Abstract
(1) Background: The purpose of this study is to compare the performance of four breast cancer risk prediction models by race, molecular subtype, family history of breast cancer, age, and BMI. (2) Methods: Using a cohort of women aged 40-84 without prior history of breast cancer who underwent screening mammography from 2006 to 2015, we generated breast cancer risk estimates using the Breast Cancer Risk Assessment tool (BCRAT), BRCAPRO, Breast Cancer Surveillance Consortium (BCSC) and combined BRCAPRO+BCRAT models. Model calibration and discrimination were compared using observed-to-expected ratios (O/E) and the area under the receiver operator curve (AUC) among patients with at least five years of follow-up. (3) Results: We observed comparable discrimination and calibration across models. There was no significant difference in model performance between Black and White women. Model discrimination was poorer for HER2+ and triple-negative subtypes compared with ER/PR+HER2-. The BRCAPRO+BCRAT model displayed improved calibration and discrimination compared to BRCAPRO among women with a family history of breast cancer. Across models, discriminatory accuracy was greater among obese than non-obese women. When defining high risk as a 5-year risk of 1.67% or greater, models demonstrated discordance in 2.9% to 19.7% of patients. (4) Conclusions: Our results can inform the implementation of risk assessment and risk-based screening among women undergoing screening mammography.
Highlights
While breast cancer mortality has fallen over the past decade in the U.S, it remains the second leading cause of cancer death among women, with 43,600 breast cancer deaths projected in 2021 [1]
We evaluated the performance of Breast Cancer Risk Assessment tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC), BRCAPRO, and BRCAPRO+BCRAT models because these models utilize risk factors that are routinely collected during the course of clinical care in these three health systems, and the integration of one or more of these risk models into clinical care for decision making is potentially feasible
The Newton-Wellesley Hospital (NWH) population was younger than the Massachusetts General Hospital (MGH) and University of Pennsylvania Health System (UPenn) populations, the UPenn population included nearly 46% Black or African American women, and distributions of age at first birth, breast density, and the proportion of missing data differed across sites
Summary
While breast cancer mortality has fallen over the past decade in the U.S, it remains the second leading cause of cancer death among women, with 43,600 breast cancer deaths projected in 2021 [1]. Identification of patients at high risk of developing breast cancer could allow targeting of preventive and screening interventions to mitigate risk to further reduce mortality. Multiple validated risk assessment models have been developed to quantify an individual woman’s risk of developing breast cancer [2]. The Breast Cancer Risk Assessment Tool (BCRAT, known as the Gail model) utilizes age, race/ethnicity, history of breast biopsy and atypical hyperplasia, first-degree family history of breast cancer, age at menarche, and age at first birth to estimate risk [3,4,5,6]. The BRCAPRO model [9] was developed in the setting of women undergoing genetic counseling and uses a detailed family history of breast and other cancers to estimate risk both of a BRCA1/2 mutation and risk of breast cancer. The BRCAPRO model was combined with the BCRAT model to create the BRCAPRO+BCRAT model [10] that incorporates both factors in the BCRAT model and detailed family history
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