Background: Risk-adjusted cancer screening and prevention is a promising and continuously emerging option for improving cancer prevention. It is driven by increasing knowledge of risk factors and the ability to determine them for individual risk prediction. However, there is a knowledge gap between evidence of increased risk and evidence of the effectiveness and efficiency of clinical preventive interventions based on increased risk. This gap is, in particular, aggravated by the extensive availability of genetic risk factor diagnostics, since the question of appropriate preventive measures immediately arises when an increased risk is identified. However, collecting proof of effective preventive measures, ideally by prospective randomized preventive studies, typically requires very long periods of time, while the knowledge about an increased risk immediately creates a high demand for action. Summary: Therefore, we propose a risk-adjusted prevention concept that is based on the best current evidence making needed and appropriate preventive measures available, and which is constantly evaluated through outcome evaluation, and continuously improved based on these results. We further discuss the structural and procedural requirements as well as legal and socioeconomical aspects relevant for the implementation of this concept.

Schmutzler, R. K., Schmitz-Luhn, B., Borisch, B., Devilee, P., Eccles, D., Hall, P., Balmaña, J., Boccia, S., Dabrock, P., Emons, G., Gaissmaier, W., Gronwald, J., Houwaart, S., Huster, S., Kast, K., Katalinic, A., Linn, S. C., Moorthie, S., Pharoah, P., Rhiem, K., Spranger, T., Stoppa-Lyonnet, D., Van Delden, J. J. M., Van Den Bulcke, M., Woopen, C., Risk-Adjusted Cancer Screening and Prevention (RiskAP): Complementing Screening for Early Disease Detection by a Learning Screening Based on Risk Factors, <<BREAST CARE>>, 2022; 17 (2): 208-223. [doi:10.1159/000517182] [https://hdl.handle.net/10807/225910]

Risk-Adjusted Cancer Screening and Prevention (RiskAP): Complementing Screening for Early Disease Detection by a Learning Screening Based on Risk Factors

Boccia, Stefania;
2022

Abstract

Background: Risk-adjusted cancer screening and prevention is a promising and continuously emerging option for improving cancer prevention. It is driven by increasing knowledge of risk factors and the ability to determine them for individual risk prediction. However, there is a knowledge gap between evidence of increased risk and evidence of the effectiveness and efficiency of clinical preventive interventions based on increased risk. This gap is, in particular, aggravated by the extensive availability of genetic risk factor diagnostics, since the question of appropriate preventive measures immediately arises when an increased risk is identified. However, collecting proof of effective preventive measures, ideally by prospective randomized preventive studies, typically requires very long periods of time, while the knowledge about an increased risk immediately creates a high demand for action. Summary: Therefore, we propose a risk-adjusted prevention concept that is based on the best current evidence making needed and appropriate preventive measures available, and which is constantly evaluated through outcome evaluation, and continuously improved based on these results. We further discuss the structural and procedural requirements as well as legal and socioeconomical aspects relevant for the implementation of this concept.
2022
Inglese
Schmutzler, R. K., Schmitz-Luhn, B., Borisch, B., Devilee, P., Eccles, D., Hall, P., Balmaña, J., Boccia, S., Dabrock, P., Emons, G., Gaissmaier, W., Gronwald, J., Houwaart, S., Huster, S., Kast, K., Katalinic, A., Linn, S. C., Moorthie, S., Pharoah, P., Rhiem, K., Spranger, T., Stoppa-Lyonnet, D., Van Delden, J. J. M., Van Den Bulcke, M., Woopen, C., Risk-Adjusted Cancer Screening and Prevention (RiskAP): Complementing Screening for Early Disease Detection by a Learning Screening Based on Risk Factors, <<BREAST CARE>>, 2022; 17 (2): 208-223. [doi:10.1159/000517182] [https://hdl.handle.net/10807/225910]
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