Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge.
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.
Oregon Health and Science University, Portland, OR.
Wei
W
Huang
Oregon Health and Science University, Portland, OR.
Xin
X
Li
Oregon Health and Science University, Portland, OR.
Yiyi
Y
Chen
Vanderbilt University, Nashville, TN.
Xia
X
Li
General Electric Global Research, Niskayuna, NY.
Ming-Ching
MC
Chang
University of Pittsburgh, Pittsburgh, PA.
Matthew J
MJ
Oborski
University of Michigan, Ann Arbor, MI.
Dariya I
DI
Malyarenko
University of Washington, Seattle, WA.
Mark
M
Muzi
Icahn School of Medicine at Mount Sinai, New York, NY.
Guido H
GH
Jajamovich
Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Andriy
A
Fedorov
Oregon Health and Science University, Portland, OR.
Alina
A
Tudorica
General Electric Global Research, Niskayuna, NY.
Sandeep N
SN
Gupta
University of Pittsburgh, Pittsburgh, PA.
Charles M
CM
Laymon
University of Washington, Seattle, WA.
Kenneth I
KI
Marro
Icahn School of Medicine at Mount Sinai, New York, NY.
Hadrien A
HA
Dyvorne
General Electric Global Research, Niskayuna, NY.
James V
JV
Miller
Duke University, Durham, NC.
Daniel P
DP
Barbodiak
University of Michigan, Ann Arbor, MI.
Thomas L
TL
Chenevert
Vanderbilt University, Nashville, TN.
Thomas E
TE
Yankeelov
University of Pittsburgh, Pittsburgh, PA.
James M
JM
Mountz
University of Washington, Seattle, WA.
Paul E
PE
Kinahan
Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Ron
R
Kikinis
Icahn School of Medicine at Mount Sinai, New York, NY.
Bachir
B
Taouli
Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Fiona
F
Fennessy
Massachusetts General Hospital and Harvard Medical School, Boston, MA.
Jayashree
J
Kalpathy-Cramer