ArticleJournal of thoracic disease2021
Application of machine learning approaches to predict the 5-year survival status of patients with esophageal cancer.
Article in Journal of thoracic disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- AI in esophageal cancer: advances, barriers to clinical translation, and perspectives for digital health.Journal of translational medicine · 2026Review
- Identification of Prognostic Factors in Esophageal Cancer Using Machine Learning: A Retrospective Study Based on the SEER Database.Journal of clinical medicine · 2026Article
- Building an interpretable machine learning prognosis prediction model-based on baseline examinations of patients with esophageal cancer undergoing surgery.Frontiers in oncology · 2026Article
- Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations.The Journal of international medical research · 2026Article
- A novel framework for esophageal cancer grading: combining CT imaging, radiomics, reproducibility, and deep learning insights.BMC gastroenterology · 2025Article
- Use machine learning to predict pulmonary metastasis of esophageal cancer: a population-based study.Journal of cancer research and clinical oncology · 2024Article
- Prediction of hepatic metastasis in esophageal cancer based on machine learning.Scientific reports · 2024Article
- Screening ovarian cancer by using risk factors: machine learning assists.Biomedical engineering online · 2024Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
- Prediction of five-year survival among esophageal cancer patients using machine learning.Heliyon · 2023Article
- Biomarkers for Early Detection, Prognosis, and Therapeutics of Esophageal Cancers.International journal of molecular sciences · 2023Review
- Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit.PloS one · 2023Article
- Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review.Translational cancer research · 2022Review
- A Machine Learning Approach Using XGBoost Predicts Lung Metastasis in Patients with Ovarian Cancer.BioMed research international · 2022Article
- Development and validation of a machine learning model for survival risk stratification after esophageal cancer surgery.Frontiers in oncology · 2022Article
- Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.Frontiers in endocrinology · 2022Article
- Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.Computational intelligence and neuroscience · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate prognostic estimation for esophageal cancer (EC) patients plays an important role in the process of clinical decision-making. The objective of this study was to develop an effective model to predict the 5-year survival status of EC patients using machine learning (ML) algorithms.
methodsWe retrieved the information of patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) Program, including 24 features. A total of 8 ML models were applied to the selected dataset to classify the EC patients in terms of 5-year survival status, including 3 newly developed gradient boosting models (GBM), XGBoost, CatBoost, and LightGBM, 2 commonly used tree-based models, gradient boosting decision trees (GBDT) and random forest (RF), and 3 other ML models, artificial neural networks (ANN), naive Bayes (NB), and support vector machines (SVM). A 5-fold cross-validation was used in model performance measurement.
resultsAfter excluding records with missing data, the final study population comprised 10,588 patients. Feature selection was conducted based on the χ
conclusionsThe XGBoost model and the complete dataset can be used to construct an accurate prognostic model for patients diagnosed with EC which may be applicable in clinical practice in the future.
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