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Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge

1Oregon Health and Science University, Portland, OR, USA.
2Vanderbilt University, Nashville, TN, USA.
3General Electric Global Research, Niskayuna, NY, USA.
4University of Pittsburgh, Pittsburgh, PA, USA.
5University of Michigan, Ann Arbor, MI, USA.
6University of Washington, Seattle, WA, USA.
7Icahn School of Medicine at Mount Sinai, New York, NY, USA.
8Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
9Duke University, Durham, NC, USA.
10Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Publication Date:
Transl Oncol.
Volume Number:
Issue Number:
Transl Oncol. 2014 Feb; 7(1): 153-66.
PubMed ID:
Appears in Collections:
Prostate Group, NA-MIC, NAC, SLICER, SPL
U01 CA140230/CA/NCI NIH HHS/United States
U01 CA142565/CA/NCI NIH HHS/United States
U01 CA148131/CA/NCI NIH HHS/United States
U01 CA151261/CA/NCI NIH HHS/United States
U01 CA154601/CA/NCI NIH HHS/United States
U01 CA154602/CA/NCI NIH HHS/United States
U01 CA166104/CA/NCI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
U01 CA172320/CA/NCI NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Huang W., Li X., Chen Y., Li X., Chang M-C., Oborski M.J., Malyarenko D.I., Muzi M., Jajamovich G.H., Fedorov A., Tudorica A., Gupta S.N., Laymon C.M., Marro K.I., Dyvorne H.A., Miller J.V., Barbodiak D.P., Chenevert T.L., Yankeelov T.E., Mountz J.M., Kinahan P.E., Kikinis R., Taouli B., Fennessy F.M., Kalpathy-Cramer J. Variations of Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Evaluation of Breast Cancer Therapy Response: A Multicenter Data Analysis Challenge. Transl Oncol. 2014 Feb; 7(1): 153-66. PMID: 24772219. PMCID: PMC3998693.
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Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as Ktrans (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 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 Ktrans 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 Ktrans 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 Ktrans) to 0.92 (for Ktrans 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 Ktrans and kep (=Ktrans/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.

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