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 Li^1,2, 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. k_ep is less susceptible to AIF variation than K^trans and may be a more robust imaging biomarker of microvasculature. k_ep percent change correlates in a uniformly negative relationship with necrosis percentage of resection specimen across all individually measured AIFs.

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