ArticleFrontiers in medicine2022
Development of a Machine Learning-Based Predictive Model for Lung Metastasis in Patients With Ewing Sarcoma.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed.
- Multimodality treatment and survival in primary cardiac malignancies: evidence from competing-risk and machine learning analyses.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- A machine learning-based predictive model for lymph node metastasis in elderly patients with early gastric cancer and prognosis.Updates in surgery · 2026Article
- Persistent racial and socioeconomic inequities in mycosis fungoides survival: a population-based study.Frontiers in public health · 2026Article
- Interpretable multiclassification machine learning models in predicting the extent of small cell lung cancer metastasis.Scientific reports · 2025Article
- Ewing's sarcoma family of tumors in adolescents in Beijing 2010-2019: a multicenter retrospective cohort study.Translational pediatrics · 2025Article
- Incidence, risk factors, and prognostic impact of regional lymph node metastasis in bone sarcoma: a population-based cohort study.Japanese journal of clinical oncology · 2025Article
- Using machine learning algorithms to predict risk factors of heart failure after complete mesocolic excision in colorectal cancer patients.Scientific reports · 2025Article
- Research progress of artificial intelligence and machine learning in pulmonary embolism.Frontiers in medicine · 2025Review
- Knowledge map of artificial intelligence in neurodegenerative diseases: a decade-long bibliometric and visualization study.Frontiers in aging neuroscience · 2025Article
- 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
- Using machine learning techniques to predict the risk of osteoporosis based on nationwide chronic disease data.Scientific reports · 2024Article
- Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.PloS one · 2024Article
- Predicting the occurrence of stress urinary incontinence after prolapse surgery: a machine learning-based model.Annals of translational medicine · 2023Article
- Comparison of the effectiveness of different machine learning algorithms in predicting new fractures after PKP for osteoporotic vertebral compression fractures.Journal of orthopaedic surgery and research · 2023Article
- 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
- Identification of Factors Driving Doxorubicin-Resistant Ewing Tumor Cells to Survival.Cancers · 2022Article
- Using Machine Learning Methods to Predict Bone Metastases in Breast Infiltrating Ductal Carcinoma Patients.Frontiers in public health · 2022Article
- Stmol: A component for building interactive molecular visualizations within streamlit web-applications.Frontiers in molecular biosciences · 2022Article
- Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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12 authors.
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Abstract
Background: This study aimed to develop and validate machine learning (ML)-based prediction models for lung metastasis (LM) in patients with Ewing sarcoma (ES), and to deploy the best model as an open access web tool. Methods: We retrospectively analyzed data from the Surveillance Epidemiology and End Results (SEER) Database from 2010 to 2016 and from four medical institutions to develop and validate predictive models for LM in patients with ES. Patient data from the SEER database was used as the training group ( Results: The study cohort consisted of 929 patients from the SEER database and 51 patients from multiple medical centers, a total of 980 ES patients. Of these, 175 (18.8%) had lung metastasis. Multivariate logistic regression analysis was performed with survival time, T-stage, N-stage, surgery, and bone metastasis providing the independent predictive factors of LM. The AUC value of six predictive models ranged from 0.585 to 0.705. The Random Forest (RF) model ( Conclusions: Machine learning were found to have utility for predicting LM in patients with Ewing sarcoma, and the RF model gave the best performance. The accessibility of the predictive model as a web-based tool offers clear opportunities for improving the personalized treatment of patients with ES.
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