Prediction of Immunotherapy Response with Geometric Deep Learning in Medical Imaging

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Cancer patients have limited treatment options. Immunotherapy is a promising strategy using the primary function of the immune system to rid the body cancer cells, showing long-term benefits for curing cancer. However, clinicians still do not have a reliable imaging biomarker that can reliably identify patients responding to immunotherapy prior to treatment. Image biomarkers can serve as a non-invasive alternative to characterize the spatial heterogeneity of tumors, but lack the statistical framework to predict therapy outcomes. Deep learning on graphs or manifolds, denoted as geometric deep learning, has recently gained interest for medical imaging problems, specifically in oncology and immunotherapy. They demonstrated the potential to process non-Euclidean domains, while addressing several challenges such as limited and unbalanced datasets.  The global objective is to develop image-driven predictive tools for immunotherapy response, based on novel concepts in geometric deep learning. The proposed methods will synthesize complex and multi-modal sources (CT images and clinical data) described with discriminative graphs, capturing underlying trends from longitudinal patient datasets. It will also allow the interpretation of deep features to capture drug effects on outcomes, as well recover spatiotemporal latent representations specific to immune phenotypes. To achieve this global objective, the main objectives are: (1) Develop a discriminative graph structure trained with a manifold-regularized deep neural network to predict responsive patients; (2) Parameterize the topology of the latent space with unsupervised domain adaptation to quantify therapeutic effects on outcomes; (3) Exploit concepts in Riemannian geometry and recurrent networks to create a prediction model for treatment response from spatiotemporal deep features; (4) Apply and validate the proposed methods on cancer patient datasets, comparing predicted results with actual outcomes. Based on our recent work on geometric deep learning, the proposal's contributions lie in a predictive platform generating representative knowledge to discover predictive biomarkers of the immune system response and tumor evolution, aimed for diagnosis and therapy. This research proposal will have a significant impact in computational medical imaging, as the geometric deep learning framework will allow to uncover domain specific relationships for cancer characterization. It will synthesize large heterogeneous sets of medical images acquired longitudinally and provide a comprehensive portrait of cancerous tumor evolution. Finally, it will propose new functionalities to disentangle complex structures into joint subsets, as well as adapting to different manifold topologies obtained with deep learning. From a clinical perspective, these developments will contribute insights and indications for early detection of cancer and predictors of therapy outcomes, as well as reliable forecasting tools in medicine.