The current status of risk-stratified breast screening

Abstract

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Feasibility and Acceptability of Personalized Breast Cancer Screening (DECIDO Study): A Single-Arm Proof-of-Concept Trial

“…Clift el al. [ 48 ] point out the relevance of distinguishing between long-term risk prediction to guide screening strategies over a woman’s lifetime and estimating the risk of an underlying cancer being present at the time of screening. While a 5-year time frame is commonly used in models aimed at the general population, some authors propose using AI risk models with imaging data, over a 2-year timeframe, to identify women with a high probability of being diagnosed with a cancer that was missed, masked, or fast growing [ 16 , 17 ].…”

Healthcare professionals’ views following implementation of risk stratification into a national breast cancer screening programme

“…These algorithms allow tailored prevention and early detection for women at higher risk[ 6 ] and possibly reduced screening for women at lower risk [ 7 ]. With no randomised controlled trial (RCT) evidence to date[ 8 ], the effectiveness of risk-stratified screening at preventing the development of higher-stage breast cancers is being tested internationally in the WISDOM and MyPeBS trials[ 9 , 10 ].…”

Gene-specific machine learning model to predict the pathogenicity of BRCA2 variants

“…A Cochrane review published in 2013 found that it is unclear if mammographic screening does more good or harm (Gøtzsche & KJ, 2013). Recent studies suggest that mammographic screening could be most effective if offered based on the personal risk of the patient calculated from family history, breast density, reproductive factors, demographic, clinical, imaging-related and genetic data (Clift et al, 2021). This highlights the great importance of genetic testing in identifying high risk individuals for screening and early detection.…”