Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes
(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 screeni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/14/1/45 |
| _version_ | 1797499468273156096 |
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| author | Anne Marie McCarthy Yi Liu Sarah Ehsan Zoe Guan Jane Liang Theodore Huang Kevin Hughes Alan Semine Despina Kontos Emily Conant Constance Lehman Katrina Armstrong Danielle Braun Giovanni Parmigiani Jinbo Chen |
| author_facet | Anne Marie McCarthy Yi Liu Sarah Ehsan Zoe Guan Jane Liang Theodore Huang Kevin Hughes Alan Semine Despina Kontos Emily Conant Constance Lehman Katrina Armstrong Danielle Braun Giovanni Parmigiani Jinbo Chen |
| author_sort | Anne Marie McCarthy |
| collection | DOAJ |
| description | (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. |
| first_indexed | 2024-03-10T03:47:48Z |
| format | Article |
| id | doaj.art-e916405fc80c461099cd62569bdeb827 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T03:47:48Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-e916405fc80c461099cd62569bdeb8272023-11-23T11:15:11ZengMDPI AGCancers2072-66942021-12-011414510.3390/cancers14010045Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular SubtypesAnne Marie McCarthy0Yi Liu1Sarah Ehsan2Zoe Guan3Jane Liang4Theodore Huang5Kevin Hughes6Alan Semine7Despina Kontos8Emily Conant9Constance Lehman10Katrina Armstrong11Danielle Braun12Giovanni Parmigiani13Jinbo Chen14Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USADepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USADepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USAMassachusetts General Hospital, Boston, MA 02114, USANewton Wellesley Hospital, Newton, MA 02462, USADepartment of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USADepartment of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USANewton Wellesley Hospital, Newton, MA 02462, USANewton Wellesley Hospital, Newton, MA 02462, USADepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USADepartment of Biostatistics, Harvard T. H. Chan School of Public Health, Harvard University, Boston, MA 02115, USADepartment of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA(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.https://www.mdpi.com/2072-6694/14/1/45breast cancerrisk predictionmammography |
| spellingShingle | Anne Marie McCarthy Yi Liu Sarah Ehsan Zoe Guan Jane Liang Theodore Huang Kevin Hughes Alan Semine Despina Kontos Emily Conant Constance Lehman Katrina Armstrong Danielle Braun Giovanni Parmigiani Jinbo Chen Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes Cancers breast cancer risk prediction mammography |
| title | Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes |
| title_full | Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes |
| title_fullStr | Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes |
| title_full_unstemmed | Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes |
| title_short | Validation of Breast Cancer Risk Models by Race/Ethnicity, Family History and Molecular Subtypes |
| title_sort | validation of breast cancer risk models by race ethnicity family history and molecular subtypes |
| topic | breast cancer risk prediction mammography |
| url | https://www.mdpi.com/2072-6694/14/1/45 |
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