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Abstract #0983

Recurrent Neural Network on DCE-MRI in Prostate Cancer

Xia Li1, Vivek Vaidya2, Sandeep Gupta1, Rakesh Mullick2, Oguz Akin3, and Dattesh Shanbhag2

1GE Global Research Center, Niskayuna, NY, United States, 2GE Global Research Center, Bengaluru, India, 3Memorial Sloan-Kettering Cancer Center, NY, United States

DCE-MRI has become an important protocol in mpMRI analysis of prostate cancer and it has been quantified typically using pharmaco-kinetic modelling and the estimated parameters are then used with other approaches (machine learning or deep learning (DL)) to characterize/discriminate tumor tissue against healthy tissue. However, it is not clear if applying DL to the DCE-MRI time series directly is beneficial for prostate cancer detection. Hence, we propose a DL based method to differentiate prostate tumor from healthy tissues at the voxel level using raw arbitrary signal DCE time-series itself. Overall, DL based tumor characterization provided similar detectability for prostate tumor when compared to Ktrans and ve maps. We also evaluated differences in tumor characterization when contrast agent concentration time-curves were used instead of arbitrary signal curves and found them to provide similar detectability.

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