Data-driven Framework for Personalised Cancer Screening

Cancer is a major 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 by almost 80%. Despite this success, it remains a major challenge to improve the screening program, such as by minimising 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 personalised prevention, by deriving screening frequency recommendations at the individual level. 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 personalised recommendations for follow-ups. By intelligent use of existing registries and health data, DeCipher has developed a data-driven framework to provide a personalised time-varying risk assessment for cancer initiation and identify subgroups of individuals leading to similar disease progression. Identifying these subgroups is expected to reduce the amount of over- and undertreatment, providing a personalised screening schedule to each female and reducing costs of screening programs. DeCipher consists of an excellent multidisciplinary research team from diverse fields such as machine learning, data mining, screening programs, and epidemiology. The Decipher results will be made available to screening programs, clinicians, and individuals in the population.

DeCipher is a multidisciplinary venture with a clear vision: to develop novel interpretable data-driven tools for personalised cancer screening. The goal is to empower women to make better-informed health-related decisions in combination with healthcare professionals. By shifting from population-based statistical follow-ups to individually tailored screening, the project results will contribute to sustainable healthcare system that benefits both the individual patients and society at large. The theoretically grounded developed methods using temporal, sparse, and multimodal data for predictive analysis has lead to scientific impact within machine learning, computer science, and neighbouring fields. The methods are easily transferable and extendable to other data and applications. The project helped to consolidate the leadership of individual partners and catalyse a milieu for multidisciplinary collaboration between AI and healthcare domains.

Cancer is a major cause of morbidity and mortality worldwide, but a large proportion of the incidents are preventable. For example, mass-screening Nordic programs of cervical cancer have a proven strong effect for preventing cancer at the population level and have produced large amounts of individual and clinical data, centrally organised at nationwide registries. Despite this success, minimising over- and under-treatment, and, thus, reducing expenditure, remains a major challenge. 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. Current knowledge about the cancer, together with a wealth of available data and modern technologies, can offer far better personalised prevention. DeCipher aims to develop a data-driven framework to provide a personalised time-varying risk assessment for cancer initiation and identify subgroups of individuals and biomarkers leading to similar disease progression. By unveiling structure hidden in the data via randomisation and probabilistic tools, we will develop novel theoretically grounded machine learning methods for analysis of temporal, sparse, and multimodal data. DeCipher consists of an excellent multidisciplinary research team from diverse fields such as machine learning, data mining, screening programs, and epidemiology. Leveraging Nordic screening programs and data, the project will enable better and more accurate cancer screening. Our foundational and algorithmic progress will also enable integration of data-driven techniques into biomedical domain, thus corresponding to the Medium-term Time Horizon objectives. Available to screening programs, clinicians, and individuals in the population, the DeCipher results will allow for improvement of individual’s preventive cancer healthcare while reducing the cost of screening programs.