Supporting Personalized Treatment Decisions in Head and Neck Cancer through Big Data

SuPerTreat is a project financed by ERA PerMed initiative that aims to develop and validate in-silico, new powerful Big Data driven Artificial Intelligence (AI) methods to build an actionable, personalized treatment decisions support system (DSS) for Head and Neck Cancer (HNC); demonstrating how the use of Big Data and AI technologies contribute to increase understanding of HNC heterogeneity across different individuals, and its impact on disease outcome treated by curative standard of care approaches.

The project will focus on the validation of multifactorial methods combining existing clinically annotated omics datasets; and the investigation of ethical and legal aspects of data-driven clinical decision making vs current evidence-based approach.

The project combines clinical research with advanced mathematical modeling and bio-informatics to exploit available and combined results from high-quality multisource and multidimensional datasets built by converging efforts of partners of this consortium to accelerate their clinical use for personalized treatment decisions.

In a 3 years study, SuPerTreat will retrospectively analyze these multisource datasets using various types of classification, regression and statistical learning methods. SuPerTreat will assess the role of omics, in addition to a currently used staging system to assist outcome of HNC; produce and validate actionable prognostic and predictive models and algorithms to orient personalized treatment decisions, and finally integrate these into decisions support tools.

SuPerTreat consortium brings together an interdisciplinary team involving bioinformaticians, medical oncologists, surgeons, radiation oncologists, representatives of ethics committees and GDPR experts, from reference HNC hospitals (Istituto Nazionale dei Tumori di Milano, Italy; Charité Universitaetsmedizin Berlin, Germany; Institut Marie Curie Paris, France), from Universitiesdata experts, mathematicians (University of Oslo, Norway), psychologists of decision-making (Università degli Studi di Milano, Italy) and eHealth IT SMEs (Athens Technology Centre, Greece) and patients advocacy groups.

The main role of LST team is related to data integration of the different data sources involved in the project, focusing on tasks of data harmonization and annotation. LST team will be responsible to select and apply standards for interoperability to guarantee data exchange from clinical centers; and will also contribute to the definition and implementation of rules to verify, report and ensure the quality of data within the project.