Abstract #0816

Prostate Cancer Detection on T2-weighted MR images with Generative Adversarial Networks

Alexandros Patsanis¹, Mohammed R. S. Sunoqrot ¹, Elise Sandsmark ², Sverre Langørgen ², Helena Bertilsson ^3,4, Kirsten M. Selnæs ^1,2, Hao Wang⁵, Tone F. Bathen ^1,2, and Mattijs Elschot ^1,2

¹Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway, ²Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway, ³Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology - NTNU, Trondheim, Norway, ⁴Department of Urology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway, ⁵Department of Computer Science, Norwegian University of Science and Technology - NTNU, Gjøvik, Norway

Generative Adversarial Networks (GANs) were evaluated for detection and visualization of prostate cancer, proposing an automated end-to-end pipeline. Two GANs were trained and tested with T2-weighted images from an in-house dataset of 646 patients. The weakly-supervised GAN performed better (AUC=0.785) than unsupervised GAN (AUC=0.462). The performance of the GANs was dependent on pre-processing parameters. The PROSTATEx dataset (N=204) was used for external validation, giving an AUC of 0.642. The weakly-supervised GAN showed promise for detecting and localizing prostate cancer on T2W MRI, but further research is necessary to improve model performance and generalizability.

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