ArticleCancers2024
Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Radiogenomics for Glioblastoma Survival Prediction: Integrating Radiomics, Clinical, and Genomic Features Using Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- miR-132 overexpression is associated with modulation in miR-21 expression and glioblastoma cell behavior.PloS one · 2026Article
- From data silos to integrated diagnosis: multimodal machine learning in glioblastoma.Frontiers in oncology · 2026Review
- The value of multi-parametric magnetic resonance imaging (MRI) radiomics in predicting isocitrate dehydrogenase (IDH) wildtype with telomerase reverse tranase (TERT) promoter mutation in glioma.Quantitative imaging in medicine and surgery · 2025Article
- Uncovering novel functions of NUF2 in glioblastoma and MRI-based expression prediction.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM.
methodsIn this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets.
resultsA total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (
conclusionsResults show that our radiogenomic model generated from radiomic features at baseline MRI, age, and
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