CHAIMELEON aims to set up a structured repository for health imaging data to be openly reused in AI experimentation for cancer management. An EU-wide repository will be built as a distributed infrastructure in full compliance with legal and ethics regulations in the involved countries. It will build on partner´s experience (e.g. PRIMAGE repository for paediatric cancer and the Euro-BioImaging node for Valencia population, by HULAFE; the Radiomics Imaging Archive by Maastricht University; the national repository DRIM AI France, the Oncology imaging biobank by Pisa University). Clinical partners and external collaborators will populate the Repository with multimodality (MR, CT, PET/CT) imaging and related clinical data for historic and newly diagnosed lung, prostate, colon and rectal cancer patients. A multimodal analytical data engine will facilitate to interpret, extract and exploit the right information stored at the Repository. An ambitious development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and images harmonisation, the latest being of the highest importance for enabling reproducibility of Radiomics when using large multiscanner/multicentre image datasets. The usability and performance of the Repository as a tool fostering AI experimentation will be validated, including a validation subphase by other world-class European AI developers, articulated via the organisation of Open Challenges to the AI Community. A set of selected AI tools will undergo early on-silico validation in observational (non-interventional) clinical studies coordinated by leading experts in Gustave Roussy (lung cancer), San Donato (breast), Sapienza (colon and rectal) and La Fe (prostate) hospitals. Their performance will be assessed, including external independent validation, on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer.

The NICI project’s ambition is to lay the foundations of a new area of research: the study of human biology using non-invasive chemistry imaging. For this, NICI aims to unite two areas of research: metabolomics and magnetic resonance imaging (MRI). Metabolomics studies body functions through the measurement of metabolites; MRI, is able to provide 3D images of the body. By advancing MRI so that it can detect metabolic biomarkers and by discovering powerful new MRI-visible biomarkers, a non-invasive technology can be developed for dynamically mapping biochemical processes in the whole human body. Vision: This new non-invasive technology for imaging biochemical processes in the human body will open a new and effective window for understanding human biology, diseases and their treatment. Breakthroughs: I. Methodology for the discovery of discriminative biomarkers and II. Technological platform for full body biochemical imaging. Novelty: Enabling a paradigm shift from morphologic imaging to biochemical understanding. Foundational: Establishing the basis for a new research area, the study of human biochemistry using non-invasive biochemical imaging. High-risk: i) The exact mechanisms of diseases are largely unknown and ii) Measuring specific metabolites is challenging. Interdisciplinary: Bringing together physicists, biologists, chemists and clinicians. The NICI project will develop a new methodology for the in vitro discovery of discriminant biomarkers using co-cultured 3D organoids as models for human organs. In addition, the project will develop a measurement platform, integrated with 7T MRI scanners and associated data acquisition approaches to adapt these MRI scanners into 3D biochemical imaging systems. NICI will validate the dynamic 3D chemical imaging approach and its predictive and prognostic value by researching a stratification strategy for patients with liver metastasis of gastrointestinal cancer. (This is one out of many possible applications.)

Alzheimer’s disease (AD) and related disorders leading to dementia are associated with staggering costs and suffering. Recently, there has been some progress in the search for effective therapeutic interventions and it is clear that any treatment is likely to be most effective if administered at the earliest stage of disease, but the health care system is not ready for this new scenario. There is an urgent need, therefore, to establish scalable, cost-efficient diagnostic markers, tools and procedures that can identify people at increased risk, at point of care for stratification into personalized interventions to prevent or delay dementia. PREDICTOM will develop an open-source, interoperable and customisable biomarker screening platform, utilizing an existing online resource to save time and money, to generate an evidence base for general population screening for AD and related disorders. We will bring diagnostics closer to the patient by examining the feasibility of using samples which can be obtained at home (e.g. finger-prick blood, saliva (for genetics and epigenetics) and stool for microbiom) for diagnostic biomarker analysis. We will also evaluate innovative technologies for disease risk identification, including digital technologies and novel MRI, EEG, eye tracking, and blood-based biomarkers. The platform will use artificial intelligence models to analyse data from all biomarkers to identify users at high risk of developing dementia and to direct them to personalized intervention to prevent further cognitive decline and development of dementia. We will seek to facilitate a change in current healthcare practice for early diagnosis of AD through development of new clinical practice guidelines based on evidence generated in the project. By improving the ease of identification of those with early signs of dementia we expect to have a significant impact on personal and financial burden of dementia in Europe and across the world.