Prognostic models to predict overall and cause-specific survival for patients with middle ear cancer: a population-based analysis
- Equal contributors
1 Department of Otolaryngology, The Institute of Otolaryngology, Head and Neck Surgery, Chinese PLA General Hospital, Beijing, P.R. China
2 Department of Public Health, Juntendo University, Tokyo, Japan
3 Epidemiology and Clinical Research Center for Children's Cancer, National Center for Child Health and Development, Tokyo, Japan
4 Division of Allergy, Department of Medical Subspecialties, Medical Support Center for Japan Environment and Children's Study (JECS), National Center for Child Health and Development, 2-10-1 Okura, Setagaya-ku, Tokyo 157-8535, Japan
BMC Cancer 2014, 14:554 doi:10.1186/1471-2407-14-554Published: 1 August 2014
The purpose of this study was to evaluate the survival outcome for middle ear cancer and to construct prognostic models to provide patients and clinicians with more accurate estimates of individual survival probability.
Patients diagnosed with middle ear cancer between 1983 and 2011 were selected for the study from the Surveillance Epidemiology and End Results Program. We used the Kaplan-Meier product limit method to describe overall survival and cause-specific survival. Cox proportional hazards models were fitted to model the relationships between patient characteristics and prognosis. Nomograms for predicting overall survival and cause-specific survival were built using the Cox models established.
The entire cohort comprised 247 patients with malignant middle ear cancer. Median duration of follow-up until censoring or death was 25 months (range, 1–319 months). Five-year overall survival and cause-specific survival were 47.4% (95% Confidence Interval (CI), 41.2% to 54.6%) and 58.0% (95% CI, 51.6% to 65.3%), respectively. In multivariable analysis, age, histological subtype, stage, surgery and radiotherapy were predictive of survival. The bootstrap corrected c-index for model predicting overall and cause-specific survival was 0.73 and 0.74, respectively. Calibration plots showed that the predicted survival reasonably approximated observed outcomes.
The models represent an objective analysis of all currently available data. The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival. Nomograms should thus be considered as a useful tool for predicting clinical prognosis.