The @neurIST project: towards understanding cerebral aneurysms
As for almost every form of knowledge, the volume of data describing human disease processes, including our understanding, diagnosis, and management of them, is growing exponentially. The data is also increasingly heterogeneous in form as text, images, and other symbolic formats. The data is also extremely diverse in context. For instance, it ranges from global guidelines based on the broadest epidemiological studies, through knowledge gained from disease-specific scientific studies, both in vitro and in vivo, to individual patient-specific data. In addition, the data also scales from the molecular to the cellular sizes, and from tissue or organ to patient representations. The recent breakthroughs in the description of the human genome and in our understanding of its connection to disease processes have also contributed more data through functional genomics studies.
This phenomenal volume of fragmented information and its growth rate represent an unprecedented data-management challenge. In particular, it is often impossible for an individual—whether a clinician responsible for patient management, or a physicist or engineer developing new imaging or interventional devices—to understand and assimilate this knowledge. The result is that it has become increasingly evident that new methods are required to manage, integrate and search data so that it becomes accessible to the end user. The @neurIST project was designed to address this issue.1
Scalable and reusable concepts
The current fragmentation of relevant data effectively compromises disease treatment. This is why the @neurIST project first aims to achieve vertical integration across data structures and across length scales. It also seeks to take into account the horizontal integration that is present at every level of abstraction, from access to information sources, evidence processing, knowledge representation, structuring, and fusion. While the project is only focused on one carefully selected disease, cerebral aneurysm (see Figure 1), our overall goal is to create an integrative approach, scalable and reusable for other disease processes.
Although important due to its impact on public health, cerebral aneurysm also has a number of interesting challenges that make it attractive as a proof of concept for our data management approach. Additionally, we believe that such a level of focus is necessary to credibly address the expected vertical integration and to identify clear exploitation paths. The latter are operative in industrial contexts, for instance in decision-support systems and in the advanced design of medical devices, and also in medical contexts, where they support further research and discovery, such as linking the molecular level understanding of a disease with the disease process itself.
Management of cerebral aneurysm information
The current understanding of cerebrovascular aneurysm disease combined with modern imaging technology can increasingly reveal silent lesions of the type found during clinical exams. With a prevalence of 2-3% and a rupture risk of the order of 1/10,000 people annually, treatment of this disease would benefit significantly from an integrated approach and the development of a personalized risk-assessment strategy. The integration of multiple clinical data and the use of grid-based information technology (IT) systems may provide the required platform. This will reduce health care costs by optimally targeting the relevant patient population, thus avoiding unnecessary and potentially risky interventions while improving methods for minimally invasive treatment.
Currently, the @neurIST project involves work at several different levels. The overall goal is to develop a novel IT-enabled system for cerebral aneurysm management.2 This requires identifying and collecting all publicly-available, relevant, and strategically important data from scientific studies.3–5 It also entails delivering a rich, multiscale information-processing chain to provide new diagnostic indexes and insights into the process of aneurysm formation and rupture.6–13 Another work area is the development of a set of scalable and reusable integrative suites and the demonstration of their value for understanding and managing the disease.1 Finally, ongoing efforts are directed at providing an information and communication technology system for developing, integrating and sharing relevant biomedical knowledge as required by the integrative suites.
The @neurIST infrastructure will not only support computationally demanding tasks, such as complex modelling and simulation, but will also enable access to public and protected health databases all over the world.14 This capacity will no doubt promote the development of corresponding systems for other disease processes by demonstrating the personal and economic impact of IT-enabled information integration in the context of cerebral aneurysm management.
@neurIST and the virtual physiological human
Although this integrated project is primarily concerned with the vertical integration of biomedical data, many of the developed concepts have obvious implications and applications within the framework of the STEP (A Strategy Towards the Europhysiome) roadmap initiative,15 funded as a concerted action with @neurIST. In particular, it provides a platform for demonstrating the benefits and viability of the ‘in silico’ model of a human being, by merging a top-down approach starting from models of body parts and organs with a bottom-up approach that models molecular interactions, pathways and cells. We believe that @neurIST is a pragmatic and concrete example of the virtual physiological human16 concept as well as a transitional project of tremendous significance for the biomedical community.