Development of a prediction model of absolute risk for breast cancer_Chinese Journal of Evidence-Based Medicine

Authors：

DIAO Sha ¹ , HOU Can ¹ , ZHONG Xiaorong ² , LI Qin ³ , LI Xu ¹ , ZHOU Min ⁴ , YUAN Xuelian ⁵ , YI Fang ¹ , LI Jiachang ¹ , HAO Yu ¹ ,  LI Jiayuan ¹

1. Department of Epidemiology and Statistics, West China School of Public Health and West China Fourth Hospital of Sichuan University, Chengdu, 610041, P.R.China;
2. Department of Head, Neck, Mammary Gland Oncology, and Medical Oncology Cancer Center, West China Hospital of Sichuan University, Chengdu, 610041, P.R.China;
3. Department of Hospital Infection Management, Sichuan Provincial Hospital for Women and Children, Chengdu, 610031, P.R.China;
4. Department of Maternal and Child Information, Shuangliu Maternal and Child Health Hospital, Chengdu, 610200, P.R.China;
5. Department of National Office for Maternal and Child Health Surveillance of China, West China Second University Hospital of Sichuan University, Chengdu, 610041, P.R.China;

Corresponding author：

LI Jiayuan, Email: lijiayuan73@163.com

Keywords：

Breast cancer; Absolute risk; Prediction model; Case-control study

DOI：

10.7507/1672-2531.201907008

Video：

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Objectives To explore the construction method of prediction model of absolute risk for breast cancer and provide personalized breast cancer management strategies based on the results.Methods A case-control design was conducted with 2 747 individuals diagnosed as primary breast cancer by pathology in West China Hospital of Sichuan University from 2000 to 2017 and 6 307 healthy controls from Breast Cancer Screening Cohort in Sichuan Women and Children Center and Chengdu Shuangliu District Maternal and Child Health Hospital. Standardized questionnaires and information management systems in hospital were used to collect information. Decision trees, logistic regression, the formula in Gail model and registration data in China were used to estimate the probability of 5-year risk of breast cancer. Eventually a ROC (receiver operating characteristics) curve was drawn to identify optimal cut-off value, and the power was evaluated.Results The decision tree exported 4 variables, which were urban or rural sources, number of live birth, age and age at menarche. The median 5-year risk and interquartile range of the controls was 0.027% and 0.137%, while the median 5-year risk and interquartile range of the cases was 0.219% and 0.256%. The ROC curve showed the cut-off value was 0.100%. Through verification, the sensitivity was 0.79, the specificity was 0.73, the accuracy was 0.75, and the AUC (area under the curve) was 0.79.Conclusions The methods used in our study based on 9 054 female individuals in Sichuan province could be used to predict the 5-year risk for breast cancer. Predictor variables include urban or rural sources, number of live birth, age, and age at menarche. If the 5-year risk is more than 0.100%, the person will be judged as a high risk individual.

Citation： DIAO Sha, HOU Can, ZHONG Xiaorong, LI Qin, LI Xu, ZHOU Min, YUAN Xuelian, YI Fang, LI Jiachang, HAO Yu, LI Jiayuan. Development of a prediction model of absolute risk for breast cancer. Chinese Journal of Evidence-Based Medicine, 2019, 19(12): 1388-1394. doi: 10.7507/1672-2531.201907008 Copy

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5.	赵亚太, 孙梅. 青岛市单中心乳腺癌风险预测模型的研究. 临床检验杂志(电子版), 2019, 8(2): 87.
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9.	Novotny J, Pecen L, Petruzelka L, et al. Breast cancer risk assessment in the Czech female population-an adjustment of the original Gail model. Breast Cancer Res Treat, 2006, 95(1): 29-35.
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11.	Bondy ML, Newman LA. Breast cancer risk assessment models: applicability to African-American women. Cancer, 2003, 97(1 Suppl): 230-235.
12.	Merajver SD, Milliron K. Breast cancer risk assessment: a guide for clinicians using the NCCN Breast Cancer Risk Reduction Guidelines. J Natl Compr Canc Netw, 2003, 1(2): 297-301.
13.	王春亭. Gail乳腺癌模型应用价值及乳腺导管内乳头状肿瘤良恶性危险因素研究. 济南: 山东大学, 2017.
14.	周建军, 王烨菁, 高淑娜, 等. Gail乳腺癌风险评估模型的应用价值初探. 上海预防医学, 2014, 26(5): 236-238.
15.	杜沛玲. 潮汕地区女性乳腺癌相关因素及风险评估模型. 汕头: 汕头大学, 2017.
16.	胡建英. Gail乳腺癌风险评估模型在浙东沿海地区妇女中的适用性研究. 杭州: 浙江大学, 2015.
17.	Cao W, Wang X, Li JC. Hereditary breast cancer in the Han Chinese population. J Epidemiol, 2013, 23(2): 75-84.
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19.	卫生部统计信息中心. 2014中国卫生和计划生育统计年鉴. 北京: 中国协和医科大学出版社, 2014.
20.	Bruzzi P, Green SB, Byar DP, et al. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol, 1985, 122(5): 904-914.
21.	恽振先. 多因素人群归因危险度估计及其计算程序. 中国卫生统计, 1993, 10(5): 53-55.
22.	Winters S, Martin C, Murphy D, et al. Breast cancer epidemiology, prevention, and screening. Prog Mol Biol Transl Sci, 2017, 151: 1-32.
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24.	Khalis M, Charbotel B, Chajès V, et al. Menstrual and reproductive factors and risk of breast cancer: a case-control study in the Fez region, Morocco. PLoS One, 2018, 13(1): e0191333.
25.	Rajbongshi N, Mahanta LB, Nath DC, et al. A matched case control study of risk indicators of breast cancer in Assam, India. Mymensingh Med J, 2015, 24(2): 385-391.
26.	Lambe M, Hsieh C, Trichopoulos D, et al. Transient increase in the risk of breast cancer after giving birth. N Engl J Med, 1994, 331(1): 5-9.
27.	Albrektsen G, Heuch I, Thoresen S, et al. Family history of breast cancer and short-term effects of childbirths on breast cancer risk. Int J Cancer, 2006, 119(6): 1468-1474.
28.	何丹丹, 吴菲, 温晓飒, 等. Gail乳腺癌风险评估模型的研究进展. 肿瘤, 2016, 36(12): 1389-1394.
29.	Crispo A, D'Aiuto G, De Marco M, et al. Gail model risk factors: impact of adding an extended family history for breast cancer. Breast J, 2008, 14(3): 221-227.
30.	Dai J, Hu Z, Jiang Y, et al. Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women. Breast Cancer Res, 2012, 14(1): R17.
31.	吴菲, 何丹丹, 赵根明, 等. 上海市女性乳腺癌危险因素分析与风险预测模型研究. 中华肿瘤防治杂志, 2017, 24(12): 795-801, 807.
32.	帅健, 李丽萍, 陈业群. 决策树模型与Logistic回归模型在伤害发生影响因素分析中的作用. 中华疾病控制杂志, 2015, 19(2): 185-189.

1. 赫捷, 陈万青. 2016中国肿瘤登记年报. 北京: 清华大学出版社, 2017.
2. Fan L, Strasserweippl K, Li JJ, et al. Breast cancer in China. Lancet Oncol, 2014, 15(7): e279-e289.
3. DeSantis CE, Ma J, Goding Sauer A, et al. Breast cancer statistics, 2017, racial disparity in mortality by state. CA Cancer J Clin, 2017, 67(6): 439-448.
4. Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin, 2016, 66(2): 115-132.
5. 赵亚太, 孙梅. 青岛市单中心乳腺癌风险预测模型的研究. 临床检验杂志(电子版), 2019, 8(2): 87.
6. 董华, 马岚. 基于机器学习的三阴乳腺癌预测模型. 云南大学学报(自然科学版), 2017, (S1): 111-115.
7. Zhang L, Jie Z, Xu S, et al. Use of receiver operating characteristic (roc) curve analysis for tyrer-cuzick and gail in breast cancer screening in Jiangxi province, China. Med Sci Monit, 2018, 24: 5528-5532.
8. 安丽营, 张开治, 张英丽, 等. Gail和Cuzick-Tyrer乳腺癌风险评估模型应用的初探. 中国妇幼保健, 2016, 31(5): 945-946.
9. Novotny J, Pecen L, Petruzelka L, et al. Breast cancer risk assessment in the Czech female population-an adjustment of the original Gail model. Breast Cancer Res Treat, 2006, 95(1): 29-35.
10. Newman LA. Applicability of the Gail model for breast cancer risk assessment in Turkish female population and evaluation of breastfeeding as a risk factor. Breast Cancer Res Treat, 2010, 120(2): 425-426.10.
11. Bondy ML, Newman LA. Breast cancer risk assessment models: applicability to African-American women. Cancer, 2003, 97(1 Suppl): 230-235.
12. Merajver SD, Milliron K. Breast cancer risk assessment: a guide for clinicians using the NCCN Breast Cancer Risk Reduction Guidelines. J Natl Compr Canc Netw, 2003, 1(2): 297-301.
13. 王春亭. Gail乳腺癌模型应用价值及乳腺导管内乳头状肿瘤良恶性危险因素研究. 济南: 山东大学, 2017.
14. 周建军, 王烨菁, 高淑娜, 等. Gail乳腺癌风险评估模型的应用价值初探. 上海预防医学, 2014, 26(5): 236-238.
15. 杜沛玲. 潮汕地区女性乳腺癌相关因素及风险评估模型. 汕头: 汕头大学, 2017.
16. 胡建英. Gail乳腺癌风险评估模型在浙东沿海地区妇女中的适用性研究. 杭州: 浙江大学, 2015.
17. Cao W, Wang X, Li JC. Hereditary breast cancer in the Han Chinese population. J Epidemiol, 2013, 23(2): 75-84.
18. Petracci E, Decarli A, Schairer C, et al. Risk factor modification and projections of absolute breast cancer risk. J Natl Cancer Inst, 2011, 103(13): 1037-1048.
19. 卫生部统计信息中心. 2014中国卫生和计划生育统计年鉴. 北京: 中国协和医科大学出版社, 2014.
20. Bruzzi P, Green SB, Byar DP, et al. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol, 1985, 122(5): 904-914.
21. 恽振先. 多因素人群归因危险度估计及其计算程序. 中国卫生统计, 1993, 10(5): 53-55.
22. Winters S, Martin C, Murphy D, et al. Breast cancer epidemiology, prevention, and screening. Prog Mol Biol Transl Sci, 2017, 151: 1-32.
23. Hilton HN, Clarke CL, Graham JD. Estrogen and progesterone signalling in the normal breast and its implications for cancer development. Mol Cell Endocrinol, 2018, 466: 2-14.
24. Khalis M, Charbotel B, Chajès V, et al. Menstrual and reproductive factors and risk of breast cancer: a case-control study in the Fez region, Morocco. PLoS One, 2018, 13(1): e0191333.
25. Rajbongshi N, Mahanta LB, Nath DC, et al. A matched case control study of risk indicators of breast cancer in Assam, India. Mymensingh Med J, 2015, 24(2): 385-391.
26. Lambe M, Hsieh C, Trichopoulos D, et al. Transient increase in the risk of breast cancer after giving birth. N Engl J Med, 1994, 331(1): 5-9.
27. Albrektsen G, Heuch I, Thoresen S, et al. Family history of breast cancer and short-term effects of childbirths on breast cancer risk. Int J Cancer, 2006, 119(6): 1468-1474.
28. 何丹丹, 吴菲, 温晓飒, 等. Gail乳腺癌风险评估模型的研究进展. 肿瘤, 2016, 36(12): 1389-1394.
29. Crispo A, D'Aiuto G, De Marco M, et al. Gail model risk factors: impact of adding an extended family history for breast cancer. Breast J, 2008, 14(3): 221-227.
30. Dai J, Hu Z, Jiang Y, et al. Breast cancer risk assessment with five independent genetic variants and two risk factors in Chinese women. Breast Cancer Res, 2012, 14(1): R17.
31. 吴菲, 何丹丹, 赵根明, 等. 上海市女性乳腺癌危险因素分析与风险预测模型研究. 中华肿瘤防治杂志, 2017, 24(12): 795-801, 807.
32. 帅健, 李丽萍, 陈业群. 决策树模型与Logistic回归模型在伤害发生影响因素分析中的作用. 中华疾病控制杂志, 2015, 19(2): 185-189.

Chinese Journal of Evidence-Based Medicine

Development of a prediction model of absolute risk for breast cancer

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