ATMOSPHERE (Adaptive, Trustworthy, Manageable, Orchestrated, Secure Privacy-assuring Hybrid, Ecosystem for REsilient Cloud Computing) is a 24-month project aiming at the design and development of an ecosystem of a framework, platform and application of next generation trustworthy cloud services on top of an intercontinental hybrid and federated resource pool. The framework considers a broad spectrum of properties and their measures. The platform supports the building, deployment, measuring and evolution of trustworthy cloud resources, data network and data services. The platform is demonstrated on a sensitive scenario to build a cloud-enabled secure and trustworthy application related to distributed telemedicine.

Breast cancer is now the most common cancer worldwide, surpassing lung cancer in 2020 for the first time. It is responsible for almost 30% of all cancers in women and current trends show its increasing incidence. Neoadjuvant chemotherapy (NAC) has shown promise in reducing mortality for advanced cases, but the therapy is associated with a high rate of over-treatment, as well as with significant side effects for the patients. For predicting NAC respondents and improving patient selection, artificial intelligence (AI) approaches based on radiomics have shown promising preclinical evidence, but existing studies have mostly focused on evaluating model accuracy, all-too-often in homogeneous populations. RadioVal is the first multi-centre, multi-continental and multi-faceted clinical validation of radiomics-driven estimation of NAC response in breast cancer. The project builds on the repositories, tools and results of five EU-funded projects from the AI for Health Imaging (AI4HI) Network, including a large multi-centre cancer imaging dataset on NAC treatment in breast cancer. To test applicability as well as transferability, the validation with take place in eight clinical centres from three high-income EU countries (Sweden, Austria, Spain), two emerging EU countries (Poland, Croatia), and three countries from South America (Argentina), North Africa (Egypt) and Eurasia (Turkey). RadioVal will develop a comprehensive and standardised methodological framework for multi-faceted radiomics evaluation based on the FUTURE-AI Guidelines, to assess Fairness, Universality, Traceability, Usability, Robustness and Explainability. Furthermore, the project will introduce new tools to enable transparent and continuous evaluation and monitoring of the radiomics tools over time. The RadioVal study will be implemented through a multi-stakeholder approach, taking into account clinical and healthcare needs, as well as socio-ethical and regulatory requirements from day one.

PRIMAGE proposes a cloud-based platform to support decision making in the clinical management of malignant solid tumours, offering predictive tools to assist diagnosis, prognosis, therapies choice and treatment follow up, based on the use of novel imaging biomarkers, in-silico tumour growth simulation, advanced visualisation of predictions with weighted confidence scores and machine-learning based translation of this knowledge into predictors for the most relevant, disease-specific, Clinical End Points. PRIMAGE implements a hybrid cloud model, comprising the of use of open public cloud (based on EOSC services) and private clouds, enabling use by the scientific community (facilitating reuse of de-identified clinical curated data in Open Science) and also suitable for future commercial exploitation. The proposed data infrastructures, imaging biomarkers and models for in-silico medicine research will be validated in the application context of two paediatric cancers, Neuroblastoma (NB, the most frequent solid cancer of early childhood) and the Diffuse Intrinsic Pontine Glioma (DIPG, the leading cause of brain tumour-related death in children). These two paediatric cancers are relevant validation cases given their representativeness of cancer disease, and their high societal impact, as they affect the most vulnerable and loved family members. The European Society for Paediatric Oncology, two Imaging Biobanks and three of the most prominent European Paediatric oncology units are partners in this project, making retrospective clinical data (imaging, clinical, molecular and genetics) registries accessible to PRIMAGE, for training of machine learning algorithms and testing of the in-silico tools´ performance. Solutions to streamline and secure the data pseudonymisation, extraction, structuring, quality control and storage processes, will be implemented and validated also for use on prospective data, contributing European shared data infrastructures.

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 FLUTE project will advance and scale up data-driven healthcare by developing novel methods for privacy-preserving cross-border utilization of data hubs. Advanced research will be performed to push the performance envelope of secure multi-party computation in Federated Learning, including the associated AI models and secure execution environments. The technical innovations will be integrated in a privacy-enforcing platform that will provide innovators with a provenly secure environment for federated healthcare AI solution development, testing and deployment, including the integration of real world health data from the data hubs and the generation and utilization of synthetic data. To maximize the impact, adoption and replicability of the results, the project will contribute to the global HL7 FHIR standard development, and create novel guidelines for GDPR-compliant cross-border Federated Learning in healthcare. To demonstrate the practical use and impact of the results, the project will integrate the FLUTE platform with health data hubs located in three different countries, use their data to develop a novel federated AI toolset for diagnosis of clinically significant prostate cancer and perform a multi-national clinical validation of its efficacy, which will help to improve predictions of aggressive prostate cancer while avoiding unnecessary biopsies, thus improving the welfare of patients and significantly reducing the associated costs. Team. The 11-strong consortium will include three clinical / data partners from three different countries, three technology SMEs, three technology research partners, a legal/ethics partner and a standards organization. Collaboration. In accordance with the priorities set by the European Commission, the project will target collaboration, cross-fertilization and synergies with related national and international European projects.