ArticleMedical science monitor : international medical journal of experimental and clinical research2017
Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 65 citations in OpenAlex.
- Pooled it
- Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.Journal of translational medicine · 2026Article
- The Diagnostic Value of Susceptibility-Weighted Imaging in Preoperative Grading of Glial Tumors.Cureus · 2025Article
- Enhanced magnetic resonance imaging-based radiomics predicts CD40LG expression and survival in high-grade gliomas: a retrospective study.Discover oncology · 2025Article
- Predicting postoperative recurrence and survival in glioma patients using enhanced MRI-based delta habitat radiomics: an 8-year retrospective pilot study.World journal of surgical oncology · 2025Article
- Simplifying Radiomics Workflow for Predicting Grade of Glioma: An Approach for Rapid and Reproducible Radiomics.Journal of biomedical physics & engineering · 2025Article
- FLAIR-based radiomics signature from brain-tumor interface for early prediction of response to EGFR-TKI therapy in NSCLC patients with brain metastasis.Frontiers in cell and developmental biology · 2025Article
- Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.Clinical neuroradiology · 2024Article
- Medical image foundation models in assisting diagnosis of brain tumors: a pilot study.European radiology · 2024Article
- A clinically relevant computed tomography (CT) radiomics strategy for intracranial rodent brain tumour monitoring.Scientific reports · 2024Article
- Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques.Scientific reports · 2023Article
- Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.Quantitative imaging in medicine and surgery · 2023Article
- Transcription Factor-Forced Astrocytic Differentiation Impairs Human Glioblastoma Growth In Vitro and In Vivo.Molecular cancer therapeutics · 2023Article
- Incorporating multiple magnetic resonance diffusion models to differentiate low- and high-grade adult gliomas: a machine learning approach.Quantitative imaging in medicine and surgery · 2022Article
- Surgical Neuro-Oncology: Management of Glioma.Neurologic clinics · 2022Review
- Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion.Cancers · 2022Article
- Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades.Journal of digital imaging · 2020Article
- Diffusion- and perfusion-weighted MRI radiomics model may predict isocitrate dehydrogenase (IDH) mutation and tumor aggressiveness in diffuse lower grade glioma.European radiology · 2020Article
- The Value of Enhanced MR Radiomics in Estimating the IDH1 Genotype in High-Grade Gliomas.BioMed research international · 2020Article
- A simple model for glioma grading based on texture analysis applied to conventional brain MRI.PloS one · 2020Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Gliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences - T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map - in glioma grading, and to improve the power of glioma grading by combining features. MATERIAL AND METHODS Sixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated. RESULTS A total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity. CONCLUSIONS The combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.
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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.