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
| Autor: | Thomas L. Chenevert, Brendan Moloney, Matthew J. Oborski, Kathleen M. Schmainda, Jayashree Kalpathy-Cramer, Guido H. Jajamovich, Kourosh Jafari-Khouzani, Madhava P. Aryal, Yue Cao, Peter S. LaViolette, Sandeep N. Gupta, Bachir Taouli, Mark Muzi, Andriy Fedorov, James M. Mountz, Paul E. Kinahan, Finbarr O'Sullivan, Wei Huang, Yiyi Chen, Aneela Afzal, Dariya I. Malyarenko, Alina Tudorica, Xin Li, Fiona M. Fennessy, Cecilia Besa, Richard G. Abramson, Thomas E. Yankeelov, Charles M. Laymon, Xia Li |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Male
Imaging biomarker DCE-MRI Coefficient of variation Pharmacokinetic modeling Contrast Media Variation Models Biological 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Prostate Image Interpretation Computer-Assisted Medicine Humans Radiology Nuclear Medicine and imaging Arterial input function cardiovascular diseases arterial input function variation shutter-speed model prostate Research Articles medicine.diagnostic_test Neovascularization Pathologic business.industry Information Dissemination Prostatic Neoplasms Reproducibility of Results Shutter-speed model Magnetic resonance imaging Arteries Magnetic Resonance Imaging Data set Dynamic contrast medicine.anatomical_structure biological phenomena cell phenomena and immunity business Nuclear medicine 030217 neurology & neurosurgery Algorithms Biomedical engineering |
| Zdroj: | Tomography; Volume 5; Issue 1; Pages: 99-109 Tomography Volume 5 Issue 1 Pages 99-109 |
| ISSN: | 2379-139X |
| DOI: | 10.18383/j.tom.2018.00027 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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