ArticleScientific reports2021
Predictive model for the 5-year survival status of osteosarcoma patients based on the SEER database and XGBoost algorithm.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Machine learning-based risk assessment of neonatal perinatal adverse outcomes of anemia during pregnancy: a modeling study.BMC medical informatics and decision making · 2026Article
- The Dysfunctional Self-Focus Attributes Scale-7 (DSAS-7): A Machine Learning-based Development of a Shortened Version of the DSAS.Journal of medical systems · 2026Article
- Machine learning algorithms and web-based prognostic tool for different histological subtypes of osteosarcoma: a retrospective cohort.Annals of medicine and surgery (2012) · 2026Article
- Improving outcomes in paediatric high-grade osteosarcoma: Insights from a retrospective survival study.Journal of clinical orthopaedics and trauma · 2025Article
- Radiomics and deep learning model based on X-ray imaging for the assisted diagnosis of early Legg-Calvé-Perthes disease.BMC musculoskeletal disorders · 2025Article
- Identification of relevant features using SEQENS to improve supervised machine learning models predicting AML treatment outcome.BMC medical informatics and decision making · 2025Article
- Applications of Machine Learning in Food Safety and HACCP Monitoring of Animal-Source Foods.Foods (Basel, Switzerland) · 2025Review
- A hybrid machine learning approach for the personalized prognostication of aggressive skin cancers.NPJ digital medicine · 2025Article
- Prognostic nomogram for overall survival in pediatric osteosarcoma with pulmonary metastases: a SEER database analysis.Frontiers in pediatrics · 2025Article
- The Role of mRNA Modifications in Bone Diseases.International journal of biological sciences · 2025Review
- Retrospective Analysis of Radiofrequency Ablation in Patients with Small Solitary Hepatocellular Carcinoma: Survival Outcomes and Development of a Machine Learning Prognostic Model.Current medical science · 2024Article
- Survival and analysis of prognostic factors for fibroblastic osteosarcoma patients: a population-based study.Translational cancer research · 2024Article
- Evaluating the prognostic value of tumor deposits in non-metastatic lymph node-positive colon adenocarcinoma using Cox regression and machine learning.International journal of colorectal disease · 2024Article
- A comparative analysis of deep learning-based location-adaptive threshold method software against other commercially available software.The international journal of cardiovascular imaging · 2024Article
- Development of a machine learning-based model to predict prognosis of alpha-fetoprotein-positive hepatocellular carcinoma.Journal of translational medicine · 2024Article
- Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma.Musculoskeletal surgery · 2024Article
- The role of Clec11a in bone construction and remodeling.Frontiers in endocrinology · 2024Review
- Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort.Biomolecules & biomedicine · 2023Article
- Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.JCO clinical cancer informatics · 2023Article
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Authors and funding
6 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents. Survival rate prediction is important for improving prognosis and planning therapy. However, there is still no prediction model with a high accuracy rate for osteosarcoma. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. The study population was 835 and was divided into the training set (n = 668) and validation set (n = 167). Characteristics selected via survival analyses were used to construct the model. Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve [AUC] = 0.977) and the validation set (AUC = 0.911). Decision curve analyses proved the model could be used to support clinical decisions. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Our prediction model had excellent accuracy and is therefore useful in clinical settings.
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