Below is a summary of the achievements from the first 18 months of the project.
Europe has been at the forefront of AI developments in cancer imaging as illustrated by the introduction of the radiomics concept in 2012 by members of the EuCanImage consortium. However, clinical translation of existing AI-based cancer imaging solutions is still lacking, as all-too-often, they have been built and validated in small site-specific cancer imaging datasets. 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 in order 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.
Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far (For the final period please include an overview of the results and their exploitation and dissemination)
During the first reporting period, the EuCanImage project has first focused on the delivering of a comprehensive legal and ethical framework by producing data transfer agreements supporting data exchanges for research activities and assessing the measures for privacy and data protection that are currently being implemented (WP1/9). Moreover, multidisciplinary consensus discussions on platform functionalities related to clinically unmet needs and clinical requirements were conducted, while multiple activities were carried out to refine the final versions of the use-cases and to specify detailed clinical questions and specific tasks for AI algorithms. After identification of datasets by the clinical partners, the data annotation and upload to the EuCanImage platform was initiated. A comprehensive data management plan (DMP) was further developed (2nd version) to define detailed data governance for the project and to provide clear procedure for FAIR data management and GDPR compliance (WP2). The EuCanImage data repository is now ready and most clinical partners are depositing data into the data annotation platform (WP2). To facilitate data transfer and interoperability in the project, the first version of the software for integration and linking between the different systems (clinical nodes, annotation platform, EuCanImage central repository) has been developed. A portal prototype for data access has been created, allowing the registration and management of data access committees, as well as the management of access conditions (WP3). To compile the EuCanImage data with detailed input from clinical experts, a centralised, cloud-based collaboration solution for annotation, segmentation and classification of cancer imaging data has been developed, which supports all EuCanImage use cases in breast, liver and colorectal cancer. In addition, a first version of a “user-friendly toolbox for generating large samples of synthetic cancer images” has been created (WP4). The pilot version of the Virtual Research Environment (VRE) platform has been deployed and progress in in the development of radiomics and machine learning tools has been achieved. Moreover, the first version of the FUTURE-AI guidelines with 30 recommendations has been released (WP5) and a consensus document (Radiomics Quality Score 2.0) has been drafted updating metrics for assessment and benchmarking of radiomics AI solutions (WP6). A visual identity for the EuCanImage project has been created, including the project logo, templates for presentations and the project video. Moreover, a project website and a dedicated Twitter account have been set up (WP7). Through close scientific interactions and project monitoring, the timely completion of the expected activities in each work package was ensured (WP8).
Progress beyond the state of the art, expected results until the end of the project and potential impacts (including the socio-economic impact and the wider societal implications of the project so far)
EuCanImage will constitute the first repository of cancer imaging data, integrating information on imaging sequences, available annotations and imaging biomarkers, and directly cross-linked to biological and health repositories. A legal governance framework will be established to promote data sharing for the benefit of science and society, while ensuring complicance with regulation on data privacy and protecting the rights of the citizens. Various tools will be delivered to enable data anonymisation, data curation, clinical annotations and secure storage in the most secure European infrastructures, namely Euro-Bioimaging and European Genome-phenome Archive. An AI environment will be developed with integrated capabilities in machine learning, federated learning, radiomics and explainable AI, to build the next generation of multi-modal AI solutions in cancer. The EuCanImage platform as a whole is expected to impact oncology research by creating an EU-wide repository of health images dedicated to the most common forms of cancer, leading to an improvement in diagnosis, treatment and follow-up and contributing to a more precise and personalised management of cancer via AI-based solutions. Datasets of EuCanImage have been carefully selected to constitute a unique resource currently unavailable in existing repositories, thereby allowing AI experts and clinical stakeholders to investigate unmet clinical needs on different types of cancers, especially for breast, liver and colorectal cancer. AI-based diagnostic and treatment planning tools will be validated on conditions that have huge personal, social and economic costs, resulting in improved outcomes for citizens and the society as a whole. EuCanImage will also develop standards and best practices for designing, developing and validating future AI solutions which will be technicall robust, clinical safe and free of bias, hence they will be trusted, deployed and exploited in real-world cancer care. Additionally, these innovations will benefit the field of imaging AI in oncology, but also medical imaging at large, bringing the quality of the implemented image-data catalogues to the next level and highest standard for future medical imaging in Europe.