Personalized breast cancer onset prediction from lifestyle and health history information

We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Br...
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Autor*in:	Shi-ang Qi [verfasserIn] Neeraj Kumar [verfasserIn] Jian-Yi Xu [verfasserIn] Jaykumar Patel [verfasserIn] Sambasivarao Damaraju [verfasserIn] Grace Shen-Tu [verfasserIn] Russell Greiner [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: PLoS ONE - Public Library of Science (PLoS), 2007, 17(2022), 12
Übergeordnetes Werk:	volume:17 ; year:2022 ; number:12

Links:	Link aufrufen Link aufrufen Journal toc

Katalog-ID:	DOAJ020818386

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allfields	(DE-627)DOAJ020818386 (DE-599)DOAJ526ecba42395401eb7e4eb290ad86133 DE-627 ger DE-627 rakwb eng Shi-ang Qi verfasserin aut Personalized breast cancer onset prediction from lifestyle and health history information 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime. Medicine R Science Q Neeraj Kumar verfasserin aut Jian-Yi Xu verfasserin aut Jaykumar Patel verfasserin aut Sambasivarao Damaraju verfasserin aut Grace Shen-Tu verfasserin aut Russell Greiner verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 17(2022), 12 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:17 year:2022 number:12 https://doaj.org/article/526ecba42395401eb7e4eb290ad86133 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762602/?tool=EBI kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 12
spelling	(DE-627)DOAJ020818386 (DE-599)DOAJ526ecba42395401eb7e4eb290ad86133 DE-627 ger DE-627 rakwb eng Shi-ang Qi verfasserin aut Personalized breast cancer onset prediction from lifestyle and health history information 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime. Medicine R Science Q Neeraj Kumar verfasserin aut Jian-Yi Xu verfasserin aut Jaykumar Patel verfasserin aut Sambasivarao Damaraju verfasserin aut Grace Shen-Tu verfasserin aut Russell Greiner verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 17(2022), 12 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:17 year:2022 number:12 https://doaj.org/article/526ecba42395401eb7e4eb290ad86133 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762602/?tool=EBI kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 12
allfields_unstemmed	(DE-627)DOAJ020818386 (DE-599)DOAJ526ecba42395401eb7e4eb290ad86133 DE-627 ger DE-627 rakwb eng Shi-ang Qi verfasserin aut Personalized breast cancer onset prediction from lifestyle and health history information 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime. Medicine R Science Q Neeraj Kumar verfasserin aut Jian-Yi Xu verfasserin aut Jaykumar Patel verfasserin aut Sambasivarao Damaraju verfasserin aut Grace Shen-Tu verfasserin aut Russell Greiner verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 17(2022), 12 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:17 year:2022 number:12 https://doaj.org/article/526ecba42395401eb7e4eb290ad86133 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762602/?tool=EBI kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 12
allfieldsGer	(DE-627)DOAJ020818386 (DE-599)DOAJ526ecba42395401eb7e4eb290ad86133 DE-627 ger DE-627 rakwb eng Shi-ang Qi verfasserin aut Personalized breast cancer onset prediction from lifestyle and health history information 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime. Medicine R Science Q Neeraj Kumar verfasserin aut Jian-Yi Xu verfasserin aut Jaykumar Patel verfasserin aut Sambasivarao Damaraju verfasserin aut Grace Shen-Tu verfasserin aut Russell Greiner verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 17(2022), 12 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:17 year:2022 number:12 https://doaj.org/article/526ecba42395401eb7e4eb290ad86133 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762602/?tool=EBI kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 12
allfieldsSound	(DE-627)DOAJ020818386 (DE-599)DOAJ526ecba42395401eb7e4eb290ad86133 DE-627 ger DE-627 rakwb eng Shi-ang Qi verfasserin aut Personalized breast cancer onset prediction from lifestyle and health history information 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime. Medicine R Science Q Neeraj Kumar verfasserin aut Jian-Yi Xu verfasserin aut Jaykumar Patel verfasserin aut Sambasivarao Damaraju verfasserin aut Grace Shen-Tu verfasserin aut Russell Greiner verfasserin aut In PLoS ONE Public Library of Science (PLoS), 2007 17(2022), 12 (DE-627)523574592 (DE-600)2267670-3 19326203 nnns volume:17 year:2022 number:12 https://doaj.org/article/526ecba42395401eb7e4eb290ad86133 kostenfrei https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762602/?tool=EBI kostenfrei https://doaj.org/toc/1932-6203 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_34 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_235 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 17 2022 12
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title	Personalized breast cancer onset prediction from lifestyle and health history information
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title_sort	personalized breast cancer onset prediction from lifestyle and health history information
title_auth	Personalized breast cancer onset prediction from lifestyle and health history information
abstract	We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime.
abstractGer	We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime.
abstract_unstemmed	We propose a method to predict when a woman will develop breast cancer (BCa) from her lifestyle and health history features. To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. We anticipate this approach could be used to identify appropriate interventions for individuals with a higher likelihood of developing BCa in their lifetime.
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url	https://doaj.org/article/526ecba42395401eb7e4eb290ad86133 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9762602/?tool=EBI https://doaj.org/toc/1932-6203
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To address this objective, we use data from the Alberta’s Tomorrow Project of 18,288 women to train Individual Survival Distribution (ISD) models to predict an individual’s Breast-Cancer-Onset (BCaO) probability curve. We show that our three-step approach–(1) filling missing data with multiple imputations by chained equations, followed by (2) feature selection with the multivariate Cox method, and finally, (3) using MTLR to learn an ISD model–produced the model with the smallest L1-Hinge loss among all calibrated models with comparable C-index. We also identified 7 actionable lifestyle features that a woman can modify and illustrate how this model can predict the quantitative effects of those changes–suggesting how much each will potentially extend her BCa-free time. 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Personalized breast cancer onset prediction from lifestyle and health history information

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