Deep learning and radiomic biomarkers for non-invasive immuno-oncology prognostication in pan-cancer patients.

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Cancer is one of the leading causes of death worldwide, and its impact is continuously growing, paralleling the world’s aging, and enlarging population. While Immuno-oncology (IO) is revolutionizing cancer care, only a subset of patients respond to immunotherapy, and established biomarkers are insufficient. Recent innovations in medical imaging and high-throughput computing allow the conversion of digital medical images into mineable high-dimensional data beyond visual perception, i.e., the concept of radiomics. In this project, we strive to leverage artificial intelligence (AI) computer vision technology for non-invasive imaging-based IO biomarker discovery to improve patient outcomes and population health. Our objective is to discover, validate and test novel imaging biomarkers to improve patient stratification and treatment decision-making so that immunotherapy treatment can be given to the patients who will most likely benefit from the treatment and adverse events can be reduced in patients who will most likely not benefit from immunotherapy. Further, we aim to complement existing biomarkers to improve their performance. It is of utmost importance that research innovations allow for clinical translation. Therefore, our project strongly focuses on validity, utility, and clinical usability. We will combine our models with biological data such as mutation profiles to unravel the potential mechanisms that underly the imaging biomarkers and we will use AI techniques such as activation mapping to increase the interpretability of our models. By doing so, we not only aim for the best possible validation but also to give the research community and clinical practitioners the possibility to gain trust in novel innovations in medical imaging, a crucial cornerstone for successful clinical translation and sustainable research. Our final step aims at sharing the data to promote open science by making the data and software code available to the scientific society.