Statistical Validation of the EORTC Prognostic Model for Malignant Pleural Mesothelioma Based on Three Consecutive Phase II Trials
- 1 January 2005
- journal article
- research article
- Published by American Society of Clinical Oncology (ASCO) in Journal of Clinical Oncology
- Vol. 23 (1) , 184-189
- https://doi.org/10.1200/jco.2005.07.050
Abstract
Purpose Malignant pleural mesothelioma (MPM) carries a poor prognosis due to chemoresistance. The European Organisation for Research and Treatment of Cancer (EORTC) prognostic model was reported to predict survival in MPM. Our retrospective analysis set out to test the validity of the model as a prognostic tool in patients treated in three phase II trials at St Bartholomew’s Hospital (London, United Kingdom) between 1999 and 2003. Patients and Methods A total of 145 patients were treated in three phase II trials; vinorelbine (VIN; 70 patients), vinorelbine/oxaliplatin (VO; 26 patients), and irinotecan/cisplatin/mitomycin C (IPM; 49 patients). Two subgroups, high-risk and low-risk, were defined by EORTC prognostic score (EPS). EPS was determined by a five-parameter model incorporating age, sex, histology, probability of diagnosis, and leukocyte count. An EPS cutoff of less than 1.27 (low risk) or more than 1.27 (high risk) was used to stratify Kaplan-Meier survival curves. Each of the EPS variables exhibited either trends or significant stratification of overall survival (OS). Results Multivariate analysis confirmed leukocyte count, Eastern Cooperative Oncology Group performance status, and sarcomatous histology as independent prognostic variables. EPS stratified OS in both individual and pooled trial datasets. No association between objective tumor response and EPS classification was identified by multinomial logistic regression. EPS stratified progression-free survival for the VO and IPM cohorts, but not for VIN. Conclusion This study validates the EPS system as a robust tool for stratifying small trials into low- and high-risk subgroups. EPS should facilitate patient selection and analysis in randomized clinical trials.Keywords
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