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Abstract #0625

Effects of AIF Quantification Variations on Shutter-Speed Pharmacokinetic Modeling of Prostate DCE-MRI Data: A Multicenter Data Analysis Challenge, Part II

Wei Huang1, Yiyi Chen1, Andriy Fedorov2, Xia Li3, Guido Jajamovich4, Dariya Malyarenko5, Madhava Aryal5, Peter LaViolette6, Matthew Oborski7, Finbarr O'Sullivan8, Richard Abramson9, Kourosh Jafari-Khouzani10, Aneela Afzal1, Alina Tudorica1, Brendan Moloney1, Sandeep Gupta3, Cecilia Besa4, Jayashree Kalpathy-Cramer10, James Mountz7, Charles Laymon7, Mark Muzi11, Paul Kinahan11, Kathleen Schmainda6, Yue Cao5, Thomas Chenevert5, Bachir Taouli4, Thomas Yankeelov12, Fiona Fennessy2, and Xin Li1

1Oregon Health & Science University, Portland, OR, United States, 2Brigham and Women's Hospital, 3GE Global Research, 4Icahn School of Medicine at Mt Sinai, 5University of Michigan, 6Medical College of Wisconsin, 7University of Pittsburgh, 8University College, 9Vanderbilt University, 10Massachusetts General Hospital, 11University of Washington, 12The University of Texas at Austin

Prostate tumor DCE-MRI data sets from 11 patients were shared among nine institutions, which determined AIFs using site-specific methods. The managing center performed pharmacokinetic data analysis using the Shutter-Speed model and these AIFs, and their scaled variants obtained with the reference-tissue method. Among the estimated parameters, Ktrans has the highest whereas τi has the lowest variability due to AIF uncertainty. The use of reference-tissue-adjusted AIFs reduces parameter variations. kep and τi are nearly insensitive to AIF scaling, suggesting that they may be robust imaging biomarkers in multicenter DCE-MRI trials where accurate and consistent AIF determination may be unattainable across sites.

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