Spatio-temporal Generative Manifolds for Prediction of Immunotherapy Response

点击翻译按钮获取中文摘要

Immunotherapy has been shown to provide long-term and curative benefits to cancer patients who have limited treatment options, with the potential to revolutionize cancer therapy and become an important part of comprehensive treatment options for curing many diseases. But while these advances are encouraging, there is still no reliable imaging tumor biomarker that can accurately identify patients responding to immunotherapy effects or monitor their response to adjust the therapeutic strategy, with highest reported detection rates of 71%. Towards this end, radiomics are quantitative non-invasive alternatives to biopsies generating image-driven biomarkers characterizing tumor spatial heterogeneity and better identify immunotherapy response, but lack the statistical framework to predict therapy outcomes.******The global objective of this research program is to develop a prediction platform for immunotherapy response from discriminant manifold embeddings, reducing complex and multi-modal medical imaging data in a temporal domain using locality preservation criterions. The spatial-temporal manifold learning framework will be able to recover radiomic signatures which are specific to immune phenotypes. The research program will incorporate highly innovative theoretical developments and applied projects with the following objectives: (1) Develop a manifold embedding technique mapping medical images of responsive and non-responsive patients to immunotherapy using probabilistic deep neural networks; (2) Parameterize manifolds to quantify therapeutic effects on outcomes, handling challenges in gradient-based optimization; (3) Create a treatment response prediction framework based on spatial-temporal manifolds anchored on Riemannian geometry; (4) Apply and validate the proposed methods on cancer patient biobanks to compare predicted results with actual outcomes.***The long-term goal is to offer the necessary tools for generating representative knowledge from a given cohort of immunotherapy cancer patients and discovering predictive biomarkers of immune system response and tumor evolution from medical imaging.******This research program will propose novel outcome prediction tools using recent advances in manifold and deep learning. While previous predictive immunotherapy response methods were based on linear regression using hand-crafted features and lacked interpretable representations of underlying features capturing immunologic effects, we will develop a framework that will learn spatial and temporal properties of underlying manifolds. Compared to traditional machine learning techniques sensitive to the intrinsic topology of high-dimensional data, we will exploit novel concepts in joint embeddings, adding flexibility and versatility capabilities to a wide range of imaging and clinical data. From a medical outlook, this research has the potential to contribute new insights and indications for possible early detection of cancer and predictors of therapeutic outcomes.