Recursive partitioning analysis classification and graded prognostic assessment for non-small cell lung cancer patients with brain metastasis: A retrospective cohort study

  • Cai-xing Sun
  • Tao Li
  • Xiao Zheng
  • Ju-fen Cai
  • Xu-li Meng
  • Hong-jian Yang
  • Zheng Wang
Original Article
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Abstract

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.

Key words

Non-small cell lung cancer (NSCLC) Brain metastasis Prognosis Recursive partitioning analysis Graded prognostic assessment 
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Copyright information

© Chinese Anti-Cancer Association and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cai-xing Sun
    • 1
    • 2
  • Tao Li
    • 2
  • Xiao Zheng
    • 3
  • Ju-fen Cai
    • 4
  • Xu-li Meng
    • 1
  • Hong-jian Yang
    • 2
  • Zheng Wang
    • 5
  1. 1.Zhejiang Traditional Chinese Medicine UniversityHangzhouChina
  2. 2.Department of NeurooncologyZhejiang Cancer HospitalHangzhouChina
  3. 3.Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
  4. 4.Department of Medical OncologyZhejiang Cancer HospitalHangzhouChina
  5. 5.Zhejiang People’s HospitalHangzhouChina

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