We demonstrate feasibility of using a supervised deep learning method with DCE time-series data to obtain consistent numerical cutoff for tumor regions. DL based characterization is robust to fluctuations in DCE data due to protocol and patient physiology differences, which typically hinders such a classification with PK maps in clinical practice.
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