ArticleJournal of thoracic disease2019
A modified recursive partitioning analysis for predicting overall survival in patients with non-small cell lung cancer and central nervous system metastases.
Article in Journal of thoracic disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Risk factor of brain metastases and its influence on patient prognosis after complete resection of non-small cell lung cancer.American journal of translational research · 2022Article
- The development and validation of a nomogram for predicting brain metastases in lung squamous cell carcinoma patients: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database.Journal of thoracic disease · 2021Article
- Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNon-small cell lung cancer (NSCLC) is a major cause of brain metastases. Nonetheless, patients with central nervous system (CNS) spread are poorly represented in clinical trials. We sought to evaluate the overall survival (OS) of patients with NSCLC and CNS metastases.
methodsPatients with NSCLC and CNS metastases treated at A. C. Camargo Cancer Center from January 2007 to December 2017 were selected. The primary endpoint was OS following the diagnosis of CNS metastasis. The Kaplan-Meier method was applied to calculate OS. Prognostic factors were assessed by the Cox Proportional Hazards model. As an exploratory analysis, a survival tree was generated based upon the two most statistically significant variables in the multivariate model and one additional clinically meaningful variable.
resultsIn total, 311 patients were included. Median OS was 10.3 months (95% CI, 8.7-13.1 months). ECOG performance status 2-4 (HR 2.12; 95% CI, 1.40-3.20; P<0.01) and the absence of a driver mutation (HR 3.30; 95% CI, 1.85-5.90; P<0.01) were strongly associated with worse OS. A Modified Recursive Partitioning Analysis (mRPA) was developed based on the curves generated by the survival tree. mRPA stratified our cohort in four subgroups with significantly different OS (3.1 to 43 months) and it outperformed both RPA and GPA in predicting OS in our population.
conclusionsOS in our cohort was better than previously reported. However, prognosis is widely variable and is mostly dictated by performance status and the presence of a driver mutation.
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