ArticleLa Radiologia medica2023
Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.
Article in La Radiologia medica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Automatic Segmentation and Classification of Glioblastoma and Solitary Brain Metastasis Using a Deep Learning Model on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Bioinformatics reveals the prognostic potential of manganese metabolism-related genes in lung adenocarcinoma.Functional & integrative genomics · 2026Article
- Artificial Intelligence-Based MRI Segmentation in Glioblastoma and Single Brain Metastasis: An Exploratory Study of Diagnostic and Prognostic Value.Life (Basel, Switzerland) · 2026Article
- Glioblastoma survival prediction through MRI and clinical data integration with transfer learning.International journal of computer assisted radiology and surgery · 2026Article
- Machine Learning-Based Prognosis Prediction in Glioblastoma Multiforme Patients by Integrating Clinical Data with Multimodal Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Development and Validation of an Early Recurrence Prediction Model for High-Grade Glioma Integrating Temporalis Muscle and Tumor Features: Exploring the Prognostic Value of Temporalis Muscle.Journal of imaging informatics in medicine · 2026Article
- An integrated prognostic model incorporating clinical characteristics, multiparametric MRI radiomics, deep learning features, and intratumoral heterogeneity for glioma patients.Frontiers in oncology · 2026Article
- Non-femoral head MRI radiomics as a potential imaging biomarker for identifying osteonecrosis of the femoral head in systemic lupus erythematosus patients.Frontiers in medicine · 2026Article
- Machine learning-based integration of dosiomics and pre-radiotherapy multimodal MRI radiomics for survival stratification in patients with glioblastoma multiforme.Radiation oncology (London, England) · 2025Article
- Pre-Treatment PET Radiomics for Prediction of Disease-Free Survival in Cervical Cancer.Cancers · 2025Article
- Deep Neural Network-Based Risk Prediction of Glioblastoma Multiforme Recurrence.Journal of molecular neuroscience : MN · 2025Article
- Radiologists and treatment response assessment: conventional and functional radiological criteria for predicting therapeutic response in virus-related and non-virus-related cancers.Infectious agents and cancer · 2025Review
- PHSP-Net: Personalized Habitat-Aware Deep Learning for Multi-Center Glioblastoma Survival Prediction Using Multiparametric MRI.Bioengineering (Basel, Switzerland) · 2025Article
- Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.Neurosurgery · 2025Review
- Transforming 3D MRI to 2D Feature Maps Using Pre-Trained Models for Diagnosis of Attention Deficit Hyperactivity Disorder.Tomography (Ann Arbor, Mich.) · 2025Article
- The Evolving Landscape of Radiomics in Gliomas: Insights into Diagnosis, Prognosis, and Research Trends.Cancers · 2025Review
- Artificial intelligence in fracture detection on radiographs: a literature review.Japanese journal of radiology · 2025Review
- Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Predicting survival in malignant glioma using artificial intelligence.European journal of medical research · 2025Review
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7 authors.
Funding
Abstract
purposeGlioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. MATERIALS AND
methodsOne hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis.
resultsOur multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values < 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively).
conclusionML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.
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