Abstract #4120

Non-invasive Gleason Score Classification with VERDICT-MRI

Vanya V Valindria¹, Saurabh Singh², Eleni Chiou¹, Thomy Mertzanidou¹, Baris Kanber¹, Shonit Punwani², Marco Palombo¹, and Eleftheria Panagiotaki¹

¹Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom, ²Centre for Medical Imaging, University College London, London, United Kingdom

This study proposes non-invasive Gleason Score (GS) classification for prostate cancer with VERDICT-MRI using convolutional neural networks (CNNs). We evaluate GS classification using parametric maps from the VERDICT prostate model with compensated relaxation. We classify lesions using two CNN architectures: DenseNet and SE-ResNet. Results show that VERDICT achieves high GS classification performance using SE-ResNet with all parametric maps as input. Also in comparison with published GS classification multi-parametric MRI studies, VERDICT maps achieve higher metrics.

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