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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.
Huang, Wei; Chen, Yiyi; Fedorov, Andriy; Li, Xia; Jajamovich, Guido H; Malyarenko, Dariya I; Aryal, Madhava P; LaViolette, Peter S; Oborski, Matthew J; O'Sullivan, Finbarr; Abramson, Richard G; Jafari-Khouzani, Kourosh; Afzal, Aneela; Tudorica, Alina; Moloney, Brendan; Gupta, Sandeep N; Besa, Cecilia; Kalpathy-Cramer, Jayashree; Mountz, James M; Laymon, Charles M; Muzi, Mark; Schmainda, Kathleen; Cao, Yue; Chenevert, Thomas L; Taouli, Bachir; Yankeelov, Thomas E; Fennessy, Fiona; Li, Xin.
Afiliação
  • Huang W; Oregon Health and Science University, Portland, OR.
  • Chen Y; Oregon Health and Science University, Portland, OR.
  • Fedorov A; Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
  • Li X; General ElectricGlobal Research, Niskayuna, NY.
  • Jajamovich GH; Icahn School ofMedicine at Mount Sinai, New York, NY.
  • Malyarenko DI; University of Michigan, Ann Arbor, MI.
  • Aryal MP; University of Michigan, Ann Arbor, MI.
  • LaViolette PS; Medical College of Wisconsin, Milwaukee, WI.
  • Oborski MJ; University of Pittsburgh,Pittsburgh, PA.
  • O'Sullivan F; University College, Cork, Ireland.
  • Abramson RG; Vanderbilt University, Nashville, TN.
  • Jafari-Khouzani K; MassachusettsGeneral Hospital and Harvard Medical School, Boston, MA.
  • Afzal A; Oregon Health and Science University, Portland, OR.
  • Tudorica A; Oregon Health and Science University, Portland, OR.
  • Moloney B; Oregon Health and Science University, Portland, OR.
  • Gupta SN; General ElectricGlobal Research, Niskayuna, NY.
  • Besa C; Icahn School ofMedicine at Mount Sinai, New York, NY.
  • Kalpathy-Cramer J; MassachusettsGeneral Hospital and Harvard Medical School, Boston, MA.
  • Mountz JM; University of Pittsburgh,Pittsburgh, PA.
  • Laymon CM; University of Pittsburgh,Pittsburgh, PA.
  • Muzi M; University of Washington, Seattle, WA.
  • Schmainda K; Medical College of Wisconsin, Milwaukee, WI.
  • Cao Y; University of Michigan, Ann Arbor, MI.
  • Chenevert TL; University of Michigan, Ann Arbor, MI.
  • Taouli B; Icahn School ofMedicine at Mount Sinai, New York, NY.
  • Yankeelov TE; Vanderbilt University, Nashville, TN.
  • Fennessy F; Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
  • Li X; Oregon Health and Science University, Portland, OR.
Tomography ; 2(1): 56-66, 2016 Mar.
Article em En | MEDLINE | ID: mdl-27200418
ABSTRACT
Dynamic contrast-enhanced MRI (DCE-MRI) has been widely used in tumor detection and therapy response evaluation. Pharmacokinetic analysis of DCE-MRI time-course data allows estimation of quantitative imaging biomarkers such as Ktrans(rate constant for plasma/interstitium contrast reagent (CR) transfer) and ve (extravascular and extracellular volume fraction). However, the use of quantitative DCE-MRI in clinical prostate imaging islimited, with uncertainty in arterial input function (AIF, i.e., the time rate of change of the concentration of CR in the blood plasma) determination being one of the primary reasons. In this multicenter data analysis challenge to assess the effects of variations in AIF quantification on estimation of DCE-MRI parameters, prostate DCE-MRI data acquired at one center from 11 prostate cancer patients were shared among nine centers. Each center used its site-specific method to determine the individual AIF from each data set and submitted the results to the managing center. Along with a literature population averaged AIF, these AIFs and their reference-tissue-adjusted variants were used by the managing center to perform pharmacokinetic analysis of the DCE-MRI data sets using the Tofts model (TM). All other variables including tumor region of interest (ROI) definition and pre-contrast T1 were kept the same to evaluate parameter variations caused by AIF variations only. Considerable pharmacokinetic parameter variations were observed with the within-subject coefficient of variation (wCV) of Ktrans obtained with unadjusted AIFs as high as 0.74. AIF-caused variations were larger in Ktrans than ve and both were reduced when reference-tissue-adjusted AIFs were used. The parameter variations were largely systematic, resulting in nearly unchanged parametric map patterns. The CR intravasation rate constant, kep (= Ktrans/ve), was less sensitive to AIF variation than Ktrans (wCV for unadjusted AIFs 0.45 for kepvs. 0.74 for Ktrans), suggesting that it might be a more robust imaging biomarker of prostate microvasculature than Ktrans.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2016 Tipo de documento: Article