Start Your Search
Poster Session (ID 8)
- Event: ACLC 2018
- Type: Poster Session
- Presentations: 1
- Coordinates: 11/07/2018, 00:00 - 00:00, Poster Hall
P051 - A Novel (ID 176)
00:00 - 00:00 | Author(s): L. Li
Leptomeningeal metastasis (LM) occurs in 3%-5% of patients with advanced non-small-cell lung cancer (NSCLC). However, its incidence has increased in patients with driver gene mutations owing to their prolonged survival from target therapies. The prognosis of NSCLC patients with LM has improved to 3-11months in the era of precision medicine. Understanding how prognosis varies across this heterogeneous patient population is essential to individualize care and design future clinical trials.
We retrospectively reviewed the medical records of 301 lung cancer patients, who were diagnosed as LM by brain MRI or cytology or next-generation sequencing of cerebrospinal fluid between January 2011 and April 2018. Their clinicopathological characteristics and prognostic data were analyzed. All prognostic factors were weighted for significance by hazard ratios (HR).
Among the 301 patients, the median age was 55 years (26-86). Most of them were adenocarcinoma (96.3%?290/301). There were 149 males and 152 females. The median overall survival of the 301 patients was 7.1 months from LM diagnosis. Multivariate analysis revealed that KPS score <60 (HR, 4.30, 3.15-5.87), presence of extracranial metastases (ECM) (HR, 1.79, 1.27-2.52), EGFR negative or unknown and ALK negative or unknown (HR, 1.83, 1.27-2.62) were the independent prognostic factors for poor overall survival. Based on the HR, we made a novel molGPA scoring criteria for LM modified from the molGPA for brain metastasis (BM) and developed a novel molGPA model stratified patients into three categories. The median overall survival for patients with the novel molGPA scores of 0, 0.5-1.0, and1.5-2.0 was 0.3, 4.2, and 12.8 months, respectively (p < 0.001). The C-index of this model was 0.69 (95%CI: 0.65-0.72).
We developed a novel molGPA model, which could help clinicians to stratify lung cancer patients with LM in the era of precision medicine. Further research is needed to validate and improve this model.