Semantic web: key technologies for bridging imaging and translational medicine

Abstract

Imaging in translational medicine Translational medicine has emerged as a new concept in medicine resulting from closer interactions between the research realm and clinical practice, as suggested by the expression ‘from bench to bedside’. The essence of translational medicine is that it not only leads to more efficient health care delivery (e.g., new drugs and procedures), but also to a more in-depth understanding of underlying biological mechanisms [1,2]. The term ‘research realm’ here means not only preclinical and clinical research (e.g., clinical trials) but something broader, embracing both basic research and translational research aiming to put biological facts obtained in experimental works (in animals) in perspective with other observations made in man concerning different pathologies, and involving common physiological and biological systems. This concept has gained credibility thanks to recent initiatives such as the Cancer Biomedical Informatics Grid (CaBIG), an initiative led by the National Cancer Institute in the USA that aims to transform the way cancer research is performed by better connecting the entire cancer community [101]. In particular, this initiative emphasizes the major contribution that biomedical informatics infrastructures can make towards this goal. Imaging plays a major role in modern medicine owing to the continuous progress of imaging equipment during the last 30 years, and this role will certainly be even more prominent in future translational medicine. A salient characteristic of modern imaging is that it is becoming more and more quantitative [3]. This means it no longer solely aims to deliver images to be interpreted by a radiologist, but also to deliver ‘measurements’ of well-defined parameters. Such parameters may be derived from acquired images and reflect some physical characteristic of tissues (e.g., the T 1 relaxation time in MRI, or the concentration of a tracer in molecular imaging). It may also result from a complex analysis of images using texture analysis or segmentation, and denote some structural, physiological or functional index. The latter are often called ‘imaging biomarkers’, an analogy with other kinds of biological biomarkers [4,5]. This notion of measurement is important and refers to a complete methodology involving quality assurance based on calibration, to guarantee reproducibility in time and space, which puts heavy constraints on image acquisition and image processing protocols [6]. It also requires a clear, explicit and consensual definition of the quantities being measured; this is a key feature with respect to relevant reuse of information in the context of translational medicine. Indeed, it is not sufficient that such numeric values be communicated to research data repositories (thanks to picture archiving and communication systems), one must also ensure that the precise semantics of this data be shared as well. This is a necessary condition for successful sharing and relevant correlation of this information with other data available from other sources.