Recursive partitioning analysis classification and graded prognostic assessment for non-small cell lung cancer
Cai-Xing Sun1, Tao Li, Xiao Zheng
1Zhejiang Traditional Chinese Medicine University, Hangzhou 310053, China ; Department of Neurooncology, Zhejiang Cancer Hospital, Hangzhou 310022, China.
Objective:
To assess prognostic factors and validate the effectiveness of recursive partitioning analysis (RPA) classes and graded prognostic assessment (GPA) in 290 non-small cell lung cancer (NSCLC) patients with brain metastasis (BM).
Methods:
From Jan 2008 to Dec 2009, the clinical data of 290 NSCLC cases with BM treated with multiple modalities including brain irradiation, systemic chemotherapy and tyrosine kinase inhibitors (TKIs) in two institutes were analyzed. Survival was estimated by Kaplan-Meier method. The differences of survival rates in subgroups were assayed using log-rank test. Multivariate Cox's regression method was used to analyze the impact of prognostic factors on survival. Two prognostic indexes models (RPA and GPA) were validated respectively.
Results:
All patients were followed up for 1-44 months, the median survival time after brain irradiation and its corresponding 95% confidence interval (95% CI) was 14 (12.3-15.8) months. 1-, 2- and 3-year survival rates in the whole group were 56.0%, 28.3%, and 12.0%, respectively. The survival curves of subgroups, stratified by both RPA and GPA, were significantly different (P<0.001). In the multivariate analysis as RPA and GPA entered Cox's regression model, Karnofsky performance status (KPS) ≥ 70, adenocarcinoma subtype, longer administration of TKIs remained their prognostic significance, RPA classes and GPA also appeared in the prognostic model.
Conclusion:
KPS ≥70, adenocarcinoma subtype, longer treatment of molecular targeted drug, and RPA classes and GPA are the independent prognostic factors affecting the survival rates of NSCLC patients with BM.
