ArticleNeurosurgical review2020
Magnetic resonance imaging texture analyses in lower-grade gliomas with a commercially available software: correlation of apparent diffusion coefficient and T2 skewness with 1p/19q codeletion.
Article in Neurosurgical review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Imaging biomarkers for detecting IDH mutations and monitoring response to novel targeted therapies: Current insights and future perspectives.Neuro-oncology · 2026Review
- Deep learning in precision medicine and focus on glioma.Bioengineering & translational medicine · 2023Review
- Deep-learning and conventional radiomics to predictFrontiers in oncology · 2023Article
- Radiological model based on the standard magnetic resonance sequences for detecting methylguanine methyltransferase methylation in glioma using texture analysis.Cancer science · 2021Article
- Performances of Whole Tumor Texture Analysis Based on MRI: Predicting Preoperative T Stage of Rectal Carcinomas.Frontiers in oncology · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preoperative prediction of molecular information of lower-grade gliomas (LrGGs) helps to determine the overall treatment strategy as well as the initial surgical strategy. This study aimed to detect magnetic resonance imaging (MRI) texture parameters to predict the molecular signature of LrGGs using a commercially available software and routine MR images. Forty-three patients treated at Keio University Hospital who had World Health Organization grade II or III gliomas were included. All patients having preoperative T1- and T2-weighted, fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted (DW) images were also included. Texture analyses of T2, FLAIR, and apparent diffusion coefficient (ADC) histograms were performed using a commercially available software. Texture parameters including kurtosis, skewness, and entropy were investigated to determine any correlation with the presence or absence of isocitrate dehydrogenase (IDH) mutations, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. ADC skewness and T2 skewness were significantly associated with 1p/19q codeletion status. ADC skewness of ≥ 0.25 predicted 1p/19q codeletion with a sensitivity and specificity of 80% and 65.2%, respectively (AUC = 0.728). T2 skewness of ≥ - 0.11 predicted 1p/19q codeletion with a sensitivity and specificity of 80% and 91.3%, respectively, (AUC = 0.866). None of the texture parameters were associated with IDH mutation and MGMT promoter methylation. MRI texture analysis using a commercially available software demonstrated that T2 skewness could predict 1p/19q codeletion with high sensitivity and specificity, suggesting a clinical utility.
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