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Calculating, Using and Improving Individual Breast Cancer Risk Estimates

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Breast MRI for High-risk Screening

Abstract

Breast cancer risk assessment is important in order to identify individuals at extremely high risk who would be potential candidates for prophylactic surgery or preventive therapy, those at a moderately enhanced risk who might benefit from enhanced surveillance and potentially those at sufficiently low risk as to not require surveillance or risk management. Women with a substantial family history of the disease qualify for genetic testing of high-risk BRCA1/2 genetic alterations. For women without mutations in high-risk genes, or without a large family history, the current state of the art for risk assessment combines classical questionnaire risk factors (including information on hormonal and reproductive factors), mammographic density and genetic testing through polygenic risk scores. Risk models for these components alone and their combination have shown a degree of accuracy in prediction of invasive breast cancer risk. In this chapter, we consider how risk models are calculated, their potential clinical use and the targets for research in the future to improve risk assessment in order to better inform risk management and surveillance.

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Notes

  1. 1.

    Segregation analysis. The process of fitting formal genetic models to data on expressed disease characteristics (phenotype) in biological family members in order to determine the most likely mode of inheritance for the trait or disease under study (https://www.cancer.gov/publications/dictionaries/genetics-dictionary/def/segregation-analysis).

  2. 2.

    The C-statistic (sometimes called the ‘concordance’ statistic or C-index) for a binary outcome, such as disease or not, gives the probability a randomly selected patient with disease will have a higher risk score than a patient without disease. For a survival outcome, such as time to death, it gives the probability that the person with the higher risk score will live longer [48].

Abbreviations

AIs:

Aromatase inhibitors

AUC:

Area under the curve

BC:

Breast cancer

BCSC:

Breast Cancer Surveillance Consortium

BI-RADS:

Breast Imaging Reporting and Data System

BMI:

Body mass index

BPE:

Background parenchymal enhancement

ER:

Oestrogen receptor

HER2:

Human epidermal growth factor receptor 2

MRI:

Magnetic resonance imaging

ROC:

Receiver operating characteristic

SERMs:

Selective oestrogen receptor modulators

SNP:

Single nucleotide polymorphism

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Authors and Affiliations

  1. Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    Adam R. Brentnall & Stephen W. Duffy

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  1. Adam R. Brentnall
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Correspondence to Stephen W. Duffy .

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  1. Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy

    Francesco Sardanelli

  2. Department of Cell Biology and Neurosciences Molecular and Cellular Imaging Unit, Istituto Superiore di Sanità (ISS), Rome, Italy

    Franca Podo

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Brentnall, A.R., Duffy, S.W. (2020). Calculating, Using and Improving Individual Breast Cancer Risk Estimates. In: Sardanelli, F., Podo, F. (eds) Breast MRI for High-risk Screening. Springer, Cham. https://doi.org/10.1007/978-3-030-41207-4_20

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