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Article: Comparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification

TitleComparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification
Authors
Keywordsadolescent
adult
aged
breast tumor
female
Issue Date2020
PublisherOxford University Press. The Journal's web site is located at http://jncicancerspectrum.oxfordjournals.org/
Citation
JNCI: Journal of the National Cancer Institute, 2020, v. 112 n. 3, p. 278-285 How to Cite?
AbstractBackground: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. Methods: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35–74 years. Risk projections in a target population of US white non-Hispanic women age 50–70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Results: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. Conclusions: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
Persistent Identifierhttp://hdl.handle.net/10722/288170
ISSN
2023 Impact Factor: 9.9
2023 SCImago Journal Rankings: 4.986
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorPal Choudhury, P-
dc.contributor.authorWilcox, AN-
dc.contributor.authorBrook, MN-
dc.contributor.authorZhang, Y-
dc.contributor.authorAhearn, T-
dc.contributor.authorOrr, N-
dc.contributor.authorCoulson, P-
dc.contributor.authorSchoemaker, MJ-
dc.contributor.authorJones, ME-
dc.contributor.authorGail, MH-
dc.contributor.authorSwerdlow, AJ-
dc.contributor.authorChatterjee, N-
dc.contributor.authorGarcia-Closas, M-
dc.date.accessioned2020-10-05T12:08:54Z-
dc.date.available2020-10-05T12:08:54Z-
dc.date.issued2020-
dc.identifier.citationJNCI: Journal of the National Cancer Institute, 2020, v. 112 n. 3, p. 278-285-
dc.identifier.issn0027-8874-
dc.identifier.urihttp://hdl.handle.net/10722/288170-
dc.description.abstractBackground: External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development and comparative model validation and to make projections for population risk stratification. Methods: Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35–74 years. Risk projections in a target population of US white non-Hispanic women age 50–70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Results: The best calibrated models were iCARE-Lit (expected to observed number of cases [E/O] = 0.98, 95% confidence interval [CI] = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years. Conclusions: iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.-
dc.languageeng-
dc.publisherOxford University Press. The Journal's web site is located at http://jncicancerspectrum.oxfordjournals.org/-
dc.relation.ispartofJNCI: Journal of the National Cancer Institute-
dc.rightsPre-print: Journal Title] ©: [year] [owner as specified on the article] Published by Oxford University Press [on behalf of xxxxxx]. All rights reserved. Pre-print (Once an article is published, preprint notice should be amended to): This is an electronic version of an article published in [include the complete citation information for the final version of the Article as published in the print edition of the Journal.] Post-print: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in [insert journal title] following peer review. The definitive publisher-authenticated version [insert complete citation information here] is available online at: xxxxxxx [insert URL that the author will receive upon publication here].-
dc.subjectadolescent-
dc.subjectadult-
dc.subjectaged-
dc.subjectbreast tumor-
dc.subjectfemale-
dc.titleComparative Validation of Breast Cancer Risk Prediction Models and Projections for Future Risk Stratification-
dc.typeArticle-
dc.identifier.emailZhang, Y: doraz@hku.hk-
dc.identifier.authorityZhang, Y=rp02590-
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1093/jnci/djz113-
dc.identifier.pmid31165158-
dc.identifier.pmcidPMC7073933-
dc.identifier.scopuseid_2-s2.0-85075023860-
dc.identifier.hkuros315215-
dc.identifier.volume112-
dc.identifier.issue3-
dc.identifier.spage278-
dc.identifier.epage285-
dc.identifier.isiWOS:000582307200009-
dc.publisher.placeUnited Kingdom-
dc.identifier.issnl0027-8874-

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