ONCOVALUE will unlock the full potential of real world data (RWD) collected in European cancer hospitals and institutes to ease the decision-making of regulators on cost-effectiveness of novel cancer therapies. To achieve this, we build up data collection and processing capabilities of leading European cancer hospitals to create a high-quality clinical, quality of life, and adverse events data-sources. With the use powerful AI technologies, we will transform unstructured data originating from medical notes and medical images into structured data to enable analytics and real world evidence (RWE). This RWE will be directly available for clinicians for treatment management and for health regulatory and HTA bodies to adopt optimized data-driven methodologies for the effective assessment of medicinal products and digital health innovations. For that, we will provide an end-to-end infrastructure for RWD reporting in health regulatory and HTA decision-making and address the legal constraints in the cancer hospitals to ensure secure and legal access to RWD. Furthermore, ONCOVALUE will ensure the implementation of the developed guidelines and methodologies, by providing trainings for the collection and management of high-quality RWD in European cancer centres and for the analysis of this data by HTA and regulatory bodies. By opening the door to widespread regulatory and HTA integration of RWD, ONCOVALUE will lead to safer, more efficient, and affordable therapies, technologies, and digital solutions for (personalised) cancer care. As such, ONCOVALUE is positioned to contribute to increased cost-effectiveness and subsequent sustainability of cancer care. Systematic collection and evaluation of the patient reported outcomes will lead to improved well-being of the patients. Subsequently, on the long-term implementation of value-based cancer care at European cancer hospitals will aid in reducing the growing burden of cancer treatment in the EU and worldwide.

The central PANCAIM concept is to successfully exploit available genomic and clinical data to improve personalized medicine of pancreatic cancer. PANCAIM’s concept is unique as it integrates the whole spectrum of genomics with radiomics and pathomics, the three future pillars of personalized medicine. The integration of these three modalities is very challenging in the clinic, but also with AI. PANCAIM uses an explainable, data-efficient, two-staged AI approach. AI biomarkers transform the unimodal data domains into interpretable likelihoods of intermediate disease features. A second AI layer merges the biomarkers and responds with an integrated assessment of prognosis, prediction and monitoring of therapy response, to assist in clinical decision making. PANCAIM builds on four key concepts of AI in Healthcare: data providers, clinical expertise, AI developers, and MedTech companies to connect to data and bring AI to healthcare. Data quantity and quality is the main factor for successful AI. Partners provide eleven Pan European repositories of almost 6000 patients that are open to ongoing accrual. SME Collective Minds builds the GDPR data platform that hosts the data and provides a trustable connection to healthcare for even more and sustainable data. SME TheHyve builds tooling to connect to more genomic repositories (EOSC Health). Six Pan European academic centers provide clinical expertise across all modalities and help realize a curated, high quality annotated data set. Partners also include expert AI healthcare researchers across all clinical modalities with a proven track record. Finally, Siemens Healthineers provides their AI expertise and tooling to bring AI into healthcare for clinical validation and swift clinical integration in 3000 health care institutes.

Atrial fibrillation (AF) is the most common heart arrhythmia worldwide, leading to life-limiting complications, high financial burden and significant resource utilisation. In Europe, stroke as a debilitating complication of AF, is amongst the commonest causes of death and the leading cause of disability. AF patients have a 5-fold increased risk for ischaemic stroke. Functional recovery from AF-related stroke (AFRS) is often unsatisfactory, leading to severe disability, reduced quality of life and high mortality. TARGET’s ambition is to develop novel personalised, integrated, multi-scale computational models (virtual twins) and decision-support tools for the AF-related stroke pathway, starting from the healthy state, pathophysiology and disease onset, progression, treatment and recovery. TARGET aims to help prevent AF and AFRS, optimise acute management and rehabilitation, reduce long-term disability, provide a better quality of life for patients and caregivers, and lower healthcare costs. We will ensure patients are at the heart of the project, and the association with experienced commercial partners will ensure the swift adoption of TARGET’s novel technologies. New observational data will be collected via 4 carefully designed prospective clinical studies, which will be used to test and validate the personalised tools and the virtual twin models using a clinical trial simulation (virtual/in-silico), to demonstrate evidence of clinically meaningful results. TARGET will also help consolidate existing mechanistic virtual twin models of the heart, the brain and the neuromusculoskeletal system, enriching these twins to deliver more complex tasks, and supporting research to move towards a more integrated human virtual twin. TARGET represents a milestone project to improve the care and rehabilitation of patients with AF and AFRS, introducing a paradigm shift in risk prediction, diagnosis and management of the disease, and accelerating translational research into practice.

Atrial fibrillation (AF) and stroke are major health care problems in Europe. They are most often the clinical expression of atrial cardiomyopathy, which is under-recognised due to the lack of specific diagnostic tools. Multidisciplinary research and stratified approaches are urgently needed to prevent, diagnose, and treat AF and stroke and preempt the AF-related threat to healthy ageing in Europe. MAESTRIA is a European consortium of 18 clinicians, scientists and Pharma industrials who are at the forefront of research and medical care of AF and stroke patients. It will create multi-parametric digital tools based on a new generation of biomarkers that integrate artificial intelligence (AI) processing and big data from cutting edge imaging, electrocardiography and omics technologies. It will develop novel biomarkers, diagnostic tools and personalized therapies for atrial cardiomyopathy. Digital Twin technologies, a rich data integrator combining biophysics and AI will be used to generate virtual twins of the human atria using patient-specific data. Unique experimental large-animal models, ongoing patient cohorts and a prospective MAESTRIA cohort of patients will provide rigorous validation for new biomarkers and newly developed tools. A dedicated core lab will collect and homogenize clinical data. MAESTRIA will be organized as a user-centered platform, easily accessible via clinical parameters routinely used in European hospitals. A Scientific Advisory Board comprising potential clinician users will help MAESTRIA meet clinical and market needs. Dissemination and visibility of the MAESTRIA consortium mission will benefit from participation of the German Competence Network on Atrial Fibrillation (AFNET), and support from the European Society of Cardiology, clinicians, scientists, and other professional societies. MAESTRIA will be ready to tackle the major challenges of data integration and personalized medicine focused on atrial cardiomyopathy, AF and stroke.

The goal of EuCanImage is to build a highly secure, federated and large-scale European cancer imaging platform, with capabilities that will greatly enhance the potential of artificial intelligence (AI) in oncology. Firstly, the EuCanImage platform will be populated with a completely new data resource totaling over 25,000 single subjects, which will allow to investigate unmet clinical needs like never before, such as for the detection of small liver lesions and metastases of colorectal cancer, or for estimating molecular subtypes of breast tumours and pathological complete response. Secondly, the cancer imaging platform, built by leveraging the well-established Euro-Bioimaging infrastructure, will be cross-linked to biological and health repositories through the European Genome-phenome Archive, allowing to develop multi-scale AI solutions that integrate organ-level, molecular and other clinical predictors into dense patient-specific cancer fingerprints. To deliver this platform, the consortium will build upon several key European initiatives in data sharing for personalised medicine research, including EUCANCAn (cancer genomics and health data sharing), euCanSHare (cardiac imaging and omics data sharing) and EUCAN-Connect (federated data analytics). Furthermore, to foster international cooperation and leverage existing success stories, the consortium comprises the coordinators of The Cancer Imaging Archive (TCIA), the US cancer imaging repository funded by the National Cancer Institute. This will allow EuCanImage to leverage a unique 10-year long experience in cancer imaging storage, anonymisation, curation and management. Finally, a close collaboration between world renown clinical, radiomics, AI and legal experts within the consortium and beyond will establish well-needed guidelines for AI development and validation named FUTURE, for delivering Fair, Universal, Traceable, Usable, Robust and Explainable decision support systems for future cancer care.