ArticleJournal of neuro-oncology2018
Molecular classification of patients with grade II/III glioma using quantitative MRI characteristics.
Article in Journal of neuro-oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Noninvasive Prediction ofDiagnostics (Basel, Switzerland) · 2026Article
- Magnetic resonance imaging fractal analysis of O(6)-methylguanine-DNA methyltransferase promoter methylation status in isocitrate dehydrogenase wild-type glioblastoma.Quantitative imaging in medicine and surgery · 2025Article
- Article
- Artificial intelligence-based MRI radiomics and radiogenomics in glioma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Review
- Prediction of Ki-67 labeling index, ATRX mutation, and MGMT promoter methylation status in IDH-mutant astrocytoma by morphological MRI, SWI, DWI, and DSC-PWI.European radiology · 2023Article
- [A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- Article
- Texture Analysis in Brain Tumor MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Radiomic Features Associated with Extent of Resection in Glioma Surgery.Acta neurochirurgica. Supplement · 2022Review
- Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Radiomics and radiogenomics in gliomas: a contemporary update.British journal of cancer · 2021Review
- Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis.Cancer science · 2021Article
- Prognostic relevance of adding MRI data to WHO 2016 and cIMPACT-NOW updates for diffuse astrocytic tumors in adults. Working toward the extended use of MRI data in integrated glioma diagnosis.Brain pathology (Zurich, Switzerland) · 2021Article
- Preoperative Radiomics Analysis of 1p/19q Status in WHO Grade II Gliomas.Frontiers in oncology · 2021Article
- Radiomics in gliomas: clinical implications of computational modeling and fractal-based analysis.Neuroradiology · 2020Review
- Multivariable non-invasive association of isocitrate dehydrogenase mutational status in World Health Organization grade II and III gliomas with advanced magnetic resonance imaging T2 mapping techniques.The neuroradiology journal · 2020Article
- Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.Neuroradiology · 2020Article
- Radiogenomics of lower-grade gliomas: machine learning-based MRI texture analysis for predicting 1p/19q codeletion status.European radiology · 2020Article
- Article
- Texture Analysis in Cerebral Gliomas: A Review of the Literature.AJNR. American journal of neuroradiology · 2019Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
backgroundMolecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas-IDH, 1p/19q, and MGMT status-show distinct quantitative MRI characteristics on FLAIR imaging.
methodsSixty-one patients with grade II/III gliomas who had molecular data and MRI available prior to radiation were included. Quantitative MRI features were extracted that measured tissue heterogeneity (homogeneity and pixel correlation) and FLAIR border distinctiveness (edge contrast; EC). T-tests were conducted to determine whether patients with different genotypes differ across the features. Logistic regression with LASSO regularization was used to determine the optimal combination of MRI and clinical features for predicting molecular subtypes.
resultsPatients with IDH wildtype tumors showed greater signal heterogeneity (p = 0.001) and lower EC (p = 0.008) within the FLAIR region compared to IDH mutant tumors. Among patients with IDH mutant tumors, 1p/19q co-deleted tumors had greater signal heterogeneity (p = 0.002) and lower EC (p = 0.005) compared to 1p/19q intact tumors. MGMT methylated tumors showed lower EC (p = 0.03) compared to the unmethylated group. The combination of FLAIR border distinctness, heterogeneity, and pixel correlation optimally classified tumors by IDH status.
conclusionQuantitative imaging characteristics of FLAIR heterogeneity and border pattern in grade II/III gliomas may provide unique information for determining molecular status at time of initial diagnostic imaging, which may then guide subsequent surgical and medical management.
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