Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma

Area of Science:

• Pediatric Oncology
• Cancer Prognostics
• Biostatistics

Background:

• Rhabdomyosarcoma (RMS) is a significant pediatric cancer.
• Accurate survival prediction is crucial for treatment planning in pediatric RMS.
• Existing prognostic models may not fully utilize routinely collected clinical data.

Purpose of the Study:

• To develop a prognostic model for predicting overall survival (OS) in pediatric rhabdomyosarcoma (RMS) patients.
• To identify key clinical parameters measurable during routine management that influence RMS patient survival.
• To create a user-friendly tool (nomogram) for estimating survival probabilities.

Main Methods:

• Utilized data from 1679 pediatric RMS patients from the Surveillance, Epidemiology, and End Results (SEER) program (1990-2010).
• Developed a multivariate Cox proportional hazards model to predict 5- and 10-year OS.
• Employed Akaike information criterion for model selection and internal validation via bootstrap-corrected c-index and calibration curves.

Main Results:

• Identified age at diagnosis, tumor size, histological type, tumor stage, surgery, and radiotherapy as significant prognostic factors (p < 0.05).
• The developed model demonstrated good predictive accuracy with a c-index of 0.74.
• The nomogram showed good calibration, indicating reliable survival predictions.

Conclusions:

• A validated prognostic nomogram for pediatric RMS has been developed using routine clinical parameters.
• This tool offers an objective method for estimating 5- and 10-year OS in pediatric RMS patients.
• The nomogram can assist clinicians in prognosis estimation and treatment selection for better patient management.