Literature DB >> 14508784

Correlation of dynamic contrast enhancement MRI parameters with microvessel density and VEGF for assessment of angiogenesis in breast cancer.

Min-Ying Su1, Yun-Chung Cheung, John P Fruehauf, Hon Yu, Orhan Nalcioglu, Eugene Mechetner, Ainura Kyshtoobayeva, Shin-Cheh Chen, Swei Hsueh, Christine E McLaren, Yung-Liang Wan.   

Abstract

PURPOSE: To investigate the association between parameters obtained from dynamic contrast enhanced MRI (DCE-MRI) of breast cancer using different analysis approaches, as well as their correlation with angiogenesis biomarkers (vascular endothelial growth factor and vessel density).
MATERIALS AND METHODS: DCE-MRI results were obtained from 105 patients with breast cancer (108 lesions). Three analysis methods were applied: 1) whole tumor analysis, 2) regional hot-spot analysis, and 3) intratumor pixel-by-pixel analysis. Early enhancement intensities and fitted pharmacokinetic parameters were studied. Paraffin blocks of 71 surgically resected specimens were analyzed by immunohistochemical staining to measure microvessel counts (with CD31) and vascular endothelial growth factor (VEGF) expression levels.
RESULTS: MRI parameters obtained from the three analysis methods showed significant correlations (P < 0.0001), but a substantial dispersion from the linear regression line was noted (r = 0.72-0.97). The entire region of interest (ROI) vs. pixel population analyses had a significantly higher association compared to the entire ROI vs. hot-spot analyses. Cancer specimens with high VEGF expression had significantly higher CD31 microvessel densities than did specimens with low VEGF levels (P < 0.005). No significant association was found between MRI parameters obtained from the three analysis strategies and IHC based measurements of angiogenesis.
CONCLUSION: A consistent analysis strategy was important in the DCE-MRI study. In this series, none of these strategies yielded results for MRI based quantitation of tumor vascularity that were associated with IHC based measurements. Therefore, different analyses could not account for the lack of association. Copyright 2003 Wiley-Liss, Inc.

Entities:  

Mesh:

Substances:

Year:  2003        PMID: 14508784     DOI: 10.1002/jmri.10380

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


  44 in total

1.  An expectation-maximisation approach for simultaneous pixel classification and tracer kinetic modelling in dynamic contrast enhanced-magnetic resonance imaging.

Authors:  Mario Sansone; Roberta Fusco; Antonella Petrillo; Mario Petrillo; Marcello Bracale
Journal:  Med Biol Eng Comput       Date:  2010-11-03       Impact factor: 2.602

2.  Dynamic contrast-enhanced and diffusion MRI show rapid and dramatic changes in tumor microenvironment in response to inhibition of HIF-1alpha using PX-478.

Authors:  Bénédicte F Jordan; Matthew Runquist; Natarajan Raghunand; Amanda Baker; Ryan Williams; Lynn Kirkpatrick; Garth Powis; Robert J Gillies
Journal:  Neoplasia       Date:  2005-05       Impact factor: 5.715

3.  Molecular MR Imaging Probes.

Authors:  Umar Mahmood; Lee Josephson
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2005-04       Impact factor: 10.961

4.  Incorporating a vascular term into a reference region model for the analysis of DCE-MRI data: a simulation study.

Authors:  A Z Faranesh; T E Yankeelov
Journal:  Phys Med Biol       Date:  2008-04-25       Impact factor: 3.609

5.  Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study.

Authors:  Chunhao Wang; Ergys Subashi; Xiao Liang; Fang-Fang Yin; Zheng Chang
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

Review 6.  Review of treatment assessment using DCE-MRI in breast cancer radiation therapy.

Authors:  Chun-Hao Wang; Fang-Fang Yin; Janet Horton; Zheng Chang
Journal:  World J Methodol       Date:  2014-06-26

7.  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

8.  Invasive breast cancer: predicting disease recurrence by using high-spatial-resolution signal enhancement ratio imaging.

Authors:  Ka-Loh Li; Savannah C Partridge; Bonnie N Joe; Jessica E Gibbs; Ying Lu; Laura J Esserman; Nola M Hylton
Journal:  Radiology       Date:  2008-07       Impact factor: 11.105

Review 9.  Magnetic resonance imaging of psoriatic arthritis: insight from traditional and three-dimensional analysis.

Authors:  Saara M S Totterman
Journal:  Curr Rheumatol Rep       Date:  2004-08       Impact factor: 4.592

10.  Mechanistic modelling of dynamic MRI data predicts that tumour heterogeneity decreases therapeutic response.

Authors:  R Venkatasubramanian; R B Arenas; M A Henson; N S Forbes
Journal:  Br J Cancer       Date:  2010-07-13       Impact factor: 7.640

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.