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. 2021 Dec 23;14(1):45.
doi: 10.3390/cancers14010045.

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

Anne Marie McCarthy  1 , Yi Liu  1 , Sarah Ehsan  1 , Zoe Guan  2   3 , Jane Liang  2   3 , Theodore Huang  2 , Kevin Hughes  4 , Alan Semine  5 , Despina Kontos  6 , Emily Conant  6 , Constance Lehman  5 , Katrina Armstrong  5 , Danielle Braun  2   3 , Giovanni Parmigiani  2   3 , Jinbo Chen  1
Affiliations

Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes

Anne Marie McCarthy et al. Cancers (Basel). .

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.

Keywords: breast cancer; mammography; risk prediction.

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Conflict of interest statement

Giovanni Parmigiani is a co-founder and equity holder in Phaeno Biotechnologies, a member of the Scientific Advisory Board of Konica-Minolta Precision Medicine (which includes Ambry Genetics and Invicro), and a consultant for Delfi Diagnostics and Foundation Medicine. Danielle Braun and Giovanni Parmigiani co-lead the BayesMendel lab, which develops and maintains the BayesMendel software package. This includes a variety of risk assessment tools including BRCAPRO, PancPRO, MelaPRO, MMRpro, and PanelPRO and is licensed for commercial use. All licensing revenues are used for software maintenance and upgrades. Neither BayesMendel lab leaders nor members derive personal income from BayesMendel licenses. Danielle Braun and Giovanni Parmigiani are co-inventor of the Ask2me tool, which is commercially licensed. Kevin Hughes receives honoraria from Hologic (surgical implant for radiation planning with breast conservation and wire free breast biopsy) and Myriad Genetics, Hughes has financial interests in CRA Health (Formerly Hughes RiskApps) which recently was sold to Volpara. CRA Health develops risk assessment models/software with a particular focus on breast cancer and colorectal cancer. Hughes is a founder of the company. Hughes is the Co-Creator of Ask2Me.Org which is freely available for clinical use and is licensed for commercial use by the Dana Farber Cancer Institute and the MGH. Hughes’s interests in CRA Health and Ask2Me.Org were reviewed and are managed by Massachusetts General Hospital and Partners Health Care in accordance with their conflict-of-interest policies. Constance Lehman is co-founder of Clairity, which is developing an AI-based risk assessment products. Lehman’s interests in Clairity were reviewed and are managed by Massachusetts General Hospital and Partners Health Care in accordance with their conflict-of-interest policies. Emily Conant is on the grant and advisory boards for iCAD, Inc. and Hologic, Inc. The remaining authors have no conflicts to disclose. The funders of this study had no role in its design; the collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure A1
Figure A1
(a) Calibration curves for four models for individuals with no family history of breast cancer. (b) Calibration curves for four models for individuals with a family history of breast cancer. Each point on the figure represents the observed probability of breast cancer (Y-axis) for each decile of probability of breast cancer predicted by the model (X-axis). The diagonal line indicates perfect agreement between predicted and observed probability of breast cancer.
Figure A1
Figure A1
(a) Calibration curves for four models for individuals with no family history of breast cancer. (b) Calibration curves for four models for individuals with a family history of breast cancer. Each point on the figure represents the observed probability of breast cancer (Y-axis) for each decile of probability of breast cancer predicted by the model (X-axis). The diagonal line indicates perfect agreement between predicted and observed probability of breast cancer.
Figure 1
Figure 1
Exclusion criteria for screening mammography population by study site.
Figure 2
Figure 2
Calibration curves for risk prediction models.
See this image and copyright information in PMC

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