The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer A Variable Importance Approach

doi:10.1097/MLR.0000000000001288

	The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer A Variable Importance Approach
	He, Jiangping; Zhang, James X.; Chen, Chin-tu ; Ma, Yan ; De Guzman, Raymond ; Meng, Jianfeng ; Pu, Yonglin
	2020-05
发表期刊	MEDICAL CARE
卷号	58 期号:5 页码:461-467
摘要	Background: Prognostic modeling in health care has been predominantly statistical, despite a rapid growth of literature on machine-learning approaches in biological data analysis. We aim to assess the relative importance of variables in predicting overall survival among patients with non-small cell lung cancer using a Variable Importance (VIMP) approach in a machine-learning Random Survival Forest (RSF) model for posttreatment planning and follow-up. Methods: A total of 935 non-small cell lung cancer patients were randomly and equally divided into 2 training and testing cohorts in an RFS model. The prognostic variables included age, sex, race, the TNM Classification of Malignant Tumors (TNM) stage, smoking history, Eastern Cooperative Oncology Group performance status, histologic type, treatment category, maximum standard uptake value of whole-body tumor (SUVmaxWB), whole-body metabolic tumor volume (MTVwb), and Charlson Comorbidity Index. The VIMP was calculated using a permutation method in the RSF model. We further compared the VIMP of the RSF model to that of the standard Cox survival model. We examined the order of VIMP with the differential functional forms of the variables. Results: In both the RSF and the standard Cox models, the most important variables are treatment category, TNM stage, and MTVwb. The order of VIMP is more robust in RSF model than in Cox model regarding the differential functional forms of the variables. Conclusions: The RSF VIMP approach can be applied alongside with the Cox model to further advance the understanding of the roles of prognostic factors, and improve prognostic precision and care efficiency.
关键词	prognostic prediction variable importance machine learning lung cancer random survival forest model
DOI	10.1097/MLR.0000000000001288
收录类别	SCI ; SCOPUS ; SCIE ; SSCI
ISSN	0025-7079
语种	英语
WOS研究方向	Health Care Sciences & Services ; Public, Environmental & Occupational Health
WOS类目	Health Care Sciences & Services ; Health Policy & Services ; Public, Environmental & Occupational Health
WOS记录号	WOS:000526851900008
出版者	LIPPINCOTT WILLIAMS & WILKINS
原始文献类型	Article
EISSN	1537-1948
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/425
专题	信息工程与人工智能学院
作者单位	1.Lanzhou Univ Finance & Econ, Sch Sci & Engn, Lanzhou, Gansu, Peoples R China; 2.Univ Chicago, Dept Med, 5841 South Maryland Ave,MC 5000, Chicago, IL 60637 USA; 3.Univ Chicago, Dept Radiol, Chicago, IL 60637 USA; 4.Nanxishan Hosp Guangxi Zhuang Autonomous Reg, Dept Resp & Crit Care Med, Guilin, Peoples R China
推荐引用方式 GB/T 7714	He, Jiangping,Zhang, James X.,Chen, Chin-tu,et al. The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer A Variable Importance Approach[J]. MEDICAL CARE,2020,58(5):461-467.
APA	He, Jiangping.,Zhang, James X..,Chen, Chin-tu.,Ma, Yan.,De Guzman, Raymond.,...&Pu, Yonglin.(2020).The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer A Variable Importance Approach.MEDICAL CARE,58(5),461-467.
MLA	He, Jiangping,et al."The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer A Variable Importance Approach".MEDICAL CARE 58.5(2020):461-467.