Erin E Karski1, Elizabeth McIlvaine2, Mark R Segal3, Mark Krailo2, Holcombe E Grier4, Linda Granowetter5, Richard B Womer6, Paul A Meyers7, Judy Felgenhauer8, Neyssa Marina9, Steven G DuBois1. 1. Department of Pediatrics, University of California, San Francisco School of Medicine, San Francisco, California. 2. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California. 3. Department of Epidemiology and Biostatistics, University of California, San Francisco, California. 4. Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, Massachusetts. 5. Department of Pediatrics, New York University Langone Medical Center, New York City, New York. 6. Division of Oncology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania. 7. Department of Pediatrics, Memorial Sloan-Kettering Cancer Center, New York City, New York. 8. Department of Pediatrics, Providence Sacred Heart Medical Center and Children's Hospital, Spokane, Washington. 9. Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California.
Abstract
BACKGROUND: Although multiple prognostic variables have been proposed for Ewing sarcoma (EWS), little work has been done to further categorize these variables into prognostic groups for risk classification. PROCEDURE: We derived initial prognostic groups from 2,124 patients with EWS in the SEER database. We constructed a multivariable recursive partitioning model of overall survival using the following covariates: age; stage; race/ethnicity; sex; axial primary; pelvic primary; and bone or soft tissue primary. Based on this model, we identified risk groups and estimated 5-year overall survival for each group using Kaplan-Meier methods. We then applied these groups to 1,680 patients enrolled on COG clinical trials. RESULTS: A multivariable model identified five prognostic groups with significantly different overall survival: (i) localized, age <18 years, non-pelvic primary; (ii) localized, age <18, pelvic primary or localized, age ≥18, white, non-Hispanic; (iii) localized, age ≥18, all races/ethnicities other than white, non-Hispanic; (iv) metastatic, age <18; and (v) metastatic, age ≥18. These five groups were applied to the COG dataset and showed significantly different overall and event-free survival based upon this classification system (P < 0.0001). A sub-analysis of COG patients treated with ifosfamide and etoposide as a component of therapy evaluated these findings in patients receiving contemporary therapy. CONCLUSIONS: Recursive partitioning analysis yields discrete prognostic groups in EWS that provide valuable information for patients and clinicians in determining an individual patient's risk of death. These groups may enable future clinical trials to adjust EWS treatment according to individualized risk.
BACKGROUND: Although multiple prognostic variables have been proposed for Ewing sarcoma (EWS), little work has been done to further categorize these variables into prognostic groups for risk classification. PROCEDURE: We derived initial prognostic groups from 2,124 patients with EWS in the SEER database. We constructed a multivariable recursive partitioning model of overall survival using the following covariates: age; stage; race/ethnicity; sex; axial primary; pelvic primary; and bone or soft tissue primary. Based on this model, we identified risk groups and estimated 5-year overall survival for each group using Kaplan-Meier methods. We then applied these groups to 1,680 patients enrolled on COG clinical trials. RESULTS: A multivariable model identified five prognostic groups with significantly different overall survival: (i) localized, age <18 years, non-pelvic primary; (ii) localized, age <18, pelvic primary or localized, age ≥18, white, non-Hispanic; (iii) localized, age ≥18, all races/ethnicities other than white, non-Hispanic; (iv) metastatic, age <18; and (v) metastatic, age ≥18. These five groups were applied to the COG dataset and showed significantly different overall and event-free survival based upon this classification system (P < 0.0001). A sub-analysis of COGpatients treated with ifosfamide and etoposide as a component of therapy evaluated these findings in patients receiving contemporary therapy. CONCLUSIONS: Recursive partitioning analysis yields discrete prognostic groups in EWS that provide valuable information for patients and clinicians in determining an individual patient's risk of death. These groups may enable future clinical trials to adjust EWS treatment according to individualized risk.
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