Risk-stratified strategies in population screening for colorectal cancer

Colorectal cancer (CRC) screening has been demonstrated to reduce CRC incidence and mortality. However, besides such benefits, CRC screening is also associated with potential harmful effects. In an ideal world, screening would only be directed to the small proportion of the population that might potentially benefit. Risk-based screening can be seen as a first step towards this ideal world, by redistributing screening resources from low-risk to high-risk individuals. In theory, this should result in scarce resources being used in individuals who benefit most, while intensity of screening is reduced in individuals who benefit less, hence improving the benefit-harm ratio among all invitees. Available strategies that have been proposed for risk-based CRC screening include using information on age, sex, prior screening history, lifestyle and/or genetic information. Implementation of risk-based screening requires careful consideration of reliable risk prediction models, participation with screening and informed decision-making. While it is important to recognise the limitations of current approaches, available evidence suggests that it might be feasible to start planning the introduction of tailored strategies within screening programmes. Implementing risk-based screening based on age, sex and prior screening history alone would already represent a substantial improvement over current uniform screening approaches. We propose that it is time that screening programmes start there and continue striving towards more comprehensive approaches embedding primary prevention as an effective approach to lower risk for everyone.

Automated summary

Risk-based colorectal cancer screening can optimize resource allocation and improve the benefit-harm ratio. Reliable risk prediction models and participation are essential for successful implementation. While recognizing the limitations of current approaches, introducing tailored strategies within screening programmes is feasible and may lead to substantial improvements.