Literature DB >> 15120166

Magnetic resonance imaging contrast-enhanced relaxometry of breast tumors: an MRI multicenter investigation concerning 100 patients.

Pierre-Antoine Eliat1, Véronique Dedieu, Catherine Bertino, Véronique Bouté, Joëlle Lacroix, Jean-Marc Constans, Brigitte de Korvin, Catherine Vincent, Corinne Bailly, Francis Joffre, Jacques de Certaines, Dominique Vincensini.   

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using extracellular contrast agents has proved to be useful for the characterization of breast tumors. DCE-MRI has demonstrated a high sensitivity (around 95%) but a rather poor and controversial specificity, varying, according to the different studies, from 45% to 90%. In order to increase (a) the specificity and (b) the robustness of this quantitative approach in multicenter evaluation (five MRI units), a quantitative approach called dynamic relaxometry has been developed. According to the proposed method, the time-dependent longitudinal relaxation rate measured on region of interest of the lesion was calculated during the contrast uptake, after intravenous bolus injection of contrast agent. A specifically developed method was used for fast R(1) measurements. Relaxometry time curves are fitted to the Tofts model allowing the measurement of the parameters describing the enhancement curve (maximum relation rate enhancement, initial, 30-s and 60-s slopes) and the tissue parameters [transfer constant (K(trans) min(-1)) and extracellular extravascular space fraction (v(e))]. Correspondence factorial analysis followed by hierarchical ascendant classification are then performed on the different parameters. Higher K(trans) values were observed in infiltrative ductal carcinomas than in infiltrative lobular carcinomas, in agreement with data published by other groups. Specificity of DCE-MRI has been increased up to 85%, with a sensitivity of 95% with K(trans)/v(e) and enhancement index I (ratio of initial slope by maximum relaxation rate enhancement). A multiparametric data analysis of the calculated parameters opens the way to include quantitative image-based information in new nosologic approaches to breast tumors.

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Year:  2004        PMID: 15120166     DOI: 10.1016/j.mri.2004.01.024

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


  11 in total

1.  Molecular MR Imaging Probes.

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

2.  A clinically feasible method to estimate pharmacokinetic parameters in breast cancer.

Authors:  Jun Li; Yanming Yu; Yibao Zhang; Shanglian Bao; Chunxue Wu; Xiaoying Wang; Jie Li; Xiaopeng Zhang; Jiani Hu
Journal:  Med Phys       Date:  2009-08       Impact factor: 4.071

3.  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

4.  Evaluation of novel genetic algorithm generated schemes for positron emission tomography (PET)/magnetic resonance imaging (MRI) image fusion.

Authors:  K G Baum; E Schmidt; K Rafferty; A Krol; María Helguera
Journal:  J Digit Imaging       Date:  2011-12       Impact factor: 4.056

5.  Automatic segmentation of invasive breast carcinomas from dynamic contrast-enhanced MRI using time series analysis.

Authors:  Jagadaeesan Jayender; Sona Chikarmane; Ferenc A Jolesz; Eva Gombos
Journal:  J Magn Reson Imaging       Date:  2013-09-23       Impact factor: 4.813

6.  Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm.

Authors:  J Jayender; K G Vosburgh; E Gombos; A Ashraf; D Kontos; S C Gavenonis; F A Jolesz; K Pohl
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2012-07-12

7.  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

8.  Comparison of three physiologically-based pharmacokinetic models for the prediction of contrast agent distribution measured by dynamic MR imaging.

Authors:  Daniel P Barboriak; James R MacFall; Benjamin L Viglianti; Mark W Dewhirst Dvm
Journal:  J Magn Reson Imaging       Date:  2008-06       Impact factor: 4.813

9.  Statistical Learning Algorithm for in situ and invasive breast carcinoma segmentation.

Authors:  Jagadeesan Jayender; Eva Gombos; Sona Chikarmane; Donnette Dabydeen; Ferenc A Jolesz; Kirby G Vosburgh
Journal:  Comput Med Imaging Graph       Date:  2013-05-19       Impact factor: 4.790

10.  Can dynamic contrast-enhanced magnetic resonance imaging combined with texture analysis differentiate malignant glioneuronal tumors from other glioblastoma?

Authors:  Pierre-Antoine Eliat; Damien Olivié; Stephan Saïkali; Béatrice Carsin; Hervé Saint-Jalmes; Jacques D de Certaines
Journal:  Neurol Res Int       Date:  2011-12-01
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