ArticleJournal of cancer research and clinical oncology2023
Establishment and validation of a competitive risk model for predicting cancer-specific survival in patients with osteosarcoma: a population-based study.
Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Epidemiology and Prognosis of Patients With Osteosarcoma at Different Primary Sites: A SEER Population-Based Study.Cancer reports (Hoboken, N.J.) · 2026Article
- Development and verification of a competing risk model for forecasting cancer-specific survival in malignant bone tumor patients: an analysis of SEER database.Translational cancer research · 2026Article
- Development and validation of the competing risk nomogram and risk classification system for predicting cancer-specific mortality in patients with cervical adenosquamous carcinoma treated via radical hysterectomy.Biomolecules & biomedicine · 2025Article
- UBE2L3 Suppresses Oxidative Stress-regulated Necroptosis to Accelerate Osteosarcoma Progression.Recent patents on anti-cancer drug discovery · 2025Article
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3 authors.
Funding
Abstract
backgroundOsteosarcoma is the most common primary bone tumor with a poor prognosis. The aim of this study was to establish a competitive risk model nomogram to predict cancer-specific survival in patients with osteosarcoma.
methodsPatient data was obtained from the Surveillance, Epidemiology, and End Results database in the United States. A sub-distribution proportional hazards model was used to analyze independent risk factors affecting cancer-specific mortality (CSM) in osteosarcoma patients. Based on these risk factors, a competitive risk model was constructed to predict 1-year, 3-year, and 5-year cancer-specific survival (CSS) in osteosarcoma patients. The reliability and accuracy of the nomogram were evaluated using the concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and calibration curves.
resultsA total of 2900 osteosarcoma patients were included. The analysis showed that age, primary tumor site, M stage, surgery, chemotherapy, and median household income were independent risk factors influencing CSM in patients. The competitive risk model was constructed to predict CSS in osteosarcoma patients. In the training and validation sets, the C-index of the model was 0.756 (95% CI 0.725-0.787) and 0.737 (95% CI 0.717-0.757), respectively, and the AUC was greater than 0.7 for both. The calibration curves also demonstrated a high consistency between the predicted survival rates and the actual survival rates, confirming the accuracy and reliability of the model.
conclusionWe established a competitive risk model to predict 1-year, 3-year, and 5-year CSS in osteosarcoma patients. The model demonstrated good predictive performance and can assist clinicians and patients in making clinical decisions and formulating follow-up strategies.
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