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Cancer Screening, Diagnosis and Care

European guidelines on breast cancer screening and diagnosis


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2. Risk stratification

Introduction

Introduction (patients)

Aim

To evaluate the application of further risk-stratification approaches to breast cancer screening programmes.

Statement

Upon the review of the published literature on this topic, the ECIBC’s Guidelines Development Group (GDG) has considered that further evaluation of the impact of a risk-stratification approach on mortality, morbidity, acceptability, organisational and economic aspects will be required prior to its application in the context of organised, population-based screening. The GDG will continue to monitor closely this evolving field.

Rationale

Women and healthcare professionals are increasingly interested in risk-based screening approaches allowing for personalised management strategies.

In their current version, the European Breast Cancer Guidelines offer separate recommendations according to age, one of the main factors influencing the risk of developing breast cancer. The European guidelines also provide specific guidance for screening women with high mammographic breast density, a characteristic that increases the risk of developing the disease in addition to hindering the early detection of breast cancer.

In addition to age and breast density, there are other modifiable (e.g. body mass index, alcohol consumption) and non-modifiable (e.g. ethnicity, family history) factors that influence the risk of developing breast cancer. Therefore, estimating the impact of these factors on risk may help to refine the assessment and prediction of individual breast cancer risk.

As part of the ECIBC updating activities, the GDG reviewed the literature on available risk assessment methods for breast cancer with a view to inform stratification of the target population, e.g. as low, moderate, or high risk, and optimising the screening strategy according to the level of risk.

Justification

The GDG observed that breast cancer risk assessment is a rapidly evolving topic. Results from ongoing clinical trials and cohort studies will likely provide relevant empirical data. In addition, research on genotyping and, as shown in encouraging preliminary reports, research on deep-learning algorithm-based prediction models based on mammographic screening images should further improve the estimation of individual breast cancer risk.

Supporting material

The JRC Technical Report on the underpinning literature review of breast cancer risk assessment models will be made publicly accessible here.

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