Introduction: Malignant pleural mesothelioma (MPM) is a rare cancer with a heterogeneous prognosis. Prognostic models are not widely utilized clinically. Classification and regression tree (CART) analysis examines the interaction of multiple variables with a given outcome.
Methods: Between 2005 and 2014, all cases with pathologically confirmed MPM had routinely available histological, clinical, and laboratory characteristics recorded. Classification and regression tree analysis was performed using 29 variables with 18-month survival as the dependent variable. Risk groups were refined according to survival and clinical characteristics. The model was then tested on an external international cohort.
Results: A total of 482 cases were included in the derivation cohort; the median survival was 12.6 months, and the median age was 69 years. The model defined four risk groups with clear survival differences (p < 0.0001). The strongest predictive variable was the presence of weight loss. The group with the best survival at 18 months (86.7% alive, median survival 34.0 months, termed risk group 1) had no weight loss, a hemoglobin level greater than 153 g/L, and a serum albumin level greater than 43 g/L. The group with the worst survival (0% alive, median survival 7.5 months, termed risk group 4d) had weight loss, a performance score of 0 or 1, and sarcomatoid histological characteristics. The C-statistic for the model was 0.761, and the sensitivity was 94.5%. Validation on 174 external cases confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic 0.68).
Conclusions: We have developed and validated a simple, clinically relevant model to reliably discriminate patients at high and lower risk of death using routinely available variables from the time of diagnosis in unselected populations of patients with MPM.
Keywords: Decision trees; Mesothelioma; Prognosis; Survival.
Copyright © 2016 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.