Development and external validation of a breast cancer absolute risk prediction model in Chinese population

Yuting Han; Jun Lv; Canqing Yu; Yu Guo; Zheng Bian; Yizhen Hu; Ling Yang; Yiping Chen; Huaidong Du; Fangyuan Zhao; Wanqing Wen; Xiao Ou Shu; Yongbing Xiang; Yu Tang Gao; Wei Zheng; Hong Guo; Peng Liang; Junshi Chen; Zhengming Chen; Dezheng Huo; Liming Li; China Kadoorie Biobank (CKB) collaborative group

doi:10.1186/s13058-021-01439-2

Development and external validation of a breast cancer absolute risk prediction model in Chinese population

Yuting Han, Jun Lv, Canqing Yu, Yu Guo, Zheng Bian, Yizhen Hu, Ling Yang, Yiping Chen, Huaidong Du, Fangyuan Zhao, Wanqing Wen, Xiao Ou Shu, Yongbing Xiang, Yu Tang Gao, Wei Zheng, Hong Guo, Peng Liang, Junshi Chen, Zhengming Chen, Dezheng HuoLiming Li, China Kadoorie Biobank (CKB) collaborative group

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Abstract

Backgrounds: In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. Methods: A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women’s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. Results: During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94–1.09) and 0.94 (95% CI, 0.89–0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608–0.661) and 0.585 (95% CI, 0.564–0.605) in the CKB and the SWHS, respectively. Conclusions: Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and aid risk-stratified screening and prevention strategies.

Original language	English
Article number	62
Number of pages	13
Journal	Breast Cancer Research
Volume	23
Issue number	1
DOIs	https://doi.org/10.1186/s13058-021-01439-2
Publication status	Published - 29 May 2021

Bibliographical note

This 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.

Funder

This work was supported by National Natural Science Foundation of China (91846303), and DH was supported by Breast Cancer Research Foundation. The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants (2016YFC0900500, 2016YFC0900501, 2016YFC0900504) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 81390541, 81390544), and Chinese Ministry of Science and Technology (2011BAI09B01). The SWHS was funded by National Institutes of Health/National Cancer Institute (UM1 CA182910 and R37CA70867).

Funding

This work was supported by National Natural Science Foundation of China (91846303), and DH was supported by Breast Cancer Research Foundation. The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants (2016YFC0900500, 2016YFC0900501, 2016YFC0900504) from the National Key R&D Program of China, National Natural Science Foundation of China (81390540, 81390541, 81390544), and Chinese Ministry of Science and Technology (2011BAI09B01). The SWHS was funded by National Institutes of Health/National Cancer Institute (UM1 CA182910 and R37CA70867).

Funders	Funder number
National Natural Science Foundation of China	91846303, 81390540, 81390541, 81390544
Breast Cancer Research Foundation
Kadoorie Charitable Foundation
National Key Research and Development Program of China	2016YFC0900500, 2016YFC0900501, 2016YFC0900504
Chinese Ministry of Science and Technology	2011BAI09B01
National Cancer Institute	UM1 CA182910, R37CA70867

Keywords

Absolute risk
Breast cancer
Global health
Prediction model
Prospective cohort study

ASJC Scopus subject areas

Oncology
Cancer Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1186/s13058-021-01439-2Licence: CC BY

PublishedFinal published version, 1.08 MBLicence: CC BY

Cite this

Han, Y., Lv, J., Yu, C., Guo, Y., Bian, Z., Hu, Y., Yang, L., Chen, Y., Du, H., Zhao, F., Wen, W., Shu, X. O., Xiang, Y., Gao, Y. T., Zheng, W., Guo, H., Liang, P., Chen, J., Chen, Z., ... China Kadoorie Biobank (CKB) collaborative group (2021). Development and external validation of a breast cancer absolute risk prediction model in Chinese population. Breast Cancer Research, 23(1), Article 62. https://doi.org/10.1186/s13058-021-01439-2

Han, Y, Lv, J, Yu, C, Guo, Y, Bian, Z, Hu, Y, Yang, L, Chen, Y, Du, H, Zhao, F, Wen, W, Shu, XO, Xiang, Y, Gao, YT, Zheng, W, Guo, H, Liang, P, Chen, J, Chen, Z, Huo, D, Li, L & China Kadoorie Biobank (CKB) collaborative group 2021, 'Development and external validation of a breast cancer absolute risk prediction model in Chinese population', Breast Cancer Research, vol. 23, no. 1, 62. https://doi.org/10.1186/s13058-021-01439-2

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abstract = "Backgrounds: In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. Methods: A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women{\textquoteright}s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. Results: During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94–1.09) and 0.94 (95% CI, 0.89–0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608–0.661) and 0.585 (95% CI, 0.564–0.605) in the CKB and the SWHS, respectively. Conclusions: Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals{\textquoteright} awareness and aid risk-stratified screening and prevention strategies.",

keywords = "Absolute risk, Breast cancer, Global health, Prediction model, Prospective cohort study",

author = "Yuting Han and Jun Lv and Canqing Yu and Yu Guo and Zheng Bian and Yizhen Hu and Ling Yang and Yiping Chen and Huaidong Du and Fangyuan Zhao and Wanqing Wen and Shu, {Xiao Ou} and Yongbing Xiang and Gao, {Yu Tang} and Wei Zheng and Hong Guo and Peng Liang and Junshi Chen and Zhengming Chen and Dezheng Huo and Liming Li and {China Kadoorie Biobank (CKB) collaborative group} and Om Kurmi",

note = "This 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. ",

year = "2021",

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doi = "10.1186/s13058-021-01439-2",

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AU - Lv, Jun

AU - Yu, Canqing

AU - Guo, Yu

AU - Bian, Zheng

AU - Hu, Yizhen

AU - Yang, Ling

AU - Chen, Yiping

AU - Du, Huaidong

AU - Zhao, Fangyuan

AU - Wen, Wanqing

AU - Shu, Xiao Ou

AU - Xiang, Yongbing

AU - Gao, Yu Tang

AU - Zheng, Wei

AU - Guo, Hong

AU - Liang, Peng

AU - Chen, Junshi

AU - Chen, Zhengming

AU - Huo, Dezheng

AU - Li, Liming

AU - China Kadoorie Biobank (CKB) collaborative group

AU - Kurmi, Om

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PY - 2021/5/29

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N2 - Backgrounds: In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. Methods: A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women’s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. Results: During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94–1.09) and 0.94 (95% CI, 0.89–0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608–0.661) and 0.585 (95% CI, 0.564–0.605) in the CKB and the SWHS, respectively. Conclusions: Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and aid risk-stratified screening and prevention strategies.

AB - Backgrounds: In contrast to developed countries, breast cancer in China is characterized by a rapidly escalating incidence rate in the past two decades, lower survival rate, and vast geographic variation. However, there is no validated risk prediction model in China to aid early detection yet. Methods: A large nationwide prospective cohort, China Kadoorie Biobank (CKB), was used to evaluate relative and attributable risks of invasive breast cancer. A total of 300,824 women free of any prior cancer were recruited during 2004–2008 and followed up to Dec 31, 2016. Cox models were used to identify breast cancer risk factors and build a relative risk model. Absolute risks were calculated by incorporating national age- and residence-specific breast cancer incidence and non-breast cancer mortality rates. We used an independent large prospective cohort, Shanghai Women’s Health Study (SWHS), with 73,203 women to externally validate the calibration and discriminating accuracy. Results: During a median of 10.2 years of follow-up in the CKB, 2287 cases were observed. The final model included age, residence area, education, BMI, height, family history of overall cancer, parity, and age at menarche. The model was well-calibrated in both the CKB and the SWHS, yielding expected/observed (E/O) ratios of 1.01 (95% confidence interval (CI), 0.94–1.09) and 0.94 (95% CI, 0.89–0.99), respectively. After eliminating the effect of age and residence, the model maintained moderate but comparable discriminating accuracy compared with those of some previous externally validated models. The adjusted areas under the receiver operating curve (AUC) were 0.634 (95% CI, 0.608–0.661) and 0.585 (95% CI, 0.564–0.605) in the CKB and the SWHS, respectively. Conclusions: Based only on non-laboratory predictors, our model has a good calibration and moderate discriminating capacity. The model may serve as a useful tool to raise individuals’ awareness and aid risk-stratified screening and prevention strategies.

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KW - Breast cancer

KW - Global health

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Development and external validation of a breast cancer absolute risk prediction model in Chinese population

Abstract

Bibliographical note

Funder

Funding

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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