Abstract #3923

Early Prediction of Soft Tissue Sarcoma Response to Preoperative Therapy Using DCE-MRI Texture Features

Tristan Xiao¹, Archana Machireddy², Xubo Song², Alina Tudorica², Aneela Afzal², May Mishal², Brooke Beckett², Megan Holtorf², Torrie Aston², Christopher Ryan², Wei Huang², and Guillaume Thibault²

¹Saratoga High School, Saratoga, CA, United States, ²OHSU, Portland, OR, United States

23 patients with soft tissue sarcoma (STS) (25 tumors) underwent DCE-MRI before and after one cycle of preoperative chemoradiotherapy. Extended Tofts model (ETM) and Shutter-Speed model (SSM) were used for pharmacokinetic (PK) analysis of DCE-MRI data and generating voxel based PK parametric maps, from which texture features were extracted using different statistical matrix methods. Changes in SZM and RLM features consistently provided good early prediction of therapy response, while more features from the SSM PK maps were good predictors of response than the ETM maps.

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