The development and validation of risk prediction model for lung cancer: a systematic review
10.3760/cma.j.cn112150-20190523-00415
- VernacularTitle:肺癌风险预测模型构建与验证的系统综述
- Author:
Zhangyan LYU
1
;
Fengwei TAN
;
Chunqing LIN
;
Jiang LI
;
Yalong WANG
;
Hongda CHEN
;
Jiansong REN
;
Jufang SHI
;
Xiaoshuang FENG
;
Luopei WEI
;
Xin LI
;
Yan WEN
;
Wanqing CHEN
;
Min DAI
;
Ni LI
;
Jie HE
Author Information
1. 国家癌症中心 国家肿瘤临床医学研究中心 中国医学科学院 北京协和医学院肿瘤医院癌症早诊早治办公室，北京 100021
- Keywords:
Lung neoplasms;
Risk factors;
Forecasting;
Systematic review
- From:
Chinese Journal of Preventive Medicine
2020;54(4):430-437
- CountryChina
- Language:Chinese
-
Abstract:
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.
