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Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer

BACKGROUND: The UK National Health Service’s Predict is a clinical tool widely used to estimate the prognosis of early-stage breast cancer. The performance of Predict for a second primary breast cancer is unknown. METHODS: Women 18 years of age or older diagnosed with a first or second invasive brea...

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Autores principales: Deng, Zhengyi, Jones, Miranda R, Wolff, Antonio C, Visvanathan, Kala
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10660126/
https://www.ncbi.nlm.nih.gov/pubmed/37773987
http://dx.doi.org/10.1093/jncics/pkad081
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author Deng, Zhengyi
Jones, Miranda R
Wolff, Antonio C
Visvanathan, Kala
author_facet Deng, Zhengyi
Jones, Miranda R
Wolff, Antonio C
Visvanathan, Kala
author_sort Deng, Zhengyi
collection PubMed
description BACKGROUND: The UK National Health Service’s Predict is a clinical tool widely used to estimate the prognosis of early-stage breast cancer. The performance of Predict for a second primary breast cancer is unknown. METHODS: Women 18 years of age or older diagnosed with a first or second invasive breast cancer between 2000 and 2013 and followed for at least 5 years were identified from the US Surveillance, Epidemiology, and End Results (SEER) database. Model calibration of Predict was evaluated by comparing predicted and observed 5-year breast cancer–specific mortality separately by estrogen receptor status for first vs second breast cancer. Receiver operating characteristic curves and areas under the curve were used to assess model discrimination. Model performance was also evaluated for various races and ethnicities. RESULTS: The study population included 6729 women diagnosed with a second breast cancer and 357 204 women with a first breast cancer. Overall, Predict demonstrated good discrimination for first and second breast cancers (areas under the curve ranging from 0.73 to 0.82). Predict statistically significantly underestimated 5-year breast cancer mortality for second estrogen receptor–positive breast cancers (predicted-observed = ‒6.24%, 95% CI = ‒6.96% to ‒5.49%). Among women with a first estrogen receptor–positive cancer, model calibration was good (predicted-observed = ‒0.22%, 95% CI = ‒0.29% to ‒0.15%), except in non-Hispanic Black women (predicted-observed = ‒2.33%, 95% CI = ‒2.65% to ‒2.01%) and women 80 years of age or older (predicted-observed = ‒3.75%, 95% CI = ‒4.12% to ‒3.41%). Predict performed well for second estrogen receptor–negative cancers overall (predicted-observed = ‒1.69%, 95% CI = ‒3.99% to 0.16%) but underestimated mortality among those who had previously received chemotherapy or had a first cancer with more aggressive tumor characteristics. In contrast, Predict overestimated mortality for first estrogen receptor–negative cancers (predicted-observed = 4.54%, 95% CI = 4.27% to 4.86%). CONCLUSION: The Predict tool underestimated 5-year mortality after a second estrogen receptor–positive breast cancer and in certain subgroups of women with a second estrogen receptor–negative breast cancer.
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spelling pubmed-106601262023-09-29 Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer Deng, Zhengyi Jones, Miranda R Wolff, Antonio C Visvanathan, Kala JNCI Cancer Spectr Article BACKGROUND: The UK National Health Service’s Predict is a clinical tool widely used to estimate the prognosis of early-stage breast cancer. The performance of Predict for a second primary breast cancer is unknown. METHODS: Women 18 years of age or older diagnosed with a first or second invasive breast cancer between 2000 and 2013 and followed for at least 5 years were identified from the US Surveillance, Epidemiology, and End Results (SEER) database. Model calibration of Predict was evaluated by comparing predicted and observed 5-year breast cancer–specific mortality separately by estrogen receptor status for first vs second breast cancer. Receiver operating characteristic curves and areas under the curve were used to assess model discrimination. Model performance was also evaluated for various races and ethnicities. RESULTS: The study population included 6729 women diagnosed with a second breast cancer and 357 204 women with a first breast cancer. Overall, Predict demonstrated good discrimination for first and second breast cancers (areas under the curve ranging from 0.73 to 0.82). Predict statistically significantly underestimated 5-year breast cancer mortality for second estrogen receptor–positive breast cancers (predicted-observed = ‒6.24%, 95% CI = ‒6.96% to ‒5.49%). Among women with a first estrogen receptor–positive cancer, model calibration was good (predicted-observed = ‒0.22%, 95% CI = ‒0.29% to ‒0.15%), except in non-Hispanic Black women (predicted-observed = ‒2.33%, 95% CI = ‒2.65% to ‒2.01%) and women 80 years of age or older (predicted-observed = ‒3.75%, 95% CI = ‒4.12% to ‒3.41%). Predict performed well for second estrogen receptor–negative cancers overall (predicted-observed = ‒1.69%, 95% CI = ‒3.99% to 0.16%) but underestimated mortality among those who had previously received chemotherapy or had a first cancer with more aggressive tumor characteristics. In contrast, Predict overestimated mortality for first estrogen receptor–negative cancers (predicted-observed = 4.54%, 95% CI = 4.27% to 4.86%). CONCLUSION: The Predict tool underestimated 5-year mortality after a second estrogen receptor–positive breast cancer and in certain subgroups of women with a second estrogen receptor–negative breast cancer. Oxford University Press 2023-09-29 /pmc/articles/PMC10660126/ /pubmed/37773987 http://dx.doi.org/10.1093/jncics/pkad081 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Article
Deng, Zhengyi
Jones, Miranda R
Wolff, Antonio C
Visvanathan, Kala
Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer
title Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer
title_full Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer
title_fullStr Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer
title_full_unstemmed Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer
title_short Evaluation of Predict, a prognostic risk tool, after diagnosis of a second breast cancer
title_sort evaluation of predict, a prognostic risk tool, after diagnosis of a second breast cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10660126/
https://www.ncbi.nlm.nih.gov/pubmed/37773987
http://dx.doi.org/10.1093/jncics/pkad081
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