ArticleNeurosurgery2020
An Online Calculator for the Prediction of Survival in Glioblastoma Patients Using Classical Statistics and Machine Learning.
Article in Neurosurgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 3 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
72 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis.BMC medical research methodology · 2025Pooled it
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.Neurosurgical review · 2021Pooled it
- Prediction of lung papillary adenocarcinoma-specific survival using ensemble machine learning models.Scientific reports · 2023Trial
- Real-World Validation of the Clinical Distinctiveness of IDH-Mutant Glioblastoma in the SEER Transition Era: A Population-Based Study Integrating Machine Learning.Brain and behavior · 2026Article
- Machine-Learning-Based Evaluation of the Prognostic Significance of the Non-High-Density Lipoprotein to High-Density Lipoprotein Cholesterol Ratio in Critical Ischemic Stroke.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Development and internal validation of an XGBoost-based prognostic prediction model and risk stratification system for overall survival in pediatric neuroblastoma: a SEER-based study.Translational pediatrics · 2026Article
- Development and validation of a machine learning model for predicting objective response to PD-1 inhibitors in lung cancer patients.Translational lung cancer research · 2026Article
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- Overall survival across registry-defined biopsy and registry-defined STR categories in glioblastoma: a population-based matched and weighted SEER study.Scientific reports · 2026Article
- Temporal Integration of Serum Proteomics, Metabolomics and MRI Tumor Volumetrics via Deep Learning Identifies Systemic Mediators of Glioblastoma Response to Chemoradiotherapy.Research square · 2026Article
- Artificial intelligence-based personalized oncological outcome prediction model for upper urinary tract urothelial carcinoma after radical nephroureterectomy: A development and multicenter validation.Investigative and clinical urology · 2026Article
- Highly Cited Artificial Intelligence Research Studies Published in Neurosurgical Journals: A Bibliometric Analysis.Cureus · 2025Review
- Predicting medical prognosis in patients with glioblastoma during inpatient rehabilitation using bed mobility function.PM & R : the journal of injury, function, and rehabilitation · 2025Article
- Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.Journal of neuro-oncology · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Survival prediction in glioblastoma: 10-year follow-up from the Dutch Neurosurgery Quality Registry.Journal of neuro-oncology · 2025Article
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- A novel predictive model utilizing retinal microstructural features for estimating survival outcome in patients with glioblastoma.Clinical neurology and neurosurgery · 2025Article
- Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.Acta neurochirurgica · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
Abstract
backgroundAlthough survival statistics in patients with glioblastoma multiforme (GBM) are well-defined at the group level, predicting individual patient survival remains challenging because of significant variation within strata.
objectiveTo compare statistical and machine learning algorithms in their ability to predict survival in GBM patients and deploy the best performing model as an online survival calculator.
methodsPatients undergoing an operation for a histopathologically confirmed GBM were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socioeconomic, clinical, and radiographic features to predict overall survival, 1-yr survival status, and compute personalized survival curves.
resultsIn total, 20 821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance index = 0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/).
conclusionThe development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with GBM. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.