abdominal tumors and a group with gliomas. At both imaging centers, precontrast T1 was calculated using a variable flip angle three-dimensional spoiled gradient echo acquisition that was used to quantify tissue contrast agent concentration, allowing voxelwise definition of summary DCE-MRI parameters.
RESULTS: A comparison of reproducibility showed that there was no statistically significant difference in reproducibility between IAUC60 and K(trans), although there was a trend towards better reproducibility for K(trans) (P = 0.0782). The 95% confidence intervals (CIs) for individual changes showed that for IAUC60 and K(trans), changes in excess of 47% and 31%, respectively, are outside the range of normal variability.
CONCLUSION: Although modeling is more complex and more computationally intensive than an IAUC parameterization, our data suggest this approach to be preferable to a model-free approach since it provides greater physiological insight without a reduction in statistical power for Phase I/II clinical drug trials. 2006 Wiley-Liss, Inc.

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Year:  2006        PMID: 16506143     DOI: 10.1002/jmri.20529

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  49 in total

1.  Repeatability of graph theoretical metrics derived from resting-state functional networks in paediatric epilepsy patients.

Authors:  Michael J Paldino; Zili D Chu; Mary L Chapieski; Farahnaz Golriz; Wei Zhang
Journal:  Br J Radiol       Date:  2017-05-23       Impact factor: 3.039

2.  Prediction of pseudoprogression in patients with glioblastomas using the initial and final area under the curves ratio derived from dynamic contrast-enhanced T1-weighted perfusion MR imaging.

Authors:  C H Suh; H S Kim; Y J Choi; N Kim; S J Kim
Journal:  AJNR Am J Neuroradiol       Date:  2013-07-04       Impact factor: 3.825

3.  Implementation of a semi-automated post-processing system for parametric MRI mapping of human breast cancer.

Authors:  Robert E Lee; E Brian Welch; Jared G Cobb; Tuhin Sinha; John C Gore; Thomas E Yankeelov
Journal:  J Digit Imaging       Date:  2008-04-30       Impact factor: 4.056

4.  Validation of dynamic contrast-enhanced magnetic resonance imaging-derived vascular permeability measurements using quantitative autoradiography in the RG2 rat brain tumor model.

Authors:  Moira C Ferrier; Hemant Sarin; Steve H Fung; Bawarjan Schatlo; Ryszard M Pluta; Sandeep N Gupta; Peter L Choyke; Edward H Oldfield; David Thomasson; John A Butman
Journal:  Neoplasia       Date:  2007-07       Impact factor: 5.715

5.  Is there any correlation between model-based perfusion parameters and model-free parameters of time-signal intensity curve on dynamic contrast enhanced MRI in breast cancer patients?

Authors:  Boram Yi; Doo Kyoung Kang; Dukyong Yoon; Yong Sik Jung; Ku Sang Kim; Hyunee Yim; Tae Hee Kim
Journal:  Eur Radiol       Date:  2014-02-21       Impact factor: 5.315

6.  Respiratory motion-compensated radial dynamic contrast-enhanced (DCE)-MRI of chest and abdominal lesions.

Authors:  Wei Lin; Junyu Guo; Mark A Rosen; Hee Kwon Song
Journal:  Magn Reson Med       Date:  2008-11       Impact factor: 4.668

7.  Enhancing fraction in glioma and its relationship to the tumoral vascular microenvironment: A dynamic contrast-enhanced MR imaging study.

Authors:  S J Mills; C Soh; J P B O'Connor; C J Rose; G Buonaccorsi; S Cheung; S Zhao; G J M Parker; A Jackson
Journal:  AJNR Am J Neuroradiol       Date:  2009-12-17       Impact factor: 3.825

8.  Magnetic resonance assessment of response to therapy: tumor change measurement, truth data and error sources.

Authors:  Edward F Jackson; Daniel P Barboriak; Luc M Bidaut; Charles R Meyer
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

9.  A new approach to analysis of the impulse response function (IRF) in dynamic contrast-enhanced MRI (DCEMRI): a simulation study.

Authors:  Xiaobing Fan; Gregory S Karczmar
Journal:  Magn Reson Med       Date:  2009-07       Impact factor: 4.668

10.  Multiparametric fully-integrated 18-FDG PET/MRI of advanced gastric cancer for prediction of chemotherapy response: a preliminary study.

Authors:  Dong Ho Lee; Se Hyung Kim; Seock-Ah Im; Do-Youn Oh; Tae-Yong Kim; Joon Koo Han
Journal:  Eur Radiol       Date:  2015-11-28       Impact factor: 5.315

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