Literature DB >> 20665785

Noncompartmental kinetic analysis of DCE-MRI data from malignant tumors: Application to glioblastoma treated with bevacizumab.

Ruediger E Port1, Lisa J Bernstein, Daniel P Barboriak, Lu Xu, Timothy P L Roberts, Nicholas van Bruggen.   

Abstract

Dynamic contrast enhanced MRI contrast agent kinetics in malignant tumors are typically complex, requiring multicompartment tumor models for adequate description. For consistent comparisons among tumors or among successive studies of the same tumor, we propose to estimate the total contrast agent-accessible volume fraction of tumor, including blood plasma, v(pe), and an average transfer rate constant across all tumor compartments, K(trans.av), by fitting a three-compartment tumor model and then calculating the area under the tumor impulse-response function (= v(pe)) and the ratio area under the tumor impulse response function over mean residence time in tumor (= K(trans.av)). If the duration of dynamic contrast enhanced MRI was too short to extrapolate the tumor impulse-response function to infinity with any confidence, then conditional parameters v(pe)(*) and K(trans.av*) should be calculated from the available incomplete impulse response function. Median decreases of 33% were found for both v(pe)(*) and K(trans.av*) in glioblastoma patients (n = 16) 24 hours after the administration of bevacizumab (P < 0.001). Median total contrast-enhancing tumor volume was reduced by 18% (P < 0.0001). The combined changes of tumor volume, v(pe)(*), and K(trans.av*) suggest a reduction of true v(pe), possibly accompanied by a reduction of true K(trans.av). The proposed method provides estimates of a scale and a shape parameter to describe contrast agent kinetics of varying complexity in a uniform way.

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Year:  2010        PMID: 20665785     DOI: 10.1002/mrm.22399

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


  13 in total

1.  Intratumor distribution and test-retest comparisons of physiological parameters quantified by dynamic contrast-enhanced MRI in rat U251 glioma.

Authors:  Madhava P Aryal; Tavarekere N Nagaraja; Stephen L Brown; Mei Lu; Hassan Bagher-Ebadian; Guangliang Ding; Swayamprava Panda; Kelly Keenan; Glauber Cabral; Tom Mikkelsen; James R Ewing
Journal:  NMR Biomed       Date:  2014-08-14       Impact factor: 4.044

2.  3'-deoxy-3'-18F-fluorothymidine PET and MRI for early survival predictions in patients with recurrent malignant glioma treated with bevacizumab.

Authors:  Johannes Schwarzenberg; Johannes Czernin; Timothy F Cloughesy; Benjamin M Ellingson; Whitney B Pope; Cheri Geist; Magnus Dahlbom; Daniel H S Silverman; Nagichettiar Satyamurthy; Michael E Phelps; Wei Chen
Journal:  J Nucl Med       Date:  2011-12-12       Impact factor: 10.057

3.  Dynamic contrast-enhanced MRI may be helpful to predict response and prognosis after bevacizumab treatment in patients with recurrent high-grade glioma: comparison with diffusion tensor and dynamic susceptibility contrast imaging.

Authors:  Yae Won Park; Sung Soo Ahn; Ju Hyung Moon; Eui Hyun Kim; Seok-Gu Kang; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Neuroradiology       Date:  2021-03-23       Impact factor: 2.804

4.  Dynamic contrast-enhanced and diffusion-weighted MR imaging in the characterisation of small, non-palpable solid testicular tumours.

Authors:  Lucia Manganaro; Matteo Saldari; Carlotta Pozza; Valeria Vinci; Daniele Gianfrilli; Ermanno Greco; Giorgio Franco; Maria Eleonora Sergi; Michele Scialpi; Carlo Catalano; Andrea M Isidori
Journal:  Eur Radiol       Date:  2017-08-30       Impact factor: 5.315

5.  Predicting Glioblastoma Response to Bevacizumab Through MRI Biomarkers of the Tumor Microenvironment.

Authors:  Andreas Stadlbauer; Karl Roessler; Max Zimmermann; Michael Buchfelder; Andrea Kleindienst; Arnd Doerfler; Gertraud Heinz; Stefan Oberndorfer
Journal:  Mol Imaging Biol       Date:  2019-08       Impact factor: 3.488

6.  Modulating antiangiogenic resistance by inhibiting the signal transducer and activator of transcription 3 pathway in glioblastoma.

Authors:  John de Groot; Ji Liang; Ling-Yuan Kong; Jun Wei; Yuji Piao; Gregory Fuller; Wei Qiao; Amy B Heimberger
Journal:  Oncotarget       Date:  2012-09

7.  GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.

Authors:  Yu-Han H Hsu; Ziyin Huang; Gregory Z Ferl; Chee M Ng
Journal:  PLoS One       Date:  2015-03-18       Impact factor: 3.240

8.  Measurement of blood-brain barrier permeability with t1-weighted dynamic contrast-enhanced MRI in brain tumors: a comparative study with two different algorithms.

Authors:  Maurizio Bergamino; Laura Saitta; Laura Barletta; Laura Bonzano; Giovanni Luigi Mancardi; Lucio Castellan; Jean Louis Ravetti; Luca Roccatagliata
Journal:  ISRN Neurosci       Date:  2013-02-20

9.  Assessing vascular effects of adding bevacizumab to neoadjuvant chemotherapy in osteosarcoma using DCE-MRI.

Authors:  J Guo; J O Glass; M B McCarville; B L Shulkin; V M Daryani; C F Stewart; J Wu; S Mao; J R Dwek; L M Fayad; J E Madewell; F Navid; N C Daw; W E Reddick
Journal:  Br J Cancer       Date:  2015-10-13       Impact factor: 7.640

Review 10.  Quantitative Perfusion and Permeability Biomarkers in Brain Cancer from Tomographic CT and MR Images.

Authors:  Armin Eilaghi; Timothy Yeung; Christopher d'Esterre; Glenn Bauman; Slav Yartsev; Jay Easaw; Enrico Fainardi; Ting-Yim Lee; Richard Frayne
Journal:  Biomark Cancer       Date:  2016-07-03
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