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.	趙亞太, 孫梅. 青島市單中心乳腺癌風險預測模型的研究. 臨床檢驗雜志(電子版), 2019, 8(2): 87.
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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乳腺癌模型應用價值及乳腺導管內乳頭狀腫瘤良惡性危險因素研究. 濟南: 山東大學, 2017.
14.	周建軍, 王燁菁, 高淑娜, 等. Gail乳腺癌風險評估模型的應用價值初探. 上海預防醫學, 2014, 26(5): 236-238.
15.	杜沛玲. 潮汕地區女性乳腺癌相關因素及風險評估模型. 汕頭: 汕頭大學, 2017.
16.	胡建英. Gail乳腺癌風險評估模型在浙東沿海地區婦女中的適用性研究. 杭州: 浙江大學, 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.
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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.	惲振先. 多因素人群歸因危險度估計及其計算程序. 中國衛生統計, 1993, 10(5): 53-55.
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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.
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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, 吳菲, 溫曉颯, 等. Gail乳腺癌風險評估模型的研究進展. 腫瘤, 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.	吳菲, 何丹丹, 趙根明, 等. 上海市女性乳腺癌危險因素分析與風險預測模型研究. 中華腫瘤防治雜志, 2017, 24(12): 795-801, 807.
32.	帥健, 李麗萍, 陳業群. 決策樹模型與Logistic回歸模型在傷害發生影響因素分析中的作用. 中華疾病控制雜志, 2015, 19(2): 185-189.

1. 赫捷, 陳萬青. 2016中國腫瘤登記年報. 北京: 清華大學出版社, 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. 趙亞太, 孫梅. 青島市單中心乳腺癌風險預測模型的研究. 臨床檢驗雜志(電子版), 2019, 8(2): 87.
6. 董華, 馬嵐. 基于機器學習的三陰乳腺癌預測模型. 云南大學學報(自然科學版), 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. 安麗營, 張開治, 張英麗, 等. Gail和Cuzick-Tyrer乳腺癌風險評估模型應用的初探. 中國婦幼保健, 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乳腺癌模型應用價值及乳腺導管內乳頭狀腫瘤良惡性危險因素研究. 濟南: 山東大學, 2017.
14. 周建軍, 王燁菁, 高淑娜, 等. Gail乳腺癌風險評估模型的應用價值初探. 上海預防醫學, 2014, 26(5): 236-238.
15. 杜沛玲. 潮汕地區女性乳腺癌相關因素及風險評估模型. 汕頭: 汕頭大學, 2017.
16. 胡建英. Gail乳腺癌風險評估模型在浙東沿海地區婦女中的適用性研究. 杭州: 浙江大學, 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. 衛生部統計信息中心. 2014中國衛生和計劃生育統計年鑒. 北京: 中國協和醫科大學出版社, 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. 惲振先. 多因素人群歸因危險度估計及其計算程序. 中國衛生統計, 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, 吳菲, 溫曉颯, 等. Gail乳腺癌風險評估模型的研究進展. 腫瘤, 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. 吳菲, 何丹丹, 趙根明, 等. 上海市女性乳腺癌危險因素分析與風險預測模型研究. 中華腫瘤防治雜志, 2017, 24(12): 795-801, 807.
32. 帥健, 李麗萍, 陳業群. 決策樹模型與Logistic回歸模型在傷害發生影響因素分析中的作用. 中華疾病控制雜志, 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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