Literature DB >> 19540690

Tumor perfusion assessed by dynamic contrast-enhanced MRI correlates to the grading of renal cell carcinoma: initial results.

Moritz Palmowski1, Isabel Schifferdecker, Stefan Zwick, Stephan Macher-Goeppinger, Hendrik Laue, Axel Haferkamp, Hans-Ulrich Kauczor, Fabian Kiessling, Peter Hallscheidt.   

Abstract

In this study, we investigated whether assessment of the tumor perfusion by dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) enables to estimate the morphologic grading of renal cell carcinomas. A total of 21 patients with suspected renal cell cancer were examined using a Gadobutrol-enhanced, dynamic saturation-recovery, turbo-fast, low-angle shot sequence. Tumor perfusion and the tissue-blood ratio within the entire tumor and the most highly vascularized part of the tumor were calculated according to the model of Miles. Immediately after examination, patients underwent surgery, and the results from imaging were compared with the morphological analysis of the histologic grading. Fourteen patients had G2 tumors, and seven patients had G3 tumors. Significantly higher perfusion values (p<0.05) were obtained in G3 tumors than in G2 tumors when the entire tumor area was considered (1.59+/-0.44(ml/g/min) vs. 1.08+/-0.38(ml/g/min)) or its most highly vascularized part (2.14+/-0.89(ml/g/min) vs. 1.40+/-0.49(ml/g/min)). By contrast, the tissue-blood ratios did not differ significantly between the two groups. In conclusion, unlike tissue-blood ratio, surrogate parameters of the tumor perfusion determined by DCE MRI seem to allow an estimation of the grading of renal cell carcinoma. However, further studies with high case numbers and including patients with G1 tumors are required to evaluate the full potential and clinical impact. Copyright (c) 2009 Elsevier Ireland Ltd. All rights reserved.

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Year:  2009        PMID: 19540690     DOI: 10.1016/j.ejrad.2009.05.042

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  13 in total

1.  Estimation of tissue perfusion by dynamic contrast-enhanced imaging: simulation-based evaluation of the steepest slope method.

Authors:  Gunnar Brix; Stefan Zwick; Jürgen Griebel; Christian Fink; Fabian Kiessling
Journal:  Eur Radiol       Date:  2010-04-21       Impact factor: 5.315

Review 2.  Imaging features of solid renal masses.

Authors:  Massimo Galia; Domenico Albano; Alberto Bruno; Antonino Agrusa; Giorgio Romano; Giuseppe Di Buono; Francesco Agnello; Giuseppe Salvaggio; Ludovico La Grutta; Massimo Midiri; Roberto Lagalla
Journal:  Br J Radiol       Date:  2017-07-13       Impact factor: 3.039

3.  Imaging techniques as predictive and prognostic biomarkers in renal cell carcinoma.

Authors:  Paul Nathan; Anup Vinayan
Journal:  Ther Adv Med Oncol       Date:  2013-03       Impact factor: 8.168

Review 4.  Recent advances in imaging techniques of renal masses.

Authors:  Ankita Aggarwal; Chandan J Das; Sanjay Sharma
Journal:  World J Radiol       Date:  2022-06-28

5.  Arterial spin-labeling MR imaging of renal masses: correlation with histopathologic findings.

Authors:  Rotem S Lanzman; Phil M Robson; Maryellen R Sun; Amish D Patel; Kimiknu Mentore; Andrew A Wagner; Elizabeth M Genega; Neil M Rofsky; David C Alsop; Ivan Pedrosa
Journal:  Radiology       Date:  2012-10-09       Impact factor: 11.105

6.  Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma.

Authors:  Shengsheng Lai; Lei Sun; Jialiang Wu; Ruili Wei; Shiwei Luo; Wenshuang Ding; Xilong Liu; Ruimeng Yang; Xin Zhen
Journal:  Cancer Manag Res       Date:  2021-02-04       Impact factor: 3.989

7.  Application of diffusion kurtosis tensor MR imaging in characterization of renal cell carcinomas with different pathological types and grades.

Authors:  Jie Zhu; Xiaojie Luo; Jiayin Gao; Saying Li; Chunmei Li; Min Chen
Journal:  Cancer Imaging       Date:  2021-03-16       Impact factor: 3.909

8.  An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom.

Authors:  Chengyue Wu; David A Hormuth; Ty Easley; Victor Eijkhout; Federico Pineda; Gregory S Karczmar; Thomas E Yankeelov
Journal:  Med Image Anal       Date:  2021-07-20       Impact factor: 13.828

Review 9.  Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma.

Authors:  Yan Wu; Young Suk Kwon; Mina Labib; David J Foran; Eric A Singer
Journal:  Dis Markers       Date:  2015-11-01       Impact factor: 3.434

10.  Assessment of Semiquantitative Parameters of Dynamic Contrast-Enhanced Perfusion MR Imaging in Differentiation of Subtypes of Renal Cell Carcinoma.

Authors:  Ahmed Abdel Khalek Abdel Razek; Amani Mousa; Ahmed Farouk; Nancy Nabil
Journal:  Pol J Radiol       Date:  2016-03-06
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