ArticleAJNR. American journal of neuroradiology2012
Survival analysis of patients with high-grade gliomas based on data mining of imaging variables.
Article in AJNR. American journal of neuroradiology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled 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
- A three-dimensional computational analysis of magnetic resonance images characterizes the biological aggressiveness in malignant brain tumours.Journal of the Royal Society, Interface · 2018Trial
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.Frontiers in bioinformatics · 2021Article
- Evaluating survival in subjects with astrocytic brain tumors by dynamic susceptibility-weighted perfusion MR imaging.PloS one · 2021Article
- Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review.Cancers · 2020Review
- Automated glioma grading on conventional MRI images using deep convolutional neural networks.Medical physics · 2020Article
- Article
- Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.Scientific reports · 2019Article
- Robustness of Radiomics for Survival Prediction of Brain Tumor Patients Depending on Resection Status.Frontiers in computational neuroscience · 2019Article
- Predicting primary outcomes of brain tumor patients with advanced neuroimaging MRI measures.Interdisciplinary neurosurgery : Advanced techniques and case management · 2018Article
- Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.Journal of neuro-oncology · 2018Article
- Whole-Tumor Histogram and Texture Analyses of DTI for Evaluation ofAJNR. American journal of neuroradiology · 2018Article
- IVIM perfusion fraction is prognostic for survival in brain glioma.Clinical neuroradiology · 2017Article
- Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study.European radiology · 2017Article
- Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas.Neuro-oncology · 2017Article
- Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma.Neuro-oncology · 2016Article
- Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab.Neuro-oncology · 2016Article
- A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.The British journal of radiology · 2016Article
- Outcome Prediction for Patient with High-Grade Gliomas from Brain Functional and Structural Networks.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
background and purposeThe prediction of prognosis in HGGs is poor in the majority of patients. Our aim was to test whether multivariate prediction models constructed by machine-learning methods provide a more accurate predictor of prognosis in HGGs than histopathologic classification. The prediction of survival was based on DTI and rCBV measurements as an adjunct to conventional imaging. MATERIALS AND
methodsThe relationship of survival to 55 variables, including clinical parameters (age, sex), categoric or continuous tumor descriptors (eg, tumor location, extent of resection, multifocality, edema), and imaging characteristics in ROIs, was analyzed in a multivariate fashion by using data-mining techniques. A variable selection method was applied to identify the overall most important variables. The analysis was performed on 74 HGGs (18 anaplastic gliomas WHO grades III/IV and 56 GBMs or gliosarcomas WHO grades IV/IV).
resultsFive variables were identified as the most significant, including the extent of resection, mass effect, volume of enhancing tumor, maximum B0 intensity, and mean trace intensity in the nonenhancing/edematous region. These variables were used to construct a prediction model based on a J48 classification tree. The average classification accuracy, assessed by cross-validation, was 85.1%. Kaplan-Meier survival curves showed that the constructed prediction model classified malignant gliomas in a manner that better correlates with clinical outcome than standard histopathology.
conclusionsPrediction models based on data-mining algorithms can provide a more accurate predictor of prognosis in malignant gliomas than histopathologic classification alone.
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