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

Wei Huang, Xin Li, Yiyi Chen, Xia Li, Ming Ching Chang, Matthew J. Oborski, Dariya I. Malyarenko, Mark Muzi, Guido H. Jajamovich, Andriy Fedorov, Alina Tudorica, Sandeep N. Gupta, Charles M. Laymon, Kenneth I. Marro, Hadrien A. Dyvorne, James V. Miller, Daniel P. Barbodiak, Thomas L. Chenevert, Thomas E. Yankeelov, James M. MountzPaul E. Kinahan, Ron Kikinis, Bachir Taouli, Fiona Fennessy, Jayashree Kalpathy-Cramer

Research output: Contribution to journalArticlepeer-review

111 Scopus citations


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), ve (extravascular extracellular volume fraction), and vp (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 neo- adjuvant 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 vp 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 kep (=K trans/ve, 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.

Original languageEnglish (US)
Pages (from-to)153-166
Number of pages14
JournalTranslational Oncology
Issue number1
StatePublished - Feb 2014

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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