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

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.

Predictors of Lung Adenocarcinoma With Leptomeningeal Metastases: A 2022 Targeted-Therapy-Assisted molGPA Model

“…We developed a novel targeted-therapy-assisted 2022 molGPA model for predicting LM in lung adenocarcinoma by incorporating a TKI therapy line in addition to a controlled primary tumor, KPS, and LANO neurological assessment. The 2022 molGPA model has a better prediction performance and is a substantial update of previous molGPA models ( 11 , 12 ). The 2022 molGPA model provides a user-friendly tool for estimating survival of lung adenocarcinoma patients with LM and may be useful in clinical decision-making and stratification of future clinical trials.…”

“…In both the lung-molGPA (2017) and molGPA (2019) models, gene mutation status was identified as a significant prognostic factor ( 11 , 12 ). From a clinical perspective, gene mutation status, which indicates molecular-targeted therapy, also has a significant impact on the treatment of EM and LM.…”

Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model

“…Lung adenocarcinoma is one of the diseases that frequently develop BM, and some disease-specific factors have been studied, such as, serum markers, epidermal growth factor receptor status (EGFR), tyrosine kinase inhibitor (TKI) therapy, and so on 6 . Recently, Sperduto et al .…”