• Sowmiya Moorthie, Chantal Babb de Villiers, Hilary Burton, Mark Kroese, Antonis C Antoniou, Proteeti Bhattacharjee, Montserrat Garcia-Closas, Per Hall, and Marjanka K Schmidt.
• PHG Foundation, University of Cambridge, Cambridge, UK; Cambridge Public Health, University of Cambridge School of Clinical Medicine, Forvie Site, Cambridge Biomedical Campus, Cambridge CB2 0SR, United Kingdom. Electronic address: Sowmiya.moorthie@phgfoundation.org.
• Prev Med. 2022 Jun 1; 159: 107075107075.

AbstractAdvances in knowledge about breast cancer risk factors have led to the development of more comprehensive risk models. These integrate information on a variety of risk factors such as lifestyle, genetics, family history, and breast density. These risk models have the potential to deliver more personalised breast cancer prevention. This is through improving accuracy of risk estimates, enabling more effective targeting of preventive options and creating novel prevention pathways through enabling risk estimation in a wider variety of populations than currently possible. The systematic use of risk tools as part of population screening programmes is one such example. A clear understanding of how such tools can contribute to the goal of personalised prevention can aid in understanding and addressing barriers to implementation. In this paper we describe how emerging models, and their associated tools can contribute to the goal of personalised healthcare for breast cancer through health promotion, early disease detection (screening) and improved management of women at higher risk of disease. We outline how addressing specific challenges on the level of communication, evidence, evaluation, regulation, and acceptance, can facilitate implementation and uptake.Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.

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