Derivation and validation of risk groups in patients with osteosarcoma utilizing regression tree analysis.

BACKGROUND For patients with osteosarcoma, apart from stage and primary site, we lack reliable prognostic factors for risk stratification at diagnosis. There is a need for further defined, discrete prognostic groups using presenting clinical features. METHODS We analyzed a cohort of 3069 patients less than 50 years of age, diagnosed with primary osteosarcoma of the bone between 1986 and 2013 from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly split into test and validation cohorts. Optimal cut points for age, tumor size, and grade were identified using classification and regression tree analysis. Manual recursive partitioning was used to identify discrete prognostic groups within the test cohort. These groups were applied to the validation cohort, and overall survival was analyzed using Cox models, Kaplan Meier methods, and log-rank tests. RESULTS After applying recursive partitioning to the test cohort, our initial model included six groups. Application of these groups to the validation cohort resulted in four final groups. Key risk factors included presence of metastases, tumor site, tumor grade, age, and tumor size. Patients with localized axial tumors were identified as having similar outcomes to patients with metastases. Age and tumor size were only prognostically important in patients with extremity tumors when assessed in the validation cohort. CONCLUSIONS This analysis supports prior reports that patients with axial tumors are a high-risk group, and demonstrates the importance of age and tumor size in patients with appendicular tumors. Biologic and genetic markers are needed to further define subgroups in osteosarcoma.