ArticleCancers2024
Reproducible and Interpretable Machine Learning-Based Radiomic Analysis for Overall Survival Prediction in Glioblastoma Multiforme.
Article in Cancers, 2024. 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.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Pre-Radiotherapy Synthetic MRI-Derived Quantitative Heterogeneity and Early Recurrence in Glioblastoma.Journal of magnetic resonance imaging : JMRI · 2026Observational
- PET|MR Open Source Radiomics Models for Treatment Outcome Prediction in Recurrent GBM.Cancers · 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
- Article
- Machine-Learning-Based Survival Prediction in Glioblastoma Using Graph-Theoretical Analysis of Structural Network Alterations.Cancers · 2026Article
- A 3D feature fusion model integrating multi-scale MRI feature for interpretable Glioblastoma prediction.Medical & biological engineering & computing · 2026Article
- Systemic Therapy for Salivary Gland Cancers: A Review of Targeted and Chemotherapeutic Approaches.Current treatment options in oncology · 2026Review
- From data silos to integrated diagnosis: multimodal machine learning in glioblastoma.Frontiers in oncology · 2026Review
- PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.Bioengineering (Basel, Switzerland) · 2025Article
- A stacking ensemble framework integrating radiomics and deep learning for prognostic prediction in head and neck cancer.Radiation oncology (London, England) · 2025Article
- Machine learning and deep learning in glioblastoma: a systematic review and meta-analysis of diagnosis, prognosis, and treatment.Discover oncology · 2025Article
- Article
- Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis.Frontiers in oncology · 2025Article
- Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study.Health science reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTo develop and validate an MRI-based radiomic model for predicting overall survival (OS) in patients diagnosed with glioblastoma multiforme (GBM), utilizing a retrospective dataset from multiple institutions. MATERIALS AND
methodsPre-treatment MRI images of 289 GBM patients were collected. From each patient's tumor volume, 660 radiomic features (RFs) were extracted and subjected to robustness analysis. The initial prognostic model with minimum RFs was subsequently enhanced by including clinical variables. The final clinical-radiomic model was derived through repeated three-fold cross-validation on the training dataset. Performance evaluation included assessment of concordance index (C-Index), integrated area under curve (iAUC) alongside patient stratification into low and high-risk groups for overall survival (OS).
resultsThe final prognostic model, which has the highest level of interpretability, utilized primary gross tumor volume (GTV) and one MRI modality (T2-FLAIR) as a predictor and integrated the age variable with two independent, robust RFs, achieving moderately good discriminatory performance (C-Index [95% confidence interval]: 0.69 [0.62-0.75]) with significant patient stratification (
conclusionWe identified and validated a clinical-radiomic model for stratification of patients into low and high-risk groups based on OS in patients with GBM using a multicenter retrospective dataset. Future work will focus on the use of deep learning-based features, with recently standardized convolutional filters on OS tasks.
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