In Europe, prostate cancer (PCa) is the second most frequent type of cancer in men and the third most lethal. Current clinical practices, often leading to overdiagnosis and overtreatment of indolent tumors, suffer from lack of precision calling for advanced AI models to go beyond SoA by deciphering non-intuitive, high-level medical image patterns and increase performance in discriminating indolent from aggressive disease, early predicting recurrence and detecting metastases or predicting effectiveness of therapies. To date efforts are fragmented, based on single–institution, size-limited and vendor-specific datasets while available PCa public datasets (e.g. US TCIA) are only few hundred cases making model generalizability impossible. The ProCAncer-I project brings together 20 partners, including PCa centers of reference, world leaders in AI and innovative SMEs, with recognized expertise in their respective domains, with the objective to design, develop and sustain a cloud based, secure European Image Infrastructure with tools and services for data handling. The platform hosts the largest collection of PCa multi-parametric (mp)MRI, anonymized image data worldwide (>17,000 cases), based on data donorship, in line with EU legislation (GDPR). Robust AI models are developed, based on novel ensemble learning methodologies, leading to vendor-specific and -neutral AI models for addressing 8 PCa clinical scenarios. To accelerate clinical translation of PCa AI models, we focus on improving the trust of the solutions with respect to fairness, safety, explainability and reproducibility. Metrics to monitor model performance and a causal explainability functionality are developed to further increase clinical trust and inform on possible failures and errors. A roadmap for AI models certification is defined, interacting with regulatory authorities, thus contributing to a European regulatory roadmap for validating the effectiveness of AI-based models for clinical decision making.

DIOPTRA aims to introduce a front-line screening tool that will consider risk factors and protein biomarkers for pinpointing individuals at a high risk for colorectal cancer (CRC) incidence. Tissue & blood samples will be examined towards a discriminative set of prognostic proteins that are detectable via standard bloodwork and can indicate a need for further evaluation (i.e. colonoscopy). Other data (e.g. medical, behavioural) will also be considered as potential risk factors. Artificial intelligence (AI) will be leveraged for assessing prognostic power, while personalised behavioural change will be promoted based on modifiable risk factors. Given the low citizen participation on CRC screening across EU, DIOPTRA seeks to broaden the evaluated population, boosting participation rates and bypassing age screening thresholds. This action is part of the Cancer Mission cluster of projects on ‘Prevention, including Screening’.

Europe still sees a quarter of the world's cancer cases each year, making cancer the second leading cause of death and illness in the region after cardiovascular diseases. Unless we take decisive action, lives lost to cancer in the EU are set to increase by more than 24% by 2035, making it the leading cause of death in the EU. Cross-border collaboration can address this challenge by combining data from various modalities and sources, extracting meaningful insights to deepen our understanding of cancer. However, ethical, legal, and national regulations, along with data access processes, including differing interpretations of the EU GDPR create significant hurdles. Technical interoperability issues across European cancer RIs, and patients' and citizens' rights to control who uses their personal information and for what purposes further complicate data sharing. The project will provide European researchers, SMEs, and innovators with a decentralized collaborative network, “UNCAN-CONNECT,” for cancer research. It consists of both technical components, a governance, compliance, and operational framework based on the UNCAN blueprint, with the goal of operationalizing it. The objective is to facilitate access to cancer data, promote open science, and revolutionize cancer research and treatment by co-creating an open-source federation of federations platform. It will be developed using specific use cases focused on six major cancer types: Paediatric, Lymphoid malignancies, Pancreatic cancer, Ovarian, Lung, and Prostate cancers and active collaboration with a diverse range of stakeholders, including researchers, SMEs, industrial end users, and citizens. It will build on existing European RIs such as BBMRI as well as initiatives like EOSC4CANCER, CanSERV, EUCAIM, to enable seamless storage, access, sharing, and processing of data across Member States and associated countries. This approach will foster interoperability and collaboration, accelerating progress in cancer research. This action is part of the Cancer Mission clusters of projects 'Understanding' established in 2022.