ArticleNPJ digital medicine2023
Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.
- Application of artificial intelligence in forecasting survival in high-grade glioma: systematic review and meta-analysis involving 79,638 participants.Neurosurgical review · 2025Pooled it
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- Women in neurosurgical oncology: Mapping the milestones and shaping the future.Neuro-oncology advances · 2026Review
- Automated feature learning and survival prognostication in grade 4 glioma using supervised machine learning models.Journal of neuro-oncology · 2025Article
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- Toxicological insights and safety considerations of vorasidenib in grade 2 astrocytoma and oligodendroglioma.International journal of surgery (London, England) · 2025Article
- Unveiling the Role of Protein Posttranslational Modifications in Glioma Prognosis.CNS neuroscience & therapeutics · 2025Article
- Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches.Journal of medical Internet research · 2025Article
- Assessing the Validity of Diffusion Weighted Imaging Models: A Study in Patients with Post-Surgical Lower-Grade Glioma.Journal of clinical medicine · 2025Article
- DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.IEEE transactions on visualization and computer graphics · 2025Article
- Machine Learning for Individualized Risk Estimation in Anterior Lumbar Interbody Fusion.Neurosurgery practice · 2024Article
- Article
- The application of time-to-event analysis in machine learning prognostic models.Journal of translational medicine · 2024Article
- Synthetic minority over-sampling technique-enhanced machine learning models for predicting recurrence of postoperative chronic subdural hematoma.Frontiers in neurology · 2024Article
- Retinal Microstructural Changes Reflecting Treatment-Associated Cognitive Dysfunction in Patients with Lower-Grade Gliomas.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
WHO grade II and III gliomas demonstrate diverse biological behaviors resulting in variable survival outcomes. In the context of glioma prognosis, machine learning (ML) approaches could facilitate the navigation through the maze of factors influencing survival, aiding clinicians in generating more precise and personalized survival predictions. Here we report the utilization of ML models in predicting survival at 12, 24, 36, and 60 months following grade II and III glioma diagnosis. From the National Cancer Database, we analyze 10,001 WHO grade II and 11,456 grade III cranial gliomas. Using the area under the receiver operating characteristic (AUROC) values, we deploy the top-performing models in a web application for individualized predictions. SHapley Additive exPlanations (SHAP) enhance the interpretability of the models. Top-performing predictive models are the ones built with LightGBM and Random Forest algorithms. For grade II gliomas, the models yield AUROC values ranging from 0.813 to 0.896 for predicting mortality across different timeframes, and for grade III gliomas, the models yield AUROCs ranging from 0.855 to 0.878. ML models provide individualized survival forecasts for grade II and III glioma patients across multiple clinically relevant time points. The user-friendly web application represents a pioneering digital tool to potentially integrate predictive analytics into neuro-oncology clinical practice, to empower prognostication and personalize clinical decision-making.
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What OpenQuestion holds
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.