Periodic Reporting for period 2 - EuCanImage (A European Cancer Image Platform Linked to Biological and Health Data for Next-Generation Artificial Intelligence and Precision Medicine in Oncology)
Reporting period: 2022-04-01 to 2023-09-30
To promote trust, clinical value and translation of emerging AI technologies for cancer imaging, not only larger and more diverse data sets are needed, but also new standards and improved practices to exploit such large datasets for addressing clinical needs that are currently unmet in oncology. Nevertheless, as large cancer imaging repositories are still missing in Europe, access to multi-centre cancer imaging dataset for the AI communities and industries remains a challenge. Thus, EuCanImage responds to an imperative need for such high-quality and large repositories in Europe to leverage the available expertise in AI, radiomics and cancer imaging for precision medicine in oncology.
Cancer imaging is a central piece to realise the promise of precision medicine in oncology. Nonetheless, non-image data are also relevant, particularly -omics and health data, for enabling to build integrated multi-scale AI models that consider patient-specific biomolecular, phenotypic, environmental and clinical information. It is expected that integrated AI models will lead to improved diagnosis and treatment selection for many cancer types. Hence, it is important to develop cancer imaging repositories that are cross-linked to corresponding -omics and health datasets.
The main objective of EuCanImage is to build and demonstrate a General Data Protection Regulation (GDPR)-compliant and scalable AI platform for leveraging large-scale, high-quality and interoperable cancer imaging datasets adequately linked to biological and health oncology data. This user-friendly platform will provide functionalities that enable easy access to information on available data and facilitate future data depositions. This platform will also integrate advanced capabilities and new standards to develop and validate AI-powered clinical decision support systems for precision oncology with increased clinical trust and adoption, while developing the legal framework that will enable responsible data sharing and enhanced Open Science.