ArticleScientific reports2024
Survival prediction of glioblastoma patients using modern deep learning and machine learning techniques.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Melatonin exerts anOncology letters · 2026Article
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- GCE: A Framework for Interpretable Nonlinear Hazard Modeling in Cardiac Sarcoma Survival Using SEER Data.Bioengineering (Basel, Switzerland) · 2026Article
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- EDRNet-based adolescent idiopathic scoliosis screening method using bare-back images.BMC medical informatics and decision making · 2026Article
- Survival Outcomes and Machine Learning-Based Prediction of 12-Month Mortality in Glioblastoma Before and During the COVID-19 Pandemic: A SEER Population-Based Study.Medicina (Kaunas, Lithuania) · 2026Article
- Glioblastoma region-specific metabolic signatures reflect patient survival duration.Journal of neuro-oncology · 2026Article
- Machine Learning-Based Classification of Gliomas and Tumor Grades with SHAP-Guided Feature Interpretation.Genes · 2026Article
- Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2026Article
- Integrative machine learning reveals hidden and emerging co-regulatory gene networks for multi-phase glioblastoma outcome prediction.European journal of cancer (Oxford, England : 1990) · 2026Article
- Hybrid 2D/3D CNN and radiomics model for brain tumor classification using EfficientNetb0 and ResNet-18.BMC medical imaging · 2026Article
- Machine-Learning-Based Prediction of Clinical Outcomes in Gliomas Using Glycomic Features.Computational and structural biotechnology journal · 2026Article
- Construction and external validation of a prognostic model for high-grade glioma based on IDH1 mutation status: implications for concurrent chemoradiotherapy efficacy.American journal of cancer research · 2026Article
- Radiomics-based quantification of tumor infiltration in the non-enhancing peritumoral region on postoperative MRI is associated with survival in glioblastoma.Scientific reports · 2025Article
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- Prediction of ectopic pregnancy using interpretable machine learning algorithms.BMC pregnancy and childbirth · 2025Article
- Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.Journal of neuro-oncology · 2025Article
- Melatonin Synergises the Chemotherapeutic Effect of Temozolomide in Glioblastoma by Suppressing NF-κB/COX-2 Signalling Pathways.Journal of cellular and molecular medicine · 2025Article
- Artificial Intelligence in Glioblastoma-Transforming Diagnosis and Treatment.Chinese neurosurgical journal · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we utilized data from the Surveillance, Epidemiology, and End Results (SEER) database to predict the glioblastoma patients' survival outcomes. To assess dataset skewness and detect feature importance, we applied Pearson's second coefficient test of skewness and the Ordinary Least Squares method, respectively. Using two sampling strategies, holdout and five-fold cross-validation, we developed five machine learning (ML) models alongside a feed-forward deep neural network (DNN) for the multiclass classification and regression prediction of glioblastoma patient survival. After balancing the classification and regression datasets, we obtained 46,340 and 28,573 samples, respectively. Shapley additive explanations (SHAP) were then used to explain the decision-making process of the best model. In both classification and regression tasks, as well as across holdout and cross-validation sampling strategies, the DNN consistently outperformed the ML models. Notably, the accuracy were 90.25% and 90.22% for holdout and five-fold cross-validation, respectively, while the corresponding R
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