ArticleFrontiers in oncology2022
Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients.
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 38 papers, 1 of them a synthesis that pooled it.
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38 citing papers in PubMed, 1 synthesis or guideline pooled it, 49 citations in OpenAlex.
- Prevalence and risk factors of distant metastasis among Chinese osteosarcoma patients: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2026Pooled it
- Identification of a mitochondrial biomarker signature linking neuroinflammation to neuronal dysfunction in spinal cord injury.Scientific reports · 2026Article
- Comparative Molecular Insights and Computational Modeling of Multiple Myeloma and Osteosarcoma.International journal of molecular sciences · 2026Review
- ELANE inhibits the progression of osteosarcoma via suppressing the CXCL12/CXCR4 axis.Molecular and cellular biochemistry · 2026Article
- Machine learning-based integration of tumor deposit molecular signatures improves prognostic stratification in colon adenocarcinoma.International journal of colorectal disease · 2026Article
- Predicting postoperative complications after pneumonectomy using machine learning: a 10-year study.Annals of medicine · 2025Article
- Machine learning prediction of early reoperation following lower extremity tumor resection and endoprosthetic reconstruction: A PARITY trial secondary analysis.Journal of orthopaedic surgery and research · 2025Article
- Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.Scientific reports · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning.Drug design, development and therapy · 2025Article
- Research on the application of a multi-model cascaded deep learning framework in the pathological diagnosis of osteosarcoma.Oncology reviews · 2025Article
- Diagnostic artificial intelligence model predicts lymph node status in non-small cell lung cancer using simplified examination.Journal of thoracic disease · 2024Article
- Machine learning‑based radiomics models accurately predict Crohn's disease‑related anorectal cancer.Oncology letters · 2024Article
- Identification of copper death-associated molecular clusters and immunological profiles for lumbar disc herniation based on the machine learning.Scientific reports · 2024Article
- Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.BMC women's health · 2024Article
- Integrative gene expression analysis and animal model reveal immune- and autophagy-related biomarkers in osteomyelitis.Immunity, inflammation and disease · 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
- Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19.Scientific reports · 2024Article
- Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.PloS one · 2024Article
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Authors and funding
14 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms. Methods: A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals. Results: Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals. Conclusions: T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.
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