[The development and validation of risk prediction model for lung cancer: a systematic review].

Objective: To systematically understand the global research progress in the construction and validation of lung cancer risk prediction models. Methods: "lung neoplasms" , "lung cancer" , "lung carcinoma" , "lung tumor" , "risk" , "malignancy" , "carcinogenesis" , "prediction" , "assessment" , "model" , "tool" , "score" , "paradigm" , and "algorithm" were used as search keywords. Original articles were systematically searched from Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, Cochrane, and Web of Science) published prior to December 2018. The language of studies was restricted to Chinese and English. The inclusion criteria were human oriented studies with complete information for model development, validation and evaluation. The exclusion criteria were informal publications such as conference abstracts, Chinese dissertation papers, and research materials such as reviews, letters, and news reports. A total of 33 papers involving 27 models were included. The population characteristics of all included studies, study design, predicting factors and the performance of models were analyzed and compared. Results: Among 27 models, the number of American-based, European-based and Asian-based model studies was 12, 6 and 9, respectively. In addition, there were 6 Chinese-based model studies. According to the factors fitted into the models, these studies could be divided into traditional epidemiological models (11 studies), clinical index models (6 studies), and genetic index models (10 studies). 15 models were not validated after construction or were cross-validated only in the internal population, and the extrapolation effect of models was not effectively evaluated; 8 models were validated in single external population; only 4 models were verified in multiple external populations (3-7); the area under the curve (AUC) of models ranged from 0.57 to 0.90. Conclusion: Research on risk prediction models for lung cancer is in development stage. In addition to the lack of external validation of existing models, the exploration of potential clinical indicators was also limited.目的： 系统评价肺癌风险预测模型构建与验证情况。 方法： 以"肺癌""肺肿瘤""发病""风险""危险""预测""预警""评估""评价""模型""评分"为中文关键词，以"lung neoplasms""lung cancer""lung carcinoma""lung tumor""risk""malignancy""carcinogenesis""prediction""assessment""model""tool""score""paradigm""algorithm"为英文关键词，系统检索中国知网、万方数据服务平台、PubMed、Embase、Cochrane和Web of Science数据库截至2018年12月发表的肺癌风险预测模型相关文献，语种限定为中文和英文。纳入标准为模型构建、验证及评价的信息完整；以人为研究对象。排除标准为会议摘要、中文学位论文等非正式发表文献；综述、述评、新闻报道等研究资料。共纳入33篇文献，涉及27个模型。对纳入研究的人群特征、研究类型、危险因素及模型预测结果等进行分析和比较。 结果： 18个模型基于欧美人群构建，9个模型基于亚洲人群构建，其中基于中国人群研究有7个；根据纳入因素分为传统流行病学因素模型（11个）、结合临床指标模型（6个）和遗传指标模型（10个）。15个模型在构建后未进行验证或仅在内部人群中进行了交叉验证，模型预测效果的外推性未得到有效评价；8个模型在1个外部人群中得到验证；仅有4个模型的风险预测效果在多（3~7）个外部人群中得到了验证；模型的曲线下面积为0.57~0.90。 结论： 肺癌风险预测模型研究处于发展阶段，模型预测效果的外部评价较少且现有模型对于临床指标的探索有限。.