Literature DB >> 18538522

Evaluation of lung tumor perfusion by dynamic contrast-enhanced MRI.

Sandra Pauls1, Felix M Mottaghy, Stefan A Schmidt, Stefan Krüger, Peter Möller, Hans-Jürgen Brambs, Arthur Wunderlich.   

Abstract

The characterization of solid pulmonary lesions with imaging methods remains a diagnostic challenge. The aim of this study was to correlate kinetic parameters of dynamic perfusion magnetic resonance imaging (MRI) with histological tumor classification. Dynamic contrast-enhanced MRI of 31 patients with pulmonary masses (five benign lesions, 26 malignant tumors) was acquired in the tumor areas every 20 s for a mean duration of 124 s. Contrast uptake (CU) was measured by signal analysis in regions of interest (ROIs). The beginning and duration of CU, maximum CU (MCU, % of baseline), maximum contrast upslope (%/s) and the delay to the maximum contrast upslope (s) were calculated. All lesions were classified histologically. The beginning of CU correlated significantly with the MCU delay in all lesions (P=.033). The frequency of a plateau phase was higher in malignant tumors compared to benign lesions (P=.031). Masses with a high MCU showed more frequently a washout of contrast medium after a plateau phase (P=.006) and a higher maximum contrast upslope (P<.001). The MCU delay time was shorter in adenocarcinoma than in squamous cell carcinoma (P=.004). These results indicate that dynamic contrast enhanced MRI might become instrumental in differentiating benign from malignant intrapulmonary tumors and distinguishing adenocarcinoma from squamous cell carcinoma.

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Year:  2008        PMID: 18538522     DOI: 10.1016/j.mri.2008.04.005

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  8 in total

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Authors:  Edward E Graves; Amit Maity; Quynh-Thu Le
Journal:  Semin Radiat Oncol       Date:  2010-07       Impact factor: 5.934

2.  Intravoxel incoherent motion diffusion-weighted MR imaging in differentiation of lung cancer from obstructive lung consolidation: comparison and correlation with pharmacokinetic analysis from dynamic contrast-enhanced MR imaging.

Authors:  Li-li Wang; Jiang Lin; Kai Liu; Cai-zhong Chen; Hui Liu; Peng Lv; Cai-xia Fu; Meng-su Zeng
Journal:  Eur Radiol       Date:  2014-05-01       Impact factor: 5.315

3.  Contrast-enhanced magnetic resonance imaging with a novel nano-size contrast agent for the clinical diagnosis of patients with lung cancer.

Authors:  Jianwei Gao; Lei Li; Xia Liu; Rui Guo; Bin Zhao
Journal:  Exp Ther Med       Date:  2018-04-30       Impact factor: 2.447

4.  MRI of the lung (3/3)-current applications and future perspectives.

Authors:  Jürgen Biederer; S Mirsadraee; M Beer; F Molinari; C Hintze; G Bauman; M Both; E J R Van Beek; J Wild; M Puderbach
Journal:  Insights Imaging       Date:  2012-01-15

5.  Diagnostic Accuracy of Dynamic Contrast Enhanced Magnetic Resonance Imaging in Characterizing Lung Masses.

Authors:  Nagihan Inan; Arzu Arslan; Muhammed Donmez; Hasan Tahsin Sarisoy
Journal:  Iran J Radiol       Date:  2016-02-27       Impact factor: 0.212

6.  Quantitative monitoring of paramagnetic contrast agents and their allocation in plant tissues via DCE-MRI.

Authors:  Simon Mayer; Eberhard Munz; Sebastian Hammer; Steffen Wagner; Andre Guendel; Hardy Rolletschek; Peter M Jakob; Ljudmilla Borisjuk; Thomas Neuberger
Journal:  Plant Methods       Date:  2022-04-11       Impact factor: 4.993

7.  Gadolinium-labeled affibody-XTEN recombinant vector for detection of HER2+ lesions of ovarian cancer lung metastasis using quantitative MRI.

Authors:  Alireza Nomani; Geng Li; Siavash Yousefi; Shawn Wu; Obeid M Malekshah; Shahryar K Nikkhoi; Mehrdad Pourfathi; Rahim Rizi; Arash Hatefi
Journal:  J Control Release       Date:  2021-07-18       Impact factor: 11.467

8.  The role of dynamic magnetic resonance imaging in the evaluation of pulmonary nodules and masses.

Authors:  Fatih Alper; Adile Tuba Polat Kurt; Yener Aydin; Mesut Ozgokce; Metin Akgun
Journal:  Med Princ Pract       Date:  2012-07-10       Impact factor: 1.927

  8 in total

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