ArticleFrontiers in oncology2022
Dynamic Predictive Models With Visualized Machine Learning for Assessing Chondrosarcoma Overall Survival.
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 6 papers, 1 of them a synthesis that pooled it.
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6 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
- Development and validation of a multiparametric MRI-based radiomics nomogram for the tripartite discrimination of primary benign, primary malignant, and metastatic lumbar spinal tumors.Frontiers in oncology · 2026Article
- Novel machine-learning prediction tools for overall survival of patients with chondrosarcoma: Based on recursive partitioning analysis.Cancer medicine · 2024Article
- Machine learning-based dynamic predictive models for prognosis and treatment decisions in patients with liver metastases from gastric cancer.American journal of cancer research · 2024Article
- Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study.Frontiers in cardiovascular medicine · 2023Article
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11 authors.
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Abstract
Chondrosarcoma is a malignant bone tumor with a low incidence rate. Accurate risk evaluation is crucial for chondrosarcoma treatment. Due to the limited reliability of existing predictive models, we intended to develop a credible predictor for clinical chondrosarcoma based on the Surveillance, Epidemiology, and End Results data and four Chinese medical institutes. Three algorithms (Best Subset Regression, Univariate and Cox regression, and Least Absolute Shrinkage and Selector Operator) were used for the joint training. A nomogram predictor including eight variables-age, sex, grade, T, N, M, surgery, and chemotherapy-is constructed. The predictor provides good performance in discrimination and calibration, with area under the curve ≥0.8 in the receiver operating characteristic curves of both internal and external validations. The predictor especially had very good clinical utility in terms of net benefit to patients at the 3- and 5-year points in both North America and China. A convenient web calculator based on the prediction model is available at https://drwenle029.shinyapps.io/CHSSapp, which is free and open to all clinicians.
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