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, K^transhas the highest whereas τ_ihas the lowest variability due to AIF uncertainty. The use of reference-tissue-adjusted AIFs reduces parameter variations. k_ep and τ_iare 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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