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Over the last decades the introduction of digital radiology and digital pathology as well as the 'age of -omics' (genomics, proteomics, microbiomic, etc) gave rise to massive data repositories in healthcare. In radiology in particular, patients are receiving more and more scans who are all digitally stored for multiple years.

Moreover, the complexity of data has grown substantially due to the increase in resolution of e.g. CT and MRI and the introduction of hybrid imaging (e.g. the combination of PET and MRI or PET and CT), 3D and even 4D data (e.g. spatial and temporal registration with ultrasound or MRI). Altogether, this has resulted in a typical amount of radiological data in the order of tens of terabytes for an average sized hospital.

This bulk of data is called 'big data' which is meant to represent both its sheer size and its huge value. Based on this data more accurate diagnosis and better treatment planning, treatment assessment and treatment personalisation can be achieved by means of clinical decision support systems. From an R&D perspective the data contains unprecedented opportunities for comparative effectiveness and cost-effectiveness studies, predictive modelling, statistical tools for trial design and biomarker and drug discovery. Altogether this data can hugely contribute to both the quality and cost reduction of healthcare.

Additional difficulty in healthcare is the diversity of data silos that have been piling up over the years. In current clinical practice it is common that each department (e.g. radiology, histology, nuclear medicine, radiotherapy, etc) uses different silos from different vendors with different data formats. The ability to integrate and simultaneously query these heterogenous types of data would yield even more opportunities to leverage the value of the data.

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Semantic DICOM