Abstract #5033

Improving Prostate Cancer Detection Using Bi-parametric MRI with Conditional Generative Adversarial Networks

Alexandros Patsanis¹, Mohammed R. S. Sunoqrot^1,2, Elise Sandsmark², Sverre Langørgen², Helena Bertilsson^3,4, Kirsten Margrete Selnæs^1,2, Hao Wang⁵, Tone Frost 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

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Analysis, Deep LearningThis study investigated automated detection and localization of prostate cancer on biparametric MRI (bpMRI). Conditional Generative Adversarial Networks (GANs) were used for image-to-image translation. We used an in-house collected dataset of 811 patients with T2- and diffusion-weighted MR images for training, validation, and testing of two different bpMRI models in comparison to three single modality models (T2-weighted, ADC, high b-value diffusion). The bpMRI models outperformed T2-weighted and high b-value models, but not ADC. GANs show promise for detecting and localizing prostate cancer on MRI, but further research is needed to improve stability, performance and generalizability of the bpMRI models.

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