ArticleInternational journal of computer assisted radiology and surgery2018
MRI radiomics analysis of molecular alterations in low-grade gliomas.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 3 of them syntheses that pooled it.
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Who cites it
55 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics and Machine Learning in Diagnostics of Glial Brain Tumors: a Systematic Review and Meta-Analysis.Sovremennye tekhnologii v meditsine · 2025Pooled it
- Diagnostic performance of radiomics using machine learning algorithms to predict MGMT promoter methylation status in glioma patients: a meta-analysis.Diagnostic and interventional radiology (Ankara, Turkey) · 2021Pooled it
- Noninvasive Determination ofAJNR. American journal of neuroradiology · 2021Pooled it
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Enhanced magnetic resonance imaging-based radiomics predicts CD40LG expression and survival in high-grade gliomas: a retrospective study.Discover oncology · 2025Article
- From pixels to prognosis: leveraging radiomics and machine learning to predict IDH1 genotype in gliomas.Neurosurgical review · 2025Article
- Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.Cancers · 2025Article
- Radiomics Analysis of Quantitative Maps from Synthetic MRI for Predicting Grades and Molecular Subtypes of Diffuse Gliomas.Clinical neuroradiology · 2024Article
- Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements.The Lancet. Oncology · 2024Review
- -New frontiers in domain-inspired radiomics and radiogenomics: increasing role of molecular diagnostics in CNS tumor classification and grading following WHO CNS-5 updates.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Development of End-to-End AI-Based MRI Image Analysis System for Predicting IDH Mutation Status of Patients with Gliomas: Multicentric Validation.Journal of imaging informatics in medicine · 2024Article
- Prognostic Factors and Resectability Predictors in Insular Gliomas: A Systematic Review.Journal of neurological surgery. Part A, Central European neurosurgery · 2024Article
- The current state of MRI-based radiomics in pituitary adenoma: promising but challenging.Frontiers in endocrinology · 2024Review
- Phenomic Imaging.Phenomics (Cham, Switzerland) · 2023Review
- Radiomics and Machine Learning in Brain Tumors and Their Habitat: A Systematic Review.Cancers · 2023Review
- Article
- Imaging 2-hydroxyglutarate and other brain oncometabolites pertinent to critical genomic alterations in brain tumors.BJR open · 2023Review
- Predicting IDH Mutation Status in Low-Grade Gliomas Based on Optimal Radiomic Features Combined with Multi-Sequence Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- SWI by 7T MR Imaging for the Microscopic Imaging Diagnosis of Astrocytic and Oligodendroglial Tumors.AJNR. American journal of neuroradiology · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLow-grade gliomas (LGG) are classified into three distinct groups based on their IDH1 mutation and 1p/19q codeletion status, each of which is associated with a different clinical expression. The genomic sub-classification of LGG requires tumor sampling via neurosurgical procedures. The aim of this study was to evaluate the radiomics approach for noninvasive classification of patients with LGG and IDH mutation, based on their 1p/19q codeletion status, by testing different classifiers and assessing the contribution of the different MR contrasts.
methodsPreoperative MRI scans of 47 patients diagnosed with LGG with IDH1-mutated tumors and a genetic analysis for 1p/19q deletion status were included in this study. A total of 152 features, including size, location and texture, were extracted from fluid-attenuated inversion recovery images, [Formula: see text]-weighted images (WI) and post-contrast [Formula: see text]. Classification was performed using 17 machine learning classifiers. Results were evaluated by a fivefold cross-validation analysis.
resultsRadiomic analysis differentiated tumors with 1p/19q intact ([Formula: see text]; astrocytomas) from those with 1p/19q codeleted ([Formula: see text]; oligodendrogliomas). Best classification was obtained using the Ensemble Bagged Trees classifier, with sensitivity [Formula: see text] 92%, specificity [Formula: see text] 83% and accuracy [Formula: see text] 87%, and with area under the curve [Formula: see text] 0.87. Tumors with 1p/19q intact were larger than those with 1p/19q codeleted ([Formula: see text] vs. [Formula: see text] cc, respectively; [Formula: see text]) and predominantly located to the left insula ([Formula: see text]).
conclusionThe proposed method yielded good discrimination between LGG with and without 1p/19q codeletion. Results from this study demonstrate the great potential of this method to aid decision-making in the clinical management of patients with LGG.
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