Background: Malignant pleural mesothelioma (MPM) is a rare and aggressive malignancy with a dismal prognosis. We aimed to identify predictors of survival among male and female MPM patients in the United States.
Methods: We identified MPM cases reported by 18 cancer registries in the Surveillance, Epidemiology, and End Results Program (2000-2017). We applied a random survival forest (RSF) algorithm to identify and rank the importance of 10 variables at patient, cancer, and area level in predicting all-cause survival overall and by female and male subgroups.
Results: Approximately 91.4% (n = 11,160) of the MPM patients had died, with better survival among females than males (11.7% vs 7.8%). The median follow-up time was 7 months (interquartile range, 2-17 months). A majority of the patients were male (78.6%), non-Hispanic White (81.8%), and residing in metropolitan counties with a population greater than 1 million (63.7%). The top 3 factors for predicting overall MPM survival were age, histological type, and cancer-directed surgery status. Except for age, the relative ranking of covariates varied by the 3 sample groups. Stage ranked fifth in predicting female survival, while it was replaced by metastasis status for male and overall patients. Race/ethnicity was not a good predictor for survival among MPM patients overall or the male subgroup, but ranked sixth for predicting survival among females. Median household income was not a good predictor for survival among females.
Conclusion: We demonstrated that RSF successfully identified predictors of MPM survival. RSF is a viable complement to the commonly used Cox proportional hazard model and a viable alternative, particularly when the proportional hazard assumption is unmet. RSF also identified differences between the sexes, which may help explain the sex differences in MPM survival rates.
Keywords: Surveillance, Epidemiology, and End Results (SEER) Program; machine learning; mesothelioma; survival; variable importance.