Discov Oncol. 2025 Aug 20;16(1):1586. doi: 10.1007/s12672-025-03359-5.
ABSTRACT
OBJECTIVE: To analyze risk factors for pediatric osteosarcoma and to construct and evaluate a risk prediction model for pediatric osteosarcoma.
METHODS: We retrospectively analyzed data from patients diagnosed with osteosarcoma between 2000 and 2013 (n = 129) from the TARGET database. First, independent prognostic factors associated with osteosarcoma-specific death were identified through Cox proportional hazards modeling. Subsequently, using these independent prognostic factors, a nomogram model for osteosarcoma-specific survival was constructed using SPSS 25.0 and R 4.1.1. The discrimination of the model was evaluated using the C-index, predictive ability was validated through receiver operating characteristic curves and area under the curve values, calibration was assessed using calibration curves, and clinical utility was measured by decision curve analysis. Additionally, Kaplan-Meier survival analysis was performed to test the rationality of nomogram grouping.
RESULTS: The final model included six variables: sex, race, tumor-specific side, tumor-specific region, site of first recurrence, and time of first recurrence. The C-indices of the model for predicting 3-year and 5-year survival rates were 0.802 (95% CI: 0.725-0.880) and 0.787 (95% CI: 0.710-0.864), respectively, indicating good discriminatory ability. Calibration curves showed high consistency between predicted and actual survival probabilities. Decision curve analysis indicated that the model has substantial net benefit across a wide range of mortality risk thresholds. Kaplan-Meier survival analysis showed significant differences in prognosis between high-risk and low-risk groups. The nomogram model constructed in this study can accurately predict 1-year, 3-year, and 5-year survival of pediatric osteosarcoma patients and has high clinical utility.
CONCLUSION: This model not only provides an effective survival prediction tool for patients but also offers important references for optimizing treatment strategies for pediatric osteosarcoma, with the aim of improving survival rates and quality of life for osteosarcoma patients.
PMID:40833720 | DOI:10.1007/s12672-025-03359-5
