[Applying decision trees to establish risk rating model of breast cancer incidence based on non-genetic factors among Southwest China females].

Objective: To estimate incident probability and establish risk rating model of breast cancer incidence under different combinations of non-genetic factors among Southwest China females, applying the decision trees. Methods: From 2014 to 2015, a total of 783 cases, which were pathologically diagnosed as primary breast cancer, were sequentially collected from West China Hospital of Sichuan University, Sichuan Cancer Hospital and Sichuan Province People's Hospital. 3, 879(excluding 36 samples with missing data) controls were randomly selected and matched by area of residence and age. Classification and regression tree (CART) algorithm was applied to construct breast cancer risk rating model according to non-genetic factors. 5 test sets were randomly selected for model validation. Results: BI-RADS classes, menopausal status, age, history of benign breast disease, menarche age, age of first delivery and number of live births were identified as risk factors and included in the risk rating model of breast cancer incidence. Among these factors, BI-RADS classes, menopausal status and age were the most important. The risk rating model developed were vitrificated by 5 test sets, and the average sensitivity, positive predictive value, accuracy were 95.60%, 92.26%, 97.93%, respectively. Conclusions: Breast cancer risk rating model constructed by decision trees was valid and reliable. The model could be used as the basic tool of breast cancer risk assessment among Southwest China females.目的： 运用决策树评估不同非遗传因素组合下乳腺癌发病的风险，构建中国西南地区女性非遗传因素乳腺癌风险等级模型。 方法： 序贯收集2014—2015年就诊于四川大学华西医院、四川省肿瘤医院和四川省人民医院乳腺外科、经病理学诊断的原发性乳腺癌新发病例783例，按城乡、年龄±1岁1∶5匹配3 879例对照(剔除数据缺失者36例)。采用分类回归树算法构建非遗传因素乳腺癌风险等级模型。随机抽取5个测试集，进行模型效能验证。 结果： 成功构建乳腺癌非遗传因素风险等级模型，超声乳腺影像报告和数据系统(BI－RADS)分类、绝经状态、年龄、乳腺良性病史、初潮年龄、初产年龄、活产次数为乳腺癌的风险因素，其中BI－RADS分类、绝经状态、年龄是影响乳腺癌发病最重要的3个因素。5个测试集评价决策树分类能力的平均灵敏度、阳性预测值和准确性分别为95.60%、92.26%和97.93%。 结论： 采用决策树构建的非遗传因素乳腺癌风险等级模型有效且可靠，能评估不同非遗传因素组合下乳腺癌发病的相对风险概率，可作为中国西南地区女性乳腺癌风险人群划分的基础工具。.