DeCipher

Cancer is a significant cause of morbidity and mortality worldwide. In Norway alone, there are more than 33,000 new cancer patients each year, and 11,000 cancer-associated deaths in 2017. A large proportion of these incidents are preventable. For example, a mass-screening program against cervical cancer established in the Nordic countries has demonstrated a reduction in morbidity and mortality almost by 80 %. Despite this success, it remains a significant challenge to improve the screening program, such as minimize over screening and undertreatment and hence reduce expenditure in a broad public health perspective.

Current knowledge about the disease, together with a wealth of available data and modern technologies, can offer far better-personalized prevention, by deriving an individual-based time till the next screening. Existing automatic decision support systems for cervical cancer prevention are, however, extremely conservative as they are mostly limited to identifying patients who are overdue for their next routine screening, without providing any personalized recommendations for follow-ups.

By intelligent use of existing registries and health data, DeCipher aims to develop a data-driven framework to provide a personalized time-varying risk assessment for cancer initiation and identify subgroups of individuals and factors leading to similar disease progression. By unveiling structure hidden in the data, we will develop novel theoretically grounded machine learning methods for the analysis of large-scale registry and health data.

 DeCipher consists of an excellent multidisciplinary research team from diverse fields such as machine learning, data mining, screening programs, and epidemiology.  Available to screening programs, clinicians, and individuals in the population, the DeCipher results will allow for an improvement of an individual’s preventive cancer healthcare while reducing the cost of screening programs.

 

SimulaMet’s Role

SimulaMet will play a central role in the development of machine learning algorithms for longitudinal screening data analysis. Moreover, as the coordinator, SimulaMet is responsible for overall project management and dissemination activities.

Funding source

Research Council of Norway, IKTPLUSS

All partners

Cancer Registry Norway

Karolinska University Hospital, Sweden

Lawrence Livermore National Lab, USA

Coordinator

SimulaMet

Affiliation

Machine Learning

Duration

10.2019-09.2022