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Optimization of predictors of Ewing sarcoma cause-specific survival: a population study
1New York Cyberknife Center, 40-20 Main Street, 4th floor, Flushing, NY 11354, USA
Asian Pacific Journal of Cancer Prevention : APJCP
|June 18, 2014
Summary
This study optimized Ewing sarcoma (ES) survival prediction models using SEER data. SEER staging proved most effective, aiding clinical trial stratification.
Area of Science:
- Oncology
- Biostatistics
- Epidemiology
Background:
- Utilized Surveillance, Epidemiology, and End Results (SEER) database for Ewing sarcoma (ES) outcome analysis.
- Investigated survival disparities and prediction models for ES.
Purpose of the Study:
- To identify and optimize Ewing sarcoma (ES)-specific survival prediction models.
- To analyze factors contributing to survival disparities in ES patients.
Main Methods:
- Analyzed socio-economic, staging, and treatment factors from the SEER database for 1844 ES patients (1973-2009).
- Employed Generalized Linear Models and Receiver Operating Characteristic (ROC) curve analysis to predict ES-specific death.
- Developed parsimonious models by combining similar risk strata.
Main Results:
- SEER staging demonstrated the highest predictive accuracy with an ROC area of 0.616 (±0.032).
- A simplified 3-tiered staging model (non-metastatic, metastatic, un-staged) achieved an ROC area of 0.612 (±0.008).
- Biologic factors were predictive of survival, while socio-economic factors were not significant in this analysis.
Conclusions:
- ROC analysis effectively measured and optimized ES survival prediction models.
- Optimized models enhance patient stratification for clinical trials, improving research efficiency.
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