ArticleFrontiers in oncology2022
Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review of 99 extremity bone malignancy survival prediction models.Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology · 2025Pooled it
- Epidemiology and Prognosis of Patients With Osteosarcoma at Different Primary Sites: A SEER Population-Based Study.Cancer reports (Hoboken, N.J.) · 2026Article
- Construction and validation of a multimodal MRI-based quantitative feature prediction model for the prognosis of non-metastatic primary osteosarcoma.Frontiers in oncology · 2026Article
- Review
- Whole-Exome Analysis and Osteosarcoma: A Game Still Open.International journal of molecular sciences · 2024Review
- Article
- A deep learning algorithm with good prediction efficacy for cancer-specific survival in osteosarcoma: A retrospective study.PloS one · 2023Article
- Identification of risk model based on glycolysis-related genes in the metastasis of osteosarcoma.Frontiers in endocrinology · 2022Article
- Association between aspartate aminotransferase to alanine aminotransferase ratio and the risk of diabetes in Chinese prediabetic population: A retrospective cohort study.Frontiers in public health · 2022Article
- DJ-1 promotes osteosarcoma progression through activating CDK4/RB/E2F1 signaling pathway.Frontiers in oncology · 2022Article
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14 authors.
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Abstract
Background: Currently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma. Methods: Clinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) Results: Eight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients. Conclusions: The clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.
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