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Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model

BACKGROUND: Considering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women. METHODS: A cause-...

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Autores principales: Wang, Lu, Liu, Liyuan, Lou, Zhen, Ding, Lijie, Guan, Hui, Wang, Fei, Yu, Lixiang, Xiang, Yujuan, Zhou, Fei, Xue, Fuzhong, Yu, Zhigang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367757/
https://www.ncbi.nlm.nih.gov/pubmed/30732565
http://dx.doi.org/10.1186/s12885-019-5321-1
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author Wang, Lu
Liu, Liyuan
Lou, Zhen
Ding, Lijie
Guan, Hui
Wang, Fei
Yu, Lixiang
Xiang, Yujuan
Zhou, Fei
Xue, Fuzhong
Yu, Zhigang
author_facet Wang, Lu
Liu, Liyuan
Lou, Zhen
Ding, Lijie
Guan, Hui
Wang, Fei
Yu, Lixiang
Xiang, Yujuan
Zhou, Fei
Xue, Fuzhong
Yu, Zhigang
author_sort Wang, Lu
collection PubMed
description BACKGROUND: Considering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women. METHODS: A cause-specific competing risk model was used to develop the Han Chinese Breast Cancer Prediction model. Data from the Shandong Case-Control Study (328 cases and 656 controls) and Taixing Prospective Cohort Study (13,176 participants) were used to develop and validate the model. The expected/observed (E/O) ratio and C-statistic were calculated to evaluate calibration and discriminative accuracy of the model, respectively. RESULTS: Compared with the reference level, the relative risks (RRs) for highest level of number of abortions, age at first live birth, history of benign breast disease, body mass index (BMI), family history of breast cancer, and life satisfaction scores were 6.3, 3.6, 4.3, 1.9, 3.3, 2.4, respectively. The model showed good calibration and discriminatory accuracy with an E/O ratio of 1.03 and C-statistic of 0.64. CONCLUSIONS: We developed a risk prediction model including fertility status and relevant disease history, as well as other modifiable risk factors. The model demonstrated good calibration and discrimination ability. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-019-5321-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-63677572019-02-15 Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model Wang, Lu Liu, Liyuan Lou, Zhen Ding, Lijie Guan, Hui Wang, Fei Yu, Lixiang Xiang, Yujuan Zhou, Fei Xue, Fuzhong Yu, Zhigang BMC Cancer Research Article BACKGROUND: Considering the lack of efficient breast cancer prediction models suitable for general population screening in China. We aimed to develop a risk prediction model to identify high-risk populations, to help with primary prevention of breast cancer among Han Chinese women. METHODS: A cause-specific competing risk model was used to develop the Han Chinese Breast Cancer Prediction model. Data from the Shandong Case-Control Study (328 cases and 656 controls) and Taixing Prospective Cohort Study (13,176 participants) were used to develop and validate the model. The expected/observed (E/O) ratio and C-statistic were calculated to evaluate calibration and discriminative accuracy of the model, respectively. RESULTS: Compared with the reference level, the relative risks (RRs) for highest level of number of abortions, age at first live birth, history of benign breast disease, body mass index (BMI), family history of breast cancer, and life satisfaction scores were 6.3, 3.6, 4.3, 1.9, 3.3, 2.4, respectively. The model showed good calibration and discriminatory accuracy with an E/O ratio of 1.03 and C-statistic of 0.64. CONCLUSIONS: We developed a risk prediction model including fertility status and relevant disease history, as well as other modifiable risk factors. The model demonstrated good calibration and discrimination ability. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-019-5321-1) contains supplementary material, which is available to authorized users. BioMed Central 2019-02-07 /pmc/articles/PMC6367757/ /pubmed/30732565 http://dx.doi.org/10.1186/s12885-019-5321-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Wang, Lu
Liu, Liyuan
Lou, Zhen
Ding, Lijie
Guan, Hui
Wang, Fei
Yu, Lixiang
Xiang, Yujuan
Zhou, Fei
Xue, Fuzhong
Yu, Zhigang
Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model
title Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model
title_full Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model
title_fullStr Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model
title_full_unstemmed Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model
title_short Risk prediction for breast Cancer in Han Chinese women based on a cause-specific Hazard model
title_sort risk prediction for breast cancer in han chinese women based on a cause-specific hazard model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367757/
https://www.ncbi.nlm.nih.gov/pubmed/30732565
http://dx.doi.org/10.1186/s12885-019-5321-1
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