ArticleJournal of neuro-oncology2023
Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.
Article in Journal of neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 3 of them syntheses that pooled it.
What it found
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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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 3 syntheses or guidelines pooled it, 36 citations in OpenAlex.
- AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.Journal of neuro-oncology · 2026Pooled it
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Optimizing MRI sequence selection for glioma classification: a radiomics-based analysis of WHO CNS grade, final pathological diagnosis, and IDH status.La Radiologia medica · 2026Article
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence for Prognostic Modelling and Adaptive Treatment Monitoring in Radiation Oncology.Cureus · 2026Review
- Predicting progression-free survival in glioblastoma with neuroimaging and machine learning.Journal of neuro-oncology · 2026Article
- Leveraging neuroscience imaging and electrophysiological assessments to decode neuron-glioma interactions.Neuro-oncology advances · 2026Review
- Glioblastoma region-specific metabolic signatures reflect patient survival duration.Journal of neuro-oncology · 2026Article
- Machine-Learning-Based Survival Prediction in Glioblastoma Using Graph-Theoretical Analysis of Structural Network Alterations.Cancers · 2026Article
- Anomaly Detection for Structural and Functional Connectivity in Glioma Patients.NMR in biomedicine · 2026Article
- Enhancing clinical insights in glioma grading using Bayesian Optimization and Explainable AI.Journal, genetic engineering & biotechnology · 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
- Multiscale network perspectives on glioma: from tumour biology to symptoms, survival and treatment.Nature reviews. Neurology · 2026Review
- Postsurgical perilesional functional connectivity predicts neurological outcome in glioma patients.Frontiers in neuroscience · 2026Article
- From data silos to integrated diagnosis: multimodal machine learning in glioblastoma.Frontiers in oncology · 2026Review
- Deep learning-based cross-attention fusion of multimodal MRI for survival prediction and risk stratification in IDH-wildtype glioblastoma: a multicenter study.Frontiers in oncology · 2026Article
- Navigating the complexity of WHO CNS5: the evolutionary trajectory of glioma classification and the emergence of large language models.Frontiers in oncology · 2026Review
- Current strategies and novel immunotherapeutic approaches for overcoming immune resistance in glioblastoma.Discover oncology · 2025Review
- Inhibition of TRAF3IP2 Modulates NAMPT and NAD Metabolism in Glioblastoma.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
purposeGlioblastoma (GBM) is the most common and aggressive malignant glioma, with an overall median survival of less than two years. The ability to predict survival before treatment in GBM patients would lead to improved disease management, clinical trial enrollment, and patient care.
methodsGBM patients (N = 133, mean age 60.8 years, median survival 14.1 months, 57.9% male) were retrospectively recruited from the neurosurgery brain tumor service at Washington University Medical Center. All patients completed structural neuroimaging and resting state functional MRI (RS-fMRI) before surgery. Demographics, measures of cortical thickness (CT), and resting state functional network connectivity (FC) were used to train a deep neural network to classify patients based on survival (< 1y, 1-2y, >2y). Permutation feature importance identified the strongest predictors of survival based on the trained models.
resultsThe models achieved a combined cross-validation and hold out accuracy of 90.6% in classifying survival (< 1y, 1-2y, >2y). The strongest demographic predictors were age at diagnosis and sex. The strongest CT predictors of survival included the superior temporal sulcus, parahippocampal gyrus, pericalcarine, pars triangularis, and middle temporal regions. The strongest FC features primarily involved dorsal and inferior somatomotor, visual, and cingulo-opercular networks.
conclusionWe demonstrate that machine learning can accurately classify survival in GBM patients based on multimodal neuroimaging before any surgical or medical intervention. These results were achieved without information regarding presentation symptoms, treatments, postsurgical outcomes, or tumor genomic information. Our results suggest GBMs have a global effect on the brain's structural and functional organization, which is predictive of survival.
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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.