Risk Models for Breast Cancer and Their Validation

Abstract

Strategies to prevent cancer and diagnose it early when it is most treatable are needed to reduce the public health burden from rising disease incidence. Risk assessment is playing an increasingly important role in targeting individuals in need of such interventions. For breast cancer many individual risk factors have been well understood for a long time, but the development of a fully comprehensive risk model has not been straightforward, in part because there have been limited data where joint effects of an extensive set of risk factors may be estimated with precision. In this article we first review the approach taken to develop the IBIS (Tyrer-Cuzick) model, and describe recent updates. We then review and develop methods to assess calibration of models such as this one, where the risk of disease allowing for competing mortality over a long follow-up time or lifetime is estimated. The breast cancer risk model model and calibration assessment methods are demonstrated using a cohort of 132 139 women attending mammography screening in Washington, USA.

Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network

“…Due to the differences among individuals, the traditional TNM staging system cannot accurately predict the clinical prognosis of patients [ 24 ]. Researchers have attempted to build a new prognostic model to improve the accuracy and sensitivity of survival predictions for BC patients [ 25 , 26 ]. In the present study, a prognostic risk assessment model for BC was constructed based on Cox multivariate regression results.…”

Inclusion of the US Preventive Services Task Force Recommendation for Mammography in State Comprehensive Cancer Control Plans in the US

“… 4 , 8 Apart from the USPSTF, differences in recommendations arise from the different studies used for evidence to support the recommendations and the organizational criteria for weighing evidence, membership advocacy, and methods of harm as well as the benefit to harm ratio threshold calculations. 12 Some health system concerns are overdiagnosis, false-positive results, and radiation harm caused by the increased number of mammography examinations for women who receive a diagnosis at a younger age. The calculation of benefits and harms varies based on age group and evidence for individual preferences and values, leading to more emphasis on shared decision-making between patients and clinicians, 6 , 13 especially for those aged 40 to 49 years.…”

Performance of the IBIS/Tyrer‐Cuzick model of breast cancer risk by race and ethnicity in the Women's Health Initiative

“…1,2 Widely used breast cancer-prediction models include the breast cancer risk-assessment tool (BCRAT or Gail model) and the BRCAPRO, BOADICEA/CanRisk, and IBIS/Tyrer-Cuzick models. [3][4][5][6][7][8][9][10] Of these, the IBIS/Tyrer-Cuzick and BOADICEA/CanRisk models have shown the best calibration and discrimination. 3,[11][12][13] However, most prior studies of model performance had limited follow-up time or focused on women selected for family cancer history.…”

“…[3][4][5][6][7][8][9][10] Of these, the IBIS/Tyrer-Cuzick and BOADICEA/CanRisk models have shown the best calibration and discrimination. 3,[11][12][13] However, most prior studies of model performance had limited follow-up time or focused on women selected for family cancer history. Furthermore, all of these models were developed using data primarily from non-Hispanic White women.…”