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Lung cancer, COPD and CVD (so-called Big-3) are highly prevalent in the general population and are expected to cause most deaths by 2050 in China and the Western world. For this so-called Big-3, early treatment has been shown to delay or stop progression and allow therapy at a treatable stage in many patients. Therefore, prevention and early treatment are of growing importance. While there is certainly evidence for the role of CT in lung cancer screening, the additional benefit for low-dose CT of the thorax needs careful investigation. With a new innovative ultra-low-dose CT technique, early imaging biomarkers of lung cancer, COPD and cardiovascular disease can be assessed by one imaging modality. This new CT scan technique, combined with an appropriate set of clinical / laboratory parameters and medical decision support systems, can open up new avenues for effective prevention and/or early treatment protocols. This can provide an invaluable resource for the development and validation of biomarker profiles and computer aided decision support in the context of personalized, precision and stratified medicine.
The aim of the current project is to improve the early detection of lung cancer, COPD and cardiovascular disease in asymptomatic (population-based) participants in China and the Netherlands with a new, ultra-low-dose CT technique, as well as normal imaging biomarker values for lung density, bronchial wall thickness and vascular calcifications by age and gender, with the final aim to integrate these biomarkers into personalized health strategies in the general population of the Chinese urban and Western population. The screening results will be implemented into a web-based system including designing and implementing a secure cloud solution to register screening participants and report the screen results to the screenees; and to develop Chinese e-learning material for lung cancer screening. The final aim is to provide a one-stop-shop screening solution for lung cancer, cardiovascular disease and COPD by ultra-low-dose CT (Netherlands-China Big-3 screening: NELCIN-B3).
The NELCIN-B3 research imaging infrastructure ENACT which connects virtual workspaces with large data centers and high performance computing facilities is embedded in the Center for Information Technology (CIT) of the University of Groningen. The CIT provides high quality IT services, primarily to the University of Groningen and secondarily to educational institutions in the north of the country. To achieve our objective, the CIT has many facilities at its disposal. The department of Research and Innovation Support (RIS) CIT provides scientists and other interested parties with state-of-the-art facilities in High-Performance Computing (HPC). These include the Peregrine Cluster (>170nodes, >4000cores, ~500TB storage, 56Gb/s Infiniband network, 10Gb/s Ethernet), the Grid Cluster (58 compute nodes, ~1000 cores, 10Gb/s Ethernet, Access to the Target storage of 10 Petabytes) and the Millipede Cluster (>250 nodes, 3280 cores, 350TB storage). The heart of the visualization facilities is the Reality Center, which consists of the 4-sided Reality Cube and the wide-screen Reality Theatre. Both the Cube and the Theatre are driven by the high-end visualization cluster. There is a portable 3D system available for on-site presentations. The facilities and expertise of the CIT are available and used both by DSSC and CMINEN in a variety of projects.
The center for Data Science and Systems Complexity (DSSC) consists of a cluster of more than 50 prominent senior researchers at the University of Groningen in a number of basic disciplines (mathematics, astronomy, computer science, artificial intelligence, systems & control) and scientific application domains (genomics, pharmacology, instrumentation). The ambition of DSSC is to combine data science and complexity science. Simulations or measurements of complex systems, like climate models or coupled cell systems, give rise to (big) data in a mixed deterministic and random style. The DSSC is co-chaired by Prof. Roerdink (link).
The DSSC focuses on a new way of scientific research, resulting from the impact of information technology. Data are produced in huge amounts, either by instruments or sensors, by simulations, or captured from the internet. Then the data are processed in software pipelines, stored in databases, and permanently archived in data centers. Only then will scientists ‘look’ at their data via complex data analysis and visualisation pipelines to search for specific information, or do large-scale exploration of the data. In short, the database has become the laboratory / observatory.
Scientific data, the software to analyze the data, the results of the analysis (tables, graphs, images), and the corresponding publications become part of one persistent digital archive. The application of Data Science in scientific, economic, health, and social contexts has become known under the name ‘Big data’. Producing, handling, and analyzing big data also increasingly involves large scientific communities of experts in their respective fields. So, by nature, data science is collaborative science, which is also reflected in increasing attention for data sharing, innovative ways of data acquisition (for example, through crowd sourcing), integration of cyber-infrastructure into human workflows and practices, and new ways of publication (including data) that depart from the traditional journal and conference format.
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