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Abstract #4375

The Effects of AIF Quantification Variations on DCE-MRI Prediction of Soft Tissue Sarcoma Response to Preoperative Therapy: A Preliminary Multicenter Study

Kimberly Li1,2, Yiyi Chen2, Yun Yu2, Xia Li3, Andriy Fedorov4, Guido Jajamovich5, Dariya Malyarenko6, Madhava Aryal6, Peter LaViolette7, Matthew Oborski8, Finbarr O'Sullivan9, Richard Abramson10, Kourosh Jafari-Khouzani11, Aneela Afzal2, Alina Tudorica2, Brendan Moloney2, Sandeep Gupta3, Cecilia Besa5, Jayashree Kalpathy-Cramer11, James Mountz8, Charles Laymon8, Mark Muzi12, Paul Kinahan12, Kathleen Schmainda7, Yue Cao6, Thomas Chenevert6, Bachir Taouli5, Fiona Fennessy4, Thomas Yankeelov13, Xin Li2, and Wei Huang2

1International School of Beaverton, Beaverton, OR, United States, 2Oregon Health & Science University, Portland, OR, United States, 3GE Global Research, 4Brigham and Women's Hospital, 5Icahn School of Medicine at Mt Sinai, 6University of Michigan, 7Medical College of Wisconsin, 8University of Pittsburgh, 9University College, 10Vanderbilt University, 11Massachusetts General Hospital, 12University of Washington, 13The University of Texas at Austin

Soft tissue sarcoma DCE-MRI data collected at baseline and after one chemotherapy cycle were shared among nine centers and individual arterial input functions (AIFs) were quantified with center-specific methods. Pharmacokinetic (PK) modeling of the data was performed with these AIFs and the Tofts model. Considerable variations in estimated PK parameters and the corresponding percent changes were observed due to AIF variations. kep is less susceptible to AIF variation than Ktrans and may be a more robust imaging biomarker of microvasculature. kep percent change correlates in a uniformly negative relationship with necrosis percentage of resection specimen across all individually measured AIFs.

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