ArticleAnnals of translational medicine2022
XGBoost algorithm and logistic regression to predict the postoperative 5-year outcome in patients with glioma.
Article in Annals of translational medicine, 2022. 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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17 citing papers in PubMed, 42 citations in OpenAlex.
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- Application of XGBoost and logistic regression in predicting 90 days mortality for elderly severe acute renal failure patients.Scientific reports · 2026Article
- Comparative study on predicting postoperative distant metastasis of lung cancer based on machine learning models.Scientific reports · 2026Article
- IDH mutation prediction in non-enhancing gliomas with relaxed T2-FLAIR mismatch and fractal dimension: a two-center study.BMC medical imaging · 2026Article
- Predicting hospital admissions, ICU utilization, and prolonged length of stay among febrile pediatric emergency department patients using incomplete and imbalanced electronic health record (EHR) data strategies.International journal of medical informatics · 2025Article
- Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism.BMC pediatrics · 2025Article
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- Interpretable prediction of hospital mortality in bleeding critically ill patients based on machine learning and SHAP.BMC medical informatics and decision making · 2025Article
- Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model.Scientific reports · 2025Article
- Predicting 30-Day Cardiotoxicity in Patients Receiving Immune Checkpoint Inhibitors: An Observational Study Utilizing XGBoost.Cardiovascular toxicology · 2025Observational
- Factors contributing to differences in physical activity levels in (pre)frail older adults living in rural areas of China.PloS one · 2025Article
- Decoding Wilson disease: a machine learning approach to predict neurological symptoms.Frontiers in neurology · 2024Article
- Machine learning-based prediction of sertraline concentration in patients with depression through therapeutic drug monitoring.Frontiers in pharmacology · 2024Article
- Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand.Acute and critical care · 2023Article
- Article
- Effect of standard nutritional support therapy based on nutritional risk screening on post-operative nutritional status and quality of life in patients with glioma.American journal of translational research · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Glioma is the most common primary intracranial tumor with poor prognosis. The prediction of glioma prognosis has not been well investigated. XGBoost algorithm has been widely used in and data analysis. The predictive value of XGBoost algorithm in glioma remains unclear. This current study used the XGBoost algorithm to construct a predictive model for postoperative outcomes of glioma patients. Methods: Patients with glioma who underwent surgery from January 2006 to April 2017 were retrospectively included in this study. Clinical and follow-up data were collected. The XGBoost model and multivariate logistic regression analysis model were used to screen the factors related to postoperative outcomes, and the results of the two models were compared. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and Youden index were calculated to evaluate the predictive value of the XGBoost model. Results: A total of 638 patients were included. In total, 336 (52.7%) cases died within 5 years after the operation. Multivariate logistic regression analysis showed that age, gender, World Health Organization (WHO) grade, extent of tumor resection, Karnofsy performance score (KPS), tumor diameter, and whether postoperative radiotherapy and chemotherapy were administered, were the most important risk factors for death within 5 years after surgery in glioma patients. The XGBoost model showed that the top 5 factors related to death of glioma patients within 5 years after surgery were WHO grade (30 points), extent of tumor resection (19 points), postoperative radiotherapy and chemotherapy (16 points), KPS (14 points), and age (11 points). The AUC of the XGBoost model for predicting the death of glioma patients within 5 years after surgery was 0.803 [95% confidence interval (CI): 0.718-0.832], and the sensitivity and specificity were 0.894 and 0.581, respectively. The Youden index was 0.475. The AUC of the multivariate logistic regression model was 0.738 (95% CI: 0.704-0.781), the sensitivity and specificity were 0.785 and 0.632, respectively, and the Youden index was 0.417. Conclusions: Compared with multivariate logistic regression model, XGBoost model has better performance in predicting the risk of death within 5 years after surgery in patients with glioma.
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