ArticleJournal of neuro-oncology2014
Computer-extracted MR imaging features are associated with survival in glioblastoma patients.
Article in Journal of neuro-oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours.Journal of the Royal Society, Interface · 2018Trial
- Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.Neuro-oncology · 2025Article
- Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.La Radiologia medica · 2023Article
- Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.Medical & biological engineering & computing · 2019Article
- Texture Analysis in Cerebral Gliomas: A Review of the Literature.AJNR. American journal of neuroradiology · 2019Review
- Prediction of survival with multi-scale radiomic analysis in glioblastoma patients.Medical & biological engineering & computing · 2018Article
- Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.Oncotarget · 2018Article
- Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study.Journal of neuro-oncology · 2017Article
- Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study.European radiology · 2017Article
- Ependymal enhancement on magnetic resonance imaging for the identification of high-grade gliomas.Surgical neurology international · 2017Article
- A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.The British journal of radiology · 2016Article
- Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma.Neuro-oncology · 2016Article
- Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.Neuro-oncology · 2016Article
- Article
- Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma.Journal of neuroimaging in psychiatry & neurology · 2016Article
- Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features.Scientific reports · 2015Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonance (MR) images assessed using a fully automatic computer algorithm. MR imaging data for 68 patients from two US institutions were used in this study. The images were obtained from the Cancer Imaging Archive. A fully automatic computer vision algorithm was applied to segment the images and extract eight imaging features from the MRI studies. The features included tumor side, proportion of enhancing tumor, proportion of necrosis, T1/FLAIR ratio, major axis length, minor axis length, tumor volume, and thickness of enhancing margin. We constructed a multivariate Cox proportional hazards regression model and used a likelihood ratio test to establish whether the imaging features are prognostic of survival. We also evaluated the individual prognostic value of each feature through multivariate analysis using the multivariate Cox model and univariate analysis using univariate Cox models for each feature. We found that the automatically extracted imaging features were predictive of survival (p = 0.031). Multivariate analysis of individual features showed that two individual features were predictive of survival: proportion of enhancing tumor (p = 0.013), and major axis length (p = 0.026). Univariate analysis indicated the same two features as significant (p = 0.021, and p = 0.017 respectively). We conclude that computer-extracted MR imaging features can be used for survival prognosis in GBM patients.
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