ArticleJournal of personalized medicine2021
Machine Learning and Radiomic Features to Predict Overall Survival Time for Glioblastoma Patients.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis 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
11 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
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- Scoping review of image-based overall survival prediction in glioma using machine learning.Polish journal of radiology · 2025Review
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.Medical & biological engineering & computing · 2023Article
- Forecasting Molecular Features in IDH-Wildtype Gliomas: The State of the Art of Radiomics Applied to Neurosurgery.Cancers · 2023Review
- An image-based modeling framework for predicting spatiotemporal brain cancer biology within individual patients.Frontiers in oncology · 2023Article
- Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI.Diagnostics (Basel, Switzerland) · 2022Review
- Relevance maps: A weakly supervised segmentation method for 3D brain tumours in MRIs.Frontiers in radiology · 2022Article
- Uses of artificial intelligence in glioma: A systematic review.Medicine internationalArticle
- Estimating overall survival of glioblastoma patients using clinical variables, tumor size, and location.Neuro-oncology advancesArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma is an aggressive brain tumor with a low survival rate. Understanding tumor behavior by predicting prognosis outcomes is a crucial factor in deciding a proper treatment plan. In this paper, an automatic overall survival time prediction system (OST) for glioblastoma patients is developed on the basis of radiomic features and machine learning (ML). This system is designed to predict prognosis outcomes by classifying a glioblastoma patient into one of three survival groups: short-term, mid-term, and long-term. To develop the prediction system, a medical dataset based on imaging information from magnetic resonance imaging (MRI) and non-imaging information is used. A novel radiomic feature extraction method is proposed and developed on the basis of volumetric and location information of brain tumor subregions extracted from MRI scans. This method is based on calculating the volumetric features from two brain sub-volumes obtained from the whole brain volume in MRI images using brain sectional planes (sagittal, coronal, and horizontal). Many experiments are conducted on the basis of various ML methods and combinations of feature extraction methods to develop the best OST system. In addition, the feature fusions of both radiomic and non-imaging features are examined to improve the accuracy of the prediction system. The best performance was achieved by the neural network and feature fusions.
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