Magnetic resonance imaging (MRI) plays a critical role in the planning and monitoring of hepatocellular carcinomas (HCC) treated with locoregional therapies, in order to assess disease progression or recurrence. Dynamic contrast-enhanced (DCE)-MRI sequences offer temporal data on tumor enhancement characteristics which has strong prognostic value. Yet, predicting follow-up DCE-MR images from which tumor enhancement and viability can be measured, before treatment of HCC actually begins, remains an unsolved problem given the complexity of spatial and temporal information. We propose an approach to predict future DCE-MRI examinations following transarterial chemoembolization (TACE) by learning the spatio-temporal features related to HCC response from pre-TACE images. A novel Spatial-Temporal Discriminant Graph Neural Network (STDGNN) based on graph convolutional networks is presented. First, embeddings of viable, equivocal and non-viable HCCs are separated within a joint low-dimensional latent space, which is created using a discriminant neural network representing tumor-specific features. Spatial tumoral features from independent MRI volumes are then extracted with a structural branch, while dynamic features are extracted from the multi-phase sequence with a separate temporal branch. The model extracts spatio-temporal features by a joint minimization of the network branches. At testing, a pre-TACE diagnostic DCE-MRI is embedded on the discriminant spatio-temporal latent space, which is then translated to the follow-up domain space, thus allowing to predict the post-TACE DCE-MRI describing HCC treatment response. A dataset of 366 HCC’s from liver cancer patients was used to train and test the model using DCE-MRI examinations with associated pathological outcomes, with the spatio-temporal framework yielding 93.5% classification accuracy in response identification, and generating follow-up images yielding insignificant differences in perfusion parameters compared to ground-truth post-TACE examinations.
w The original article contained a mistake. The correct author biographies of Liset Vazquez Romaguera, Catherine Huet and Guilllaume Gilbert are shown below: Liset Vazquez Romaguera, M.Sc., is a Ph.D. candidate in Biomedical Engineering at Polytechnique Montreal. She completed her Masters' in Electrical Engineering at the Federal University of Amazonas in 2017. Catherine Huet, M.Sc., completed her Masters' in Preventive Nutrition at Université de Montréal in 2003 and in Human Nutrition at McGill University in 2011. She is a research agent at the CHUM Hospital in Montreal. Guilllaume Gilbert, Ph.D., is a MR clinical scientist at Philips Healthcare and an associate professor of radiology at University of Montreal. He completed his Ph.D. in physics in 2009 and specialises in MRI sequence development. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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