Literature DB >> 18710983

Colorectal liver metastases: contrast agent diffusion coefficient for quantification of contrast enhancement heterogeneity at MR imaging.

Guang Jia1, Craig O'Dell, Johannes T Heverhagen, Xiangyu Yang, Jiachao Liang, Richard V Jacko, Steffen Sammet, Theodore Pellas, Patricia Cole, Michael V Knopp.   

Abstract

PURPOSE: To describe and determine the reproducibility of a simplified model to quantitatively measure heterogeneous intralesion contrast agent diffusion in colorectal liver metastases.
MATERIALS AND METHODS: This HIPAA-compliant retrospective study received institutional review board approval, and written informed consent was obtained from 14 patients (mean age, 61 years +/- 9 [standard deviation]; range, 41-78 years), including 10 men (mean age, 65 years +/- 8; range, 47-78 years) and four women (mean age, 54 years +/- 9; range, 41-59 years), with colorectal liver metastases. Magnetic resonance (MR) imaging was performed twice (first baseline MR image [B(1)] and second baseline MR image [B(2)]) in a single target lesion prior to therapy. Dynamic contrast material-enhanced MR imaging was performed by using a saturation-recovery fast gradient-echo sequence. A simplified contrast agent diffusion model was proposed, and a contrast agent diffusion coefficient (CDC) was calculated. The reproducibility of the CDC measurement was evaluated by using the Bland-Altman plot and a linear regression model.
RESULTS: The mean CDC was 0.22 mm(2)/sec (range, 0.01-0.73 mm(2)/sec) on B(1) and 0.24 mm(2)/sec (range, 0.01-0.71 mm(2)/sec) on B(2), with an intraclass correlation coefficient of 0.91 (P < .0001). Bland-Altman plot showed good agreement, with a mean difference in measurement pairs of 0.017 mm(2)/sec +/- 0.096. The slope from the linear regression model was 0.89 (95% confidence interval: 0.63, 1.15) and the intercept was 0.01 (95% confidence interval: -0.08, 0.09).
CONCLUSION: The CDC enables a quantitative description of contrast enhancement heterogeneity in lesions. Given the high reproducibility of the CDC metric, CDC appears promising for further qualification as an imaging biomarker of change measurement in response assessment. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/248/3/901/DC1. RSNA, 2008

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Year:  2008        PMID: 18710983     DOI: 10.1148/radiol.2491071936

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  8 in total

1.  A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women.

Authors:  Jacob E D Levman; Cristina Gallego-Ortiz; Ellen Warner; Petrina Causer; Anne L Martel
Journal:  J Digit Imaging       Date:  2016-02       Impact factor: 4.056

2.  Techniques and applications of dynamic contrast enhanced magnetic resonance imaging in cancer.

Authors:  Stephanie L Barnes; Jennifer G Whisenant; Thomas E Yankeelov
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2014

3.  A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results.

Authors:  Jacob U Fluckiger; Mary E Loveless; Stephanie L Barnes; Martin Lepage; Thomas E Yankeelov
Journal:  Phys Med Biol       Date:  2013-03-04       Impact factor: 3.609

4.  The effects of intravoxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains.

Authors:  Ryan T Woodall; Stephanie L Barnes; David A Hormuth; Anna G Sorace; C Chad Quarles; Thomas E Yankeelov
Journal:  Magn Reson Med       Date:  2017-11-08       Impact factor: 4.668

5.  Correlation of tumor characteristics derived from DCE-MRI and DW-MRI with histology in murine models of breast cancer.

Authors:  Stephanie L Barnes; Anna G Sorace; Mary E Loveless; Jennifer G Whisenant; Thomas E Yankeelov
Journal:  NMR Biomed       Date:  2015-08-30       Impact factor: 4.044

Review 6.  Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review.

Authors:  Xiangyu Yang; Michael V Knopp
Journal:  J Biomed Biotechnol       Date:  2011-04-26

7.  Modeling the effect of intra-voxel diffusion of contrast agent on the quantitative analysis of dynamic contrast enhanced magnetic resonance imaging.

Authors:  Stephanie L Barnes; C Chad Quarles; Thomas E Yankeelov
Journal:  PLoS One       Date:  2014-10-02       Impact factor: 3.240

8.  Practical dynamic contrast enhanced MRI in small animal models of cancer: data acquisition, data analysis, and interpretation.

Authors:  Stephanie L Barnes; Jennifer G Whisenant; Mary E Loveless; Thomas E Yankeelov
Journal:  Pharmaceutics       Date:  2012       Impact factor: 6.321

  8 in total

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