Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data
Abstract Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patients with glioblastoma and establish predi...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-15553-0 |
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| author | Denglin Li Luxin Zhang Lifei Xu Renhe Zhai Hanyu Gao Junlan Gao Minghai Wei Ningwei Che Yeting He |
| author_facet | Denglin Li Luxin Zhang Lifei Xu Renhe Zhai Hanyu Gao Junlan Gao Minghai Wei Ningwei Che Yeting He |
| author_sort | Denglin Li |
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| description | Abstract Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patients with glioblastoma and establish predictive models for the OS and CSS of Asian patients with glioblastoma based on the machine learning algorithms. Data from Asian patients with glioblastoma in the SEER database were retrieved and stochastically grouped into a training set (n = 845) and a validation set (n = 362), and patients in our centre were assigned to the test set (n = 172). Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic factors. Predictive models for OS and CSS were established based on eight machine learning algorithms, including Lasso Cox, random survival forest, CoxBoost, generalized boosted regression modelling (GBM), stepwise Cox and survival support vector machine, eXtreme Gradient Boosting, supervised principal component and partial least squares regression for Cox, and the selected predictive models were evaluated by the area under the ROC curves (AUC) and 95% confidence interval (CI), calibration curves and decision curve analyses in the training set, validation set and test set. In our retrospective study, age, tumour history, histologic type, surgery and chemotherapy were confirmed to be predictors of OS (p < 0.05); age, tumour history, histologic type, surgery and chemotherapy were identified as independent factors for CSS (p < 0.05). The predictive model for OS based on the GBM algorithm exhibited excellent predictive performance at 6 months (AUC = 0.837, 95% CI: 0.803–0.870), 12 months (AUC = 0.809, 95% CI: 0.780–0.839) and 24 months (AUC = 0.750, 95% CI: 0.717–0.783) in the training set, and the powerful predictive performance of the GBM model was confirmed in the validation and test sets, with good concordance between the predicted and observed OS rates demonstrated by calibration curves and clinical decision making performance suggested by the decision curve analyses curves. The predictive model based on the GBM algorithm for CSS also performed best = in the training set at 6 months (AUC = 0.808, 95% CI: 0.770–0.847), 12 months (AUC = 0.755, 95% CI: 0.721–0.789) and 24 months (AUC = 0.692, 95% CI: 0.657–0.728) in the training set, and convincing predictive effectiveness was also confirmed in the validation and test sets with good calibration and clinical utility. Age, tumour history, histologic type, surgery and chemotherapy were confirmed to be independent factors for OS; and age, tumour history, histologic type, surgery and chemotherapy were identified as prognostic factors for CSS in our retrospective study. The predictive model constructed for OS and CSS based on the GBM algorithm in Asian patients with glioblastoma can be used to accurately predict OS and CSS in clinical practice, which may help tailor personalized treatment regimens and provide significant benefits for these patients. |
| format | Article |
| id | doaj-art-01d4aab6e71a478ea4d5005affef3f18 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
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| spelling | doaj-art-01d4aab6e71a478ea4d5005affef3f182025-08-24T11:29:13ZengNature PortfolioScientific Reports2045-23222025-08-0115111910.1038/s41598-025-15553-0Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese dataDenglin Li0Luxin Zhang1Lifei Xu2Renhe Zhai3Hanyu Gao4Junlan Gao5Minghai Wei6Ningwei Che7Yeting He8Department of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Urology, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Emergency, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityDepartment of Neurosurgery, Second Affiliated Hospital of Dalian Medical UniversityAbstract Glioblastoma is an aggressive, malignant primary brain tumour and the most prevalent histological type of glioma. Our study attempted to investigate the independent predictors of overall survival (OS) and cancer-specific survival (CSS) in Asian patients with glioblastoma and establish predictive models for the OS and CSS of Asian patients with glioblastoma based on the machine learning algorithms. Data from Asian patients with glioblastoma in the SEER database were retrieved and stochastically grouped into a training set (n = 845) and a validation set (n = 362), and patients in our centre were assigned to the test set (n = 172). Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic factors. Predictive models for OS and CSS were established based on eight machine learning algorithms, including Lasso Cox, random survival forest, CoxBoost, generalized boosted regression modelling (GBM), stepwise Cox and survival support vector machine, eXtreme Gradient Boosting, supervised principal component and partial least squares regression for Cox, and the selected predictive models were evaluated by the area under the ROC curves (AUC) and 95% confidence interval (CI), calibration curves and decision curve analyses in the training set, validation set and test set. In our retrospective study, age, tumour history, histologic type, surgery and chemotherapy were confirmed to be predictors of OS (p < 0.05); age, tumour history, histologic type, surgery and chemotherapy were identified as independent factors for CSS (p < 0.05). The predictive model for OS based on the GBM algorithm exhibited excellent predictive performance at 6 months (AUC = 0.837, 95% CI: 0.803–0.870), 12 months (AUC = 0.809, 95% CI: 0.780–0.839) and 24 months (AUC = 0.750, 95% CI: 0.717–0.783) in the training set, and the powerful predictive performance of the GBM model was confirmed in the validation and test sets, with good concordance between the predicted and observed OS rates demonstrated by calibration curves and clinical decision making performance suggested by the decision curve analyses curves. The predictive model based on the GBM algorithm for CSS also performed best = in the training set at 6 months (AUC = 0.808, 95% CI: 0.770–0.847), 12 months (AUC = 0.755, 95% CI: 0.721–0.789) and 24 months (AUC = 0.692, 95% CI: 0.657–0.728) in the training set, and convincing predictive effectiveness was also confirmed in the validation and test sets with good calibration and clinical utility. Age, tumour history, histologic type, surgery and chemotherapy were confirmed to be independent factors for OS; and age, tumour history, histologic type, surgery and chemotherapy were identified as prognostic factors for CSS in our retrospective study. The predictive model constructed for OS and CSS based on the GBM algorithm in Asian patients with glioblastoma can be used to accurately predict OS and CSS in clinical practice, which may help tailor personalized treatment regimens and provide significant benefits for these patients.https://doi.org/10.1038/s41598-025-15553-0GlioblastomaSurvivalRisk factorMachine learningPredictive model |
| spellingShingle | Denglin Li Luxin Zhang Lifei Xu Renhe Zhai Hanyu Gao Junlan Gao Minghai Wei Ningwei Che Yeting He Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data Scientific Reports Glioblastoma Survival Risk factor Machine learning Predictive model |
| title | Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data |
| title_full | Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data |
| title_fullStr | Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data |
| title_full_unstemmed | Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data |
| title_short | Development and validation of a machine learning-based survival prediction model for Asian glioblastoma patients using the SEER database and Chinese data |
| title_sort | development and validation of a machine learning based survival prediction model for asian glioblastoma patients using the seer database and chinese data |
| topic | Glioblastoma Survival Risk factor Machine learning Predictive model |
| url | https://doi.org/10.1038/s41598-025-15553-0 |
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