Table of Contents
Lung Cancer International
Volume 2014 (2014), Article ID 731925, 10 pages
Research Article

Simple and Objective Prediction of Survival in Patients with Lung Cancer: Staging the Host Systemic Inflammatory Response

1Beatson Oncology Centre, 1053 Great Western Road, Glasgow G12 0YN, UK
2University of Aberdeen, Aberdeen, UK
3Inverclyde Royal Hospital, Inverclyde, UK
4Queen Margaret Hospital, Dunfermline, UK
5University of Glasgow, Glasgow, UK
6Glasgow Royal Infirmary, Glasgow, UK
7Aberdeen Royal Infirmary, Aberdeen, UK

Received 27 August 2013; Revised 20 December 2013; Accepted 24 December 2013; Published 5 March 2014

Academic Editor: Seiji Niho

Copyright © 2014 Derek Grose et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. Prediction of survival in patients diagnosed with lung cancer remains problematical. The aim of the present study was to examine the clinical utility of an established objective marker of the systemic inflammatory response, the Glasgow Prognostic Score, as the basis of risk stratification in patients with lung cancer. Methods. Between 2005 and 2008 all newly diagnosed lung cancer patients coming through the multidisciplinary meetings (MDTs) of four Scottish centres were included in the study. The details of 882 patients with a confirmed new diagnosis of any subtype or stage of lung cancer were collected prospectively. Results. The median survival was 5.6 months (IQR 4.8–6.5). Survival analysis was undertaken in three separate groups based on mGPS score. In the mGPS 0 group the most highly predictive factors were performance status, weight loss, stage of NSCLC, and palliative treatment offered. In the mGPS 1 group performance status, stage of NSCLC, and radical treatment offered were significant. In the mGPS 2 group only performance status and weight loss were statistically significant. Discussion. This present study confirms previous work supporting the use of mGPS in predicting cancer survival; however, it goes further by showing how it might be used to provide more objective risk stratification in patients diagnosed with lung cancer.