Imaging biobanks are defined as organised databases of medical images, and associated imaging biomarkers (radiology and beyond), shared among multiple researchers, linked to other biorepositories .
Already existing biobanks are designed to give researchers access to large collections of patient/subject samples and data. Biobanks group biological material of healthy subjects (population-based) and/or patients with specific pathologies (disease-oriented), of which the most frequent are cancer-related. Most biobanks focus only on the collection of genotype data, but do not simultaneously come with a system to collect related clinical or phenotype data. In particular, most biobanks do not include or are not linked to any kind of imaging information, neither primary images nor imaging biomarkers. Comprehensive exploitation of biobanks that also include imaging (genotype and phenotype) is an important cornerstone in diagnostics in the era of “personalised medicine” [10–14].
Personalised medicine describes the intent to provide individual patients with state-of-the-art diagnostic tests, tailored interventions and specific treatments, whenever clinically indicated. Personalised medicine proposes the adjustment/customisation of health care from a one-size-fits-all approach to a patient-specific diagnosis and treatment . As such, personalised medicine can be described as an evidence-supported pre-selection and assignment of tests and therapy selections to patients in need. Quantitative medical imaging, potentially resulting in the discovery of imaging biomarkers, is an essential part of personalised medicine providing a priori selection criteria and a posteriori follow-up strategies, tailored to a given patient with a specific clinical need. Obviously, development of such strategies will be greatly enhanced by the availability of large data repositories [16–19].
Biobanks give researchers access to large repositories of biomaterials for a broad spectrum of further and future analysis, e.g., genetic, genomic, epigenetic, mRNA, proteomics and transcriptomics. The large scale and the broad spectrum of data allow the detection and validation of relevant biomarkers for personalised medicine. Moreover, biobanking in European networks will result in harmonisation of health, lifestyle and other exposure data as well as the development and implemention of harmonised definitions of diseases by increased consensus on the criteria for clinical endpoints.
The classical biobanking activities are to be mirrored by a similar network of imaging biobanks. Modern radiology and nuclear medicine can also provide multiple imaging biomarkers of the same patient, using quantitative data derived from all sources of digital imaging, such as CT, MRI, PET, SPECT, US, x-ray, etc. [20, 21]. Imaging biobanks are infrastructures with massive storage and computing capacity. High-performance computing resources are needed to facilitate image processing comparison, standardisation and validation. Integration of resources and services through a platform that manages the information flow and image processing is a step needed in the development of imaging biobanks.
Other types of images can also be collected from endoscopy, microscopy, surgery, etc., also providing measurable personalised data. All this imaging information should be considered as the phenotypic expression of a patient and can be linked to the genotype. Such data should be available to the research community [22, 23].
A European imaging biobanks network would significantly boost European research in the imaging domain by stimulating the design and validation of new imaging biomarkers, as well as improving our understanding of their biological significance.
This requires standardisation, validation and benchmarking of the data in imaging biobanks. This activity will further stimulate the linking and integration of existing (national and regional) image data repositories as well as the link between imaging biobanks and traditional biobanks.
Standards will have to be developed and implemented. Innovative solutions that promote fair access to high-quality data sets with regard to image-based phenotypes and imaging biomarkers will provide support to users for its utilisation.
Finally, the economic and ethical/legal issues for the management of imaging biobanks have to be explored. These will advance insights and yield benefits to enhance collaborative research, utilise limited resources effectively and share data, technology and expertise. Research on image data management and analysis plays a key role in improving the performance of protocols, software-based analysis and further methodologies for imaging biobanks and the development and validation of imaging biomarkers. All these aspects will surely foster high-level multicenter collaboration.