Scientific aims

We aim to, at the end of the grant period, have introduced at least one eScience-based predictive algorithm within a national screening program.

  • We hypothesize that a concerted action using already available registries, biobanks and eScience tools could result in more effective screening programs. Stratification of the population by risk for disease will enable design of programs where both screening intervals and choice of screening test may be tailored based on individual disease risk. This will lead to improved cost-effectiveness in screening. Nordic concerted action where full advantage is taken of all available data and infrastructure for cancer screening programs, apart from the obvious computational advantages offered, also constitutes an ethical advantage since the research generated constitutes an excellent environment for exploring new biomarkers (e.g. genetic) in a rapid and reliable fashion rather than a fragmented, low-powered one.

We further aim to develop a substantial legacy of improved generic eScience infrastructure that could directly translate to and carry impact also on other common diseases. The methodological developments in algorithms, programmatic tools and increased data access produced by NIASC will be of a generic nature, resulting in that they could be further exploited to benefit research also on other medically important diseases.

  • Different cancer screening programs are at different stages of innovation development, with cervical cancer representing ongoing implementation of risk-stratified screening; prostate and breast cancer representing ongoing development of eScience algorithms for potentially optimized risk prediction; and colorectal cancer representing an area where different types of screening tests are being evaluated and may be selected based on risk. By networking of the interdisciplinary expertise, resources and already ongoing national projects in the Nordic countries, it is highly likely that NIASC could contribute to the actual implementation of an eSciencebased predictive algorithm in at least one screening program.