Literature DB >> 21661044

Dynamic contrast-enhanced-MRI of tumor hypoxia.

Tormod A M Egeland1, Kristine Gulliksrud, Jon-Vidar Gaustad, Berit Mathiesen, Einar K Rofstad.   

Abstract

Patients with highly hypoxic primary tumors show increased frequency of locoregional treatment failure and poor survival rates and may benefit from particularly aggressive treatment. The potential of gadolinium diethylene-triamine penta-acetic acid-based dynamic contrast-enhanced-MRI in assessing tumor hypoxia was investigated in this preclinical study. Xenografted tumors of eight human melanoma lines were subjected to dynamic contrast-enhanced-MRI and measurement of the fraction of radiobiologically hypoxic cells and the fraction of pimonidazole-positive hypoxic cells. Tumor images of K(trans) (the volume transfer constant of gadolinium diethylene-triamine penta-acetic acid) and v(e) (the fractional distribution volume of gadolinium diethylene-triamine penta-acetic acid) were produced by pharmacokinetic analysis of the dynamic contrast-enhanced-MRI data, and K(trans) and v(e) frequency distributions of the non-necrotic tumor tissue were established and related to the extent of hypoxia. Tumors showing high K(trans) values and high v(e) values had low fractions of hypoxic cells, whereas tumors showing both low K(trans) values and low v(e) values had high hypoxic fractions. K(trans) differentiated better between tumors with low and high hypoxic fractions than did v(e). This study supports the current attempts to establish dynamic contrast-enhanced-MRI as a method for assessing the extent of hypoxia in human tumors, and it provides guidelines for the clinical development of valid assays.
Copyright © 2011 Wiley Periodicals, Inc.

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Year:  2011        PMID: 21661044     DOI: 10.1002/mrm.23014

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  20 in total

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Review 7.  Imaging tumor hypoxia to advance radiation oncology.

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8.  GdDO3NI, a nitroimidazole-based T1 MRI contrast agent for imaging tumor hypoxia in vivo.

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

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10.  Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results.

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