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Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk

PURPOSE: We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS: Using nested...

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Autores principales: Allman, Richard, Mu, Yi, Dite, Gillian S., Spaeth, Erika, Hopper, John L., Rosner, Bernard A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020257/
https://www.ncbi.nlm.nih.gov/pubmed/36749458
http://dx.doi.org/10.1007/s10549-022-06834-7
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author Allman, Richard
Mu, Yi
Dite, Gillian S.
Spaeth, Erika
Hopper, John L.
Rosner, Bernard A.
author_facet Allman, Richard
Mu, Yi
Dite, Gillian S.
Spaeth, Erika
Hopper, John L.
Rosner, Bernard A.
author_sort Allman, Richard
collection PubMed
description PURPOSE: We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS: Using nested case–control data from the Nurses’ Health Study, we compared the models’ association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk. RESULTS: The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035). CONCLUSION: BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10549-022-06834-7.
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spelling pubmed-100202572023-03-18 Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk Allman, Richard Mu, Yi Dite, Gillian S. Spaeth, Erika Hopper, John L. Rosner, Bernard A. Breast Cancer Res Treat Epidemiology PURPOSE: We compared a simple breast cancer risk prediction model, BRISK (which includes mammographic density, polygenic risk and clinical factors), against a similar model with more risk factors (simplified Rosner) and against two commonly used clinical models (Gail and IBIS). METHODS: Using nested case–control data from the Nurses’ Health Study, we compared the models’ association, discrimination and calibration. Classification performance was compared between Gail and BRISK for 5-year risks and between IBIS and BRISK for remaining lifetime risk. RESULTS: The odds ratio per standard deviation was 1.43 (95% CI 1.32, 1.55) for BRISK 5-year risk, 1.07 (95% CI 0.99, 1.14) for Gail 5-year risk, 1.72 (95% CI 1.59, 1.87) for simplified Rosner 10-year risk, 1.51 (95% CI 1.41, 1.62) for BRISK remaining lifetime risk and 1.26 (95% CI 1.16, 1.36) for IBIS remaining lifetime risk. The area under the receiver operating characteristic curve (AUC) was improved for BRISK over Gail for 5-year risk (AUC = 0.636 versus 0.511, P < 0.0001) and for BRISK over IBIS for remaining lifetime risk (AUC = 0.647 versus 0.571, P < 0.0001). BRISK was well calibrated for the estimation of both 5-year risk (expected/observed [E/O] = 1.03; 95% CI 0.73, 1.46) and remaining lifetime risk (E/O = 1.01; 95% CI 0.86, 1.17). The Gail 5-year risk (E/O = 0.85; 95% CI 0.58, 1.24) and IBIS remaining lifetime risk (E/O = 0.73; 95% CI 0.60, 0.87) were not well calibrated, with both under-estimating risk. BRISK improves classification of risk compared to Gail 5-year risk (NRI = 0.31; standard error [SE] = 0.031) and IBIS remaining lifetime risk (NRI = 0.287; SE = 0.035). CONCLUSION: BRISK performs better than two commonly used clinical risk models and no worse compared to a similar model with more risk factors. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10549-022-06834-7. Springer US 2023-02-07 2023 /pmc/articles/PMC10020257/ /pubmed/36749458 http://dx.doi.org/10.1007/s10549-022-06834-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Epidemiology
Allman, Richard
Mu, Yi
Dite, Gillian S.
Spaeth, Erika
Hopper, John L.
Rosner, Bernard A.
Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
title Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
title_full Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
title_fullStr Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
title_full_unstemmed Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
title_short Validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
title_sort validation of a breast cancer risk prediction model based on the key risk factors: family history, mammographic density and polygenic risk
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10020257/
https://www.ncbi.nlm.nih.gov/pubmed/36749458
http://dx.doi.org/10.1007/s10549-022-06834-7
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