ArticleMedical & biological engineering & computing2019
Regression based overall survival prediction of glioblastoma multiforme patients using a single discovery cohort of multi-institutional multi-channel MR images.
Article in Medical & biological engineering & computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review.BMC cancer · 2024Pooled it
- Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma.European journal of medical research · 2025Article
- GlioSurvQNet: A DuelContextAttn DQN Framework for Brain Tumor Prognosis with Metaheuristic Optimization.Diagnostics (Basel, Switzerland) · 2025Article
- Radiomic and Volumetric Measurements as Clinical Trial Endpoints-A Comprehensive Review.Cancers · 2022Review
- Identifying risk factors of intracerebral hemorrhage stability using explainable attention model.Medical & biological engineering & computing · 2022Article
- Survival prediction in glioblastoma on post-contrast magnetic resonance imaging using filtration based first-order texture analysis: Comparison of multiple machine learning models.The neuroradiology journal · 2021Article
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
- An Automatic Deep Learning-Based Workflow for Glioblastoma Survival Prediction Using Preoperative Multimodal MR Images: A Feasibility Study.Advances in radiation oncologyArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Glioblastoma multiforme (GBM) are malignant brain tumors, associated with poor overall survival (OS). This study aims to predict OS of GBM patients (in days) using a regression framework and assess the impact of tumor shape features on OS prediction. Multi-channel MR image derived texture features, tumor shape, and volumetric features, and patient age were obtained for 163 GBM patients. In order to assess the impact of tumor shape features on OS prediction, two feature sets, with and without tumor shape features, were created. For the feature set with tumor shape features, the mean prediction error (MPE) was 14.6 days and its 95% confidence interval (CI) was 195.8 days. For the feature set excluding shape features, the MPE was 17.1 days and its 95% CI was observed to be 212.7 days. The coefficient of determination (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.