Literature DB >> 24772219

Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.

Wei Huang1, Xin Li1, Yiyi Chen1, Xia Li2, Ming-Ching Chang3, Matthew J Oborski4, Dariya I Malyarenko5, Mark Muzi6, Guido H Jajamovich7, Andriy Fedorov8, Alina Tudorica1, Sandeep N Gupta3, Charles M Laymon4, Kenneth I Marro6, Hadrien A Dyvorne7, James V Miller3, Daniel P Barbodiak9, Thomas L Chenevert5, Thomas E Yankeelov2, James M Mountz4, Paul E Kinahan6, Ron Kikinis8, Bachir Taouli7, Fiona Fennessy8, Jayashree Kalpathy-Cramer10.   

Abstract

Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.

Entities:  

Year:  2014        PMID: 24772219      PMCID: PMC3998693          DOI: 10.1593/tlo.13838

Source DB:  PubMed          Journal:  Transl Oncol        ISSN: 1936-5233            Impact factor:   4.243


  39 in total

Review 1.  Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability.

Authors:  S P Sourbron; D L Buckley
Journal:  Phys Med Biol       Date:  2011-12-15       Impact factor: 3.609

Review 2.  Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies.

Authors:  James P B O'Connor; Alan Jackson; Geoff J M Parker; Caleb Roberts; Gordon C Jayson
Journal:  Nat Rev Clin Oncol       Date:  2012-02-14       Impact factor: 66.675

3.  Multiparametric imaging of tumor response to therapy.

Authors:  Anwar R Padhani; Kenneth A Miles
Journal:  Radiology       Date:  2010-08       Impact factor: 11.105

Review 4.  Modeling tracer kinetics in dynamic Gd-DTPA MR imaging.

Authors:  P S Tofts
Journal:  J Magn Reson Imaging       Date:  1997 Jan-Feb       Impact factor: 4.813

5.  Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions.

Authors:  Tobias Heye; Matthew S Davenport; Jeffrey J Horvath; Sebastian Feuerlein; Steven R Breault; Mustafa R Bashir; Elmar M Merkle; Daniel T Boll
Journal:  Radiology       Date:  2012-12-06       Impact factor: 11.105

6.  Reproducibility assessment of a multiple reference tissue method for quantitative dynamic contrast enhanced-MRI analysis.

Authors:  Cheng Yang; Gregory S Karczmar; Milica Medved; Aytekin Oto; Marta Zamora; Walter M Stadler
Journal:  Magn Reson Med       Date:  2009-04       Impact factor: 4.668

7.  Temporal sampling requirements for the tracer kinetics modeling of breast disease.

Authors:  E Henderson; B K Rutt; T Y Lee
Journal:  Magn Reson Imaging       Date:  1998-11       Impact factor: 2.546

Review 8.  Beyond RECIST: molecular and functional imaging techniques for evaluation of response to targeted therapy.

Authors:  I M E Desar; C M L van Herpen; H W M van Laarhoven; J O Barentsz; W J G Oyen; W T A van der Graaf
Journal:  Cancer Treat Rev       Date:  2009-01-10       Impact factor: 12.111

9.  Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy.

Authors:  W Fraser Symmans; Florentia Peintinger; Christos Hatzis; Radhika Rajan; Henry Kuerer; Vicente Valero; Lina Assad; Anna Poniecka; Bryan Hennessy; Marjorie Green; Aman U Buzdar; S Eva Singletary; Gabriel N Hortobagyi; Lajos Pusztai
Journal:  J Clin Oncol       Date:  2007-09-04       Impact factor: 44.544

10.  Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging.

Authors:  M O Leach; B Morgan; P S Tofts; D L Buckley; W Huang; M A Horsfield; T L Chenevert; D J Collins; A Jackson; D Lomas; B Whitcher; L Clarke; R Plummer; I Judson; R Jones; R Alonzi; T Brunner; D M Koh; P Murphy; J C Waterton; G Parker; M J Graves; T W J Scheenen; T W Redpath; M Orton; G Karczmar; H Huisman; J Barentsz; A Padhani
Journal:  Eur Radiol       Date:  2012-05-07       Impact factor: 5.315

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  53 in total

1.  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

2.  Repeatability of dynamic contrast enhanced vp parameter in healthy subjects and patients with brain tumors.

Authors:  Moran Artzi; Gilad Liberman; Deborah T Blumenthal; Felix Bokstein; Orna Aizenstein; Dafna Ben Bashat
Journal:  J Neurooncol       Date:  2018-11-03       Impact factor: 4.130

3.  Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging.

Authors:  Alireza Mehrtash; Sandeep N Gupta; Dattesh Shanbhag; James V Miller; Tina Kapur; Fiona M Fennessy; Ron Kikinis; Andriy Fedorov
Journal:  J Med Imaging (Bellingham)       Date:  2016-03-01

4.  Novel High Spatiotemporal Resolution Versus Standard-of-Care Dynamic Contrast-Enhanced Breast MRI: Comparison of Image Quality.

Authors:  Courtney K Morrison; Leah C Henze Bancroft; Wendy B DeMartini; James H Holmes; Kang Wang; Ryan J Bosca; Frank R Korosec; Roberta M Strigel
Journal:  Invest Radiol       Date:  2017-04       Impact factor: 6.016

5.  Dynamic Contrast-enhanced MRI Detects Responses to Stroma-directed Therapy in Mouse Models of Pancreatic Ductal Adenocarcinoma.

Authors:  Jianbo Cao; Stephen Pickup; Cynthia Clendenin; Barbara Blouw; Hoon Choi; David Kang; Mark Rosen; Peter J O'Dwyer; Rong Zhou
Journal:  Clin Cancer Res       Date:  2018-12-26       Impact factor: 12.531

6.  Quantitative pharmacokinetic analysis of prostate cancer DCE-MRI at 3T: comparison of two arterial input functions on cancer detection with digitized whole mount histopathological validation.

Authors:  Fiona M Fennessy; Andriy Fedorov; Tobias Penzkofer; Kyung Won Kim; Michelle S Hirsch; Mark G Vangel; Paul Masry; Trevor A Flood; Ming-Ching Chang; Clare M Tempany; Robert V Mulkern; Sandeep N Gupta
Journal:  Magn Reson Imaging       Date:  2015-02-14       Impact factor: 2.546

7.  The impact of reliable prebolus T 1 measurements or a fixed T 1 value in the assessment of glioma patients with dynamic contrast enhancing MRI.

Authors:  Anna Tietze; Kim Mouridsen; Irene Klærke Mikkelsen
Journal:  Neuroradiology       Date:  2015-03-06       Impact factor: 2.804

8.  Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive.

Authors:  Jayashree Kalpathy-Cramer; John Blake Freymann; Justin Stephen Kirby; Paul Eugene Kinahan; Fred William Prior
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

Review 9.  MR Imaging Biomarkers in Oncology Clinical Trials.

Authors:  Richard G Abramson; Lori R Arlinghaus; Adrienne N Dula; C Chad Quarles; Ashley M Stokes; Jared A Weis; Jennifer G Whisenant; Eduard Y Chekmenev; Igor Zhukov; Jason M Williams; Thomas E Yankeelov
Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

Review 10.  Multiparametric MR Imaging of Breast Cancer.

Authors:  Habib Rahbar; Savannah C Partridge
Journal:  Magn Reson Imaging Clin N Am       Date:  2016-02       Impact factor: 2.266

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