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Cited by 18Cancer Risks and FactorsGlobal Cancer Incidence and ScreeningBRCA gene mutations in cancer

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

Open access
Han, Yuting, Lv, Jun, Yu, Canqing, Guo, Yu, Bian, Zheng, Hu, Yizhen, Yang, Ling, Chen, Yiping +191 more·2021·Breast Cancer Research
DOI 10.1186/s13058-021-01439-2PMC PMC8164768
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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.

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Authors
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., Huo, D., Li, L., on behalf of the China Kadoorie Biobank Collaborative Group, Chen, J., Chen, Z., Clarke, R., Collins, R., Guo, Y., Li, L., Lv, J., Peto, R., Walters, R., Avery, D., Boxall, R., Bennett, D., Chang, Y., Chen, Y., Chen, Z., Clarke, R., Du, H., Gilbert, S., Hacker, A., Hill, M., Holmes, M., Iona, A., Kartsonaki, C., Kerosi, R., Kong, L., Kurmi, O., Lancaster, G., Lewington, S., Lin, K., McDonnell, J., Millwood, I., Nie, Q., Radhakrishnan, J., Ryder, P., Sansome, S., Schmidt, D., Sherliker, P., Sohoni, R., Stevens, B., Turnbull, I., Walters, R., Wang, J., Wang, L., Wright, N., Yang, L., Yang, X., Bian, Z., Guo, Y., Han, X., Hou, C., Lv, J., Pei, P., Liu, C., Yu, C., Pang, Z., Gao, R., Li, S., Wang, S., Liu, Y., Du, R., Zang, Y., Cheng, L., Tian, X., Zhang, H., Zhai, Y., Ning, F., Sun, X., Li, F., Lv, S., Wang, J., Hou, W., Zeng, M., Jiang, G., Zhou, X., Yang, L., He, H., Yu, B., Li, Y., Xu, Q., Kang, Q., Guo, Z., Wang, D., Hu, X., Chen, J., Fu, Y., Fu, Z., Wang, X., Weng, M., Guo, Z., Wu, S., Li, Y., Li, H., Fu, Z., Wu, M., Zhou, Y., Zhou, J., Tao, R., Yang, J., Su, J., Liu, F., Zhang, J., Hu, Y., Lu, Y., Ma, L., Tang, A., Zhang, S., Jin, J., Liu, J., Tang, Z., Chen, N., Huang, Y., Li, M., Meng, J., Pan, R., Jiang, Q., Lan, J., Liu, Y., Wei, L., Zhou, L., Chen, N., Wang, P., Meng, F., Qin, Y., Wang, S., Wu, X., Zhang, N., Chen, X., Zhou, W., Luo, G., Li, J., Chen, X., Zhong, X., Liu, J., Sun, Q., Ge, P., Ren, X., Dong, C., Zhang, H., Mao, E., Wang, X., Wang, T., Zhang, X., Zhang, D., Zhou, G., Feng, S., Chang, L., Fan, L., Gao, Y., He, T., Sun, H., He, P., Hu, C., Zhang, X., Wu, H., He, P., Yu, M., Hu, R., Wang, H., Qian, Y., Wang, C., Xie, K., Chen, L., Zhang, Y., Pan, D., Gu, Q., Huang, Y., Chen, B., Yin, L., Liu, H., Fu, Z., Xu, Q., Xu, X., Zhang, H., Long, H., Li, X., Zhang, L., & Qiu, Z.
Affiliations
  • Peking University (CN)
  • University of Chicago (US)
  • Vanderbilt-Ingram Cancer Center (US)
  • Shanghai Jiao Tong University (CN)
  • China National Center for Food Safety Risk Assessment (CN)
  • Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
  • University of Oxford (GB)
  • Lianyungang Maternal and Children’s Hospital (CN)
Corresponding author
Huo, Dezheng
ORCID
Venue
Breast Cancer Research
Publisher
Springer Science and Business Media LLC
Published
2021-05-29
Volume
23
Issue
1
Pages
62-62
ISSN
  • 1465-5411 (ISSN-L)
  • 1465-542X
DOI
10.1186/s13058-021-01439-2
PMC
PMC8164768
OpenAlex
W3164953453
Cited by
18
References
32
Citations by year
2026: 1 · 2025: 9 · 2024: 1 · 2023: 3 · 2022: 4
FWCI
0.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Breast Cancer Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Pure (Coventry University) (acceptedVersion, cc-by)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (91846303, 81390540, 81390541, 81390544)
  • Breast Cancer Research Foundation
  • National Key R&D Program of China (2016YFC0900500, 2016YFC0900501, 2016YFC0900504)
  • Chinese Ministry of Science and Technology (2011BAI09B01)
  • National Institutes of Health/National Cancer Institute (UM1 CA182910,R37CA70867)
Topics
  • Health Sciences › Medicine › Oncology › Cancer Risks and Factors
  • Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › BRCA gene mutations in cancer
Keywords
breast cancer, risk factors, China Kadoorie Biobank, Shanghai Women's Health Study, external validation, model calibration, discrimination (statistics), risk-stratified screening, early detection, Chinese women, family history of cancer, parity, age at menarche, body mass index
MeSH terms
Adult, Aged, Breast Neoplasms, China, Female, Humans, Middle Aged, Prospective Studies, Risk Factors, Reproducibility of Results, Models, Statistical, Incidence, Women's Health, Risk Assessment, Area Under Curve, Asian People

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