Systematic Immunological Level Determined the Prognosis of Leptomeningeal Metastasis in Lung Cancer

Abstract

Background and Purpose: Leptomeningeal metastases (LM) is the end-event of lung cancer and the prognosis is dismal. Few studies explored the prognostic performance of systematic immunological levels in lung cancer patients with LM. Our study aimed to explore the possible relationship between the prognosis of LM and systematic immunological level and other clinical characteristics. Methods: This retrospective, multi-institutional, observational study was conducted in 4 tertiary centers in China. Patients were screened from January 2009 to May 2019. Patients with radiographically or histologically confirmed LM were enrolled. The data of systematic immunological level and other clinical characteristics of each patient were extracted and statistically analyzed to establish a prognostic model based on statistical analysis results. The predictive accuracy and discriminative capability of the model were evaluated by the calibration curve and concordance index (C-index). Results: A total of 109 patients were enrolled in the study. Patients with adenocarcinoma, tumors harboring EGFR mutation, at their age of 50–59, with either bone, brain, or lung metastases, were enriched in this cohort. The median overall survival (OS) was 20.4 months (95% CI: 15.2–25.6). Cox univariate and multivariate analysis revealed better PS (0–1), no distant lymph nodes metastasis (DLNM), simultaneous diagnosis of lung cancer and leptomeningeal metastasis (SDLL), and lower neutrophil to lymphocyte ratio (NLR), were associated with better OS. Based on these independent prognostic variables, a prognostic nomogram model with a C-index for OS prediction of 0.71, was constructed. The actual probability of survival at 1-, 2-and 3-year showed good concordance with the prediction curve of our nomogram. Conclusion: The systematic immunological level was an independent prognostic factor of lung cancer patients with LM. The prognostic model based on statistical analysis had a good ability to predict the OS of patients.