1. The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non–Small Cell Lung Cancer
- Author
-
James X. Zhang, Jiangping He, Yan Ma, Jianfeng Meng, Yonglin Pu, Raymond De Guzman, and Chin-Tu Chen
- Subjects
Adult ,Male ,Oncology ,medicine.medical_specialty ,Prognostic variable ,Lung Neoplasms ,Standardized uptake value ,Comorbidity ,Machine Learning ,Random Allocation ,03 medical and health sciences ,0302 clinical medicine ,Carcinoma, Non-Small-Cell Lung ,Positron Emission Tomography Computed Tomography ,Internal medicine ,medicine ,Humans ,Whole Body Imaging ,030212 general & internal medicine ,Stage (cooking) ,Lung cancer ,Survival analysis ,Aged ,Neoplasm Staging ,Retrospective Studies ,Models, Statistical ,business.industry ,Proportional hazards model ,030503 health policy & services ,Public Health, Environmental and Occupational Health ,Retrospective cohort study ,Middle Aged ,Prognosis ,medicine.disease ,Tumor Burden ,Female ,Radiopharmaceuticals ,0305 other medical science ,business - Abstract
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.
- Published
- 2020
- Full Text
- View/download PDF