Thiago Pimentel Muniz
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Victor Hugo Fonseca Jesus
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Victor Aurelio Ramos Sousa
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malu barbosa
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Vladmir Claudio Cordeiro De Lima
e20604 Background: Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancers. Most patients with NSCLC have metastases at diagnosis and 30-50% of them will present brain lesions during follow-up. Nonetheless, patients with central nervous system (CNS) spread are poorly represented in clinical trials and the management of this clinical scenario is not standardized. Prognostic tolls are needed to help to guide the best approach for these patients. Methods: Descriptive, analytical, retrospective, single center study. Patients > 18 years of age diagnosed with NSCLC who developed CNS metastases treated at AC Camargo Cancer Center from January 2007 to December 2017 were included. The primary endpoint was overall survival (OS). The secondary endpoint was intracranial progression-free survival (IC-PFS). An exploratory analysis of prognostic factors associated with OS was undertaken. The Kaplan-Meier method was used to calculate OS and IC-PFS. We used the Cox Proportional Hazard model to assess the prognostic role of clinical and pathological characteristics on IC-PFS and OS. Variables with p < 0.20 at univariate analysis were used to generate a multivariate model. A survival tree was generated based on the two most statistically significant variables in the multivariate model. A p < 0.05 was considered statistically significant. Results: 311 patients were included. Median age was 60 years-old (IQR 54-68), 72.6% had an ECOG performance status 0 or 1 and 18.3% had a driver mutation detected ( EGFR, ALK or ROS1). IC-PFS was 7.1 months (95%CI 6.1-8.6) and OS was 10.3 months (95%CI 8.7-13.1). At multivariate analysis, the two most statistically significant prognostic factors associated with poor OS were ECOG performance status 2-4 (HR 2.12; 95%CI 1.40-3.20; p < 0.01) and the absence of a known driver mutation (HR 3.30; 95%CI 1.50-3.87; p < 0.01). Based on the curves generated by the survival tree, a prognostic model was developed - the Modified Recursive Partitioning Analysis (mRPA). This model stratified our cohort in four subgroups with significantly different OS (3.1 to 43 months). mRPA was better than RPA and GPA to predict prognosis in our population. Conclusions: OS and IC-PFS in patients with NSCLC and CNS metastases were better than previously reported. However, prognosis is widely variable and is directed mainly by performance status and the presence of a driver mutation. We propose a new prognostic model that my help to guide the treatment of these patients in the future. This model needs external validation.
Prognostic Model Using a Simple Survival Tree Algorithm for Patients Undergoing Palliative Radiation
