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The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II

Institution:
1Oregon Health and Science University, Portland, OR, USA.
2Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Mar-2019
Journal:
Tomography
Volume Number:
5
Issue Number:
1
Pages:
99-109
Citation:
Tomography. 2019 Mar;5(1):99-109.
PubMed ID:
30854447
PMCID:
PMC6403046
Keywords:
DCE-MRI, arterial input function, prostate, shutter-speed model, variation
Appears in Collections:
SPL
Sponsors:
U01 CA154602/CA/NCI NIH HHS/United States
U01 CA151261/CA/NCI NIH HHS/United States
U01 CA183848/CA/NCI NIH HHS/United States
U01 CA154601/CA/NCI NIH HHS/United States
U01 CA148131/CA/NCI NIH HHS/United States
U01 CA176110/CA/NCI NIH HHS/United States
U01 CA172320/CA/NCI NIH HHS/United States
U01 CA142565/CA/NCI NIH HHS/United States
U01 CA166104/CA/NCI NIH HHS/United States
U01 CA140230/CA/NCI NIH HHS/United States
Generated Citation:
Huang W., Chen Y., Fedorov A., Li X., Jajamovich G.H., Malyarenko D.I., Aryal M.P., LaViolette P.S., Oborski M.J., O'Sullivan F., Abramson R.G., Jafari-Khouzani K., Afzal A., Tudorica A., Moloney B., Gupta S.N., Besa C., Kalpathy-Cramer J., Mountz J.M., Laymon C.M., Muzi M., Kinahan P.E., Schmainda K., Cao Y., Chenevert T.L., Taouli B., Yankeelov T.E., Fennessy F., Li X. The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge, Part II. Tomography. 2019 Mar;5(1):99-109. PMID: 30854447. PMCID: PMC6403046.
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This multicenter study evaluated the effect of variations in arterial input function (AIF) determination on pharmacokinetic (PK) analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using the shutter-speed model (SSM). Data acquired from eleven prostate cancer patients were shared among nine centers. Each center used a site-specific method to measure the individual AIF from each data set and submitted the results to the managing center. These AIFs, their reference tissue-adjusted variants, and a literature population-averaged AIF, were used by the managing center to perform SSM PK analysis to estimate Ktrans (volume transfer rate constant), ve (extravascular, extracellular volume fraction), kep (efflux rate constant), and τi (mean intracellular water lifetime). All other variables, including the definition of the tumor region of interest and precontrast T1 values, were kept the same to evaluate parameter variations caused by variations in only the AIF. Considerable PK parameter variations were observed with within-subject coefficient of variation (wCV) values of 0.58, 0.27, 0.42, and 0.24 for Ktrans, ve, kep, and τi, respectively, using the unadjusted AIFs. Use of the reference tissue-adjusted AIFs reduced variations in Ktrans and ve (wCV = 0.50 and 0.10, respectively), but had smaller effects on kep and τi (wCV = 0.39 and 0.22, respectively). kep is less sensitive to AIF variation than Ktrans, suggesting it may be a more robust imaging biomarker of prostate microvasculature. With low sensitivity to AIF uncertainty, the SSM-unique τi parameter may have advantages over the conventional PK parameters in a longitudinal study.