ArticleNeuro-oncology2016
Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab.
Article in Neuro-oncology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review and epistemic meta-analysis to advance binomial AI-radiomics integration for predicting high-grade glioma progression and enhancing patient management.Scientific reports · 2025Pooled it
- Survival prediction of glioblastoma patients-are we there yet? A systematic review of prognostic modeling for glioblastoma and its clinical potential.Neurosurgical review · 2021Pooled it
- Radiomics-Based Machine Learning for Outcome Prediction in a Multicenter Phase II Study of Programmed Death-Ligand 1 Inhibition Immunotherapy for Glioblastoma.AJNR. American journal of neuroradiology · 2022Trial
- Intranasal perillyl alcohol therapy improves survival of patients with recurrent glioblastoma harboring mutant variant for MTHFR rs1801133 polymorphism.BMC cancer · 2020Trial
- Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab.Neuro-oncology · 2017Trial
- Individualized Treatment Effect Inference of Head and Neck Cancer with Multimodal Data.APSIPA Transactions on Signal and Information Processing · 2026Article
- Prognostic role of delta radiomics in pediatric pontine diffuse midline gliomas.Journal of neuro-oncology · 2026Article
- GlioSurv: interpretable transformer for multimodal, individualized survival prediction in diffuse glioma.NPJ digital medicine · 2025Article
- Engineering Multifunctional Peptide-Decorated Nanofibers for Targeted Delivery of Temozolomide across the Blood-Brain Barrier.Molecular pharmaceutics · 2025Article
- Beta-Caryophyllene Augments Radiotherapy Efficacy in GBM by Modulating Cell Apoptosis and DNA Damage Repair via PPARγ and NF-κB Pathways.Phytotherapy research : PTR · 2025Article
- Article
- Single-Cell Transcriptomics Reveals the Heterogeneity of the Immune Landscape of IDH-Wild-Type High-Grade Gliomas.Cancer immunology research · 2024Article
- Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study.medRxiv : the preprint server for health sciences · 2024Article
- Hypoxia within the glioblastoma tumor microenvironment: a master saboteur of novel treatments.Frontiers in immunology · 2024Review
- Insights into the glioblastoma tumor microenvironment: current and emerging therapeutic approaches.Frontiers in pharmacology · 2024Review
- The application of electrosprayed minocycline-loaded PLGA microparticles for the treatment of glioblastoma.Biotechnology and bioengineering · 2023Article
- Prognostic Values of Systemic Inflammatory Immunological Markers in Glioblastoma: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Assessment and prediction of glioblastoma therapy response: challenges and opportunities.Brain : a journal of neurology · 2023Article
- VAV3 regulates glioblastoma cell proliferation, migration, invasion and cancer stem‑like cell self‑renewal.Molecular medicine reports · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBevacizumab is a humanized antibody against vascular endothelial growth factor approved for treatment of recurrent glioblastoma. There is a need to discover imaging biomarkers that can aid in the selection of patients who will likely derive the most survival benefit from bevacizumab.
methodsThe aim of the study was to examine if pre- and posttherapy multimodal MRI features could predict progression-free survival and overall survival (OS) for patients with recurrent glioblastoma treated with bevacizumab. The patient population included 84 patients in a training cohort and 42 patients in a testing cohort, separated based on pretherapy imaging date. Tumor volumes of interest were segmented from contrast-enhanced T1-weighted and fluid attenuated inversion recovery images and were used to derive volumetric, shape, texture, parametric, and histogram features. A total of 2293 pretherapy and 9811 posttherapy features were used to generate the model.
resultsUsing standard radiographic assessment criteria, the hazard ratio for predicting OS was 3.38 (P < .001). The hazard ratios for pre- and posttherapy features predicting OS were 5.10 (P < .001) and 3.64 (P < .005) for the training and testing cohorts, respectively.
conclusionWith the use of machine learning techniques to analyze imaging features derived from pre- and posttherapy multimodal MRI, we were able to develop a predictive model for patient OS that could potentially assist clinical decision making.
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