Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma
1 Epidemiology and Clinical Research Center for Children’s Cancer, National Center for Child Health and Development, 2-10-1 Okura, Setagaya-ku, Tokyo 157-8535, Japan
2 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, 14:654 doi:10.1186/1471-2407-14-654Published: 5 September 2014
The purpose of this study was to develop a prognostic model for the survival of pediatric patients with rhabdomyosarcoma (RMS) using parameters that are measured during routine clinical management.
Demographic and clinical variables were evaluated in 1679 pediatric patients with RMS registered in the Surveillance, Epidemiology, and End Results (SEER) program from 1990 to 2010. A multivariate Cox proportional hazards model was developed to predict median, 5-year and 10-year overall survival (OS). The Akaike information criterion technique was used for model selection. A nomogram was constructed using the reduced model after model selection, and was internally validated.
Of the total 1679 patients, 543 died. The 5-year OS rate was 64.5% (95% confidence interval (CI), 62.1-67.1%) and the 10-year OS was 61.8% (95%CI, 59.2-64.5%) for the entire cohort. Multivariate analysis identified age at diagnosis, tumor size, histological type, tumor stage, surgery and radiotherapy as significantly associated with survival (p < 0.05). The bootstrap-corrected c-index for the model was 0.74. The calibration curve suggested that the model was well calibrated for all predictions.
This study provided an objective analysis of all currently available data for pediatric RMS from the SEER cancer registry. A nomogram based on parameters that are measured on a routine basis was developed. The nomogram can be used to predict 5- and 10-year OS with reasonable accuracy. This information will be useful for estimating prognosis and in guiding treatment selection.