ArticleFrontiers in oncology2022
Preoperative and Noninvasive Prediction of Gliomas Histopathological Grades and IDH Molecular Types Using Multiple MRI Characteristics.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 32 citations in OpenAlex.
- Robust deep learning for incomplete MRI sequences in glioma grading and IDH mutation status prediction: a large-scale multicenter study.European radiology · 2026Article
- Differentiation of Molecular Glioblastomas and Lower-grade Diffuse Astrocytomas Using MRI Perfusion and Diffusion Parameters.Clinical neuroradiology · 2026Article
- A novel model based on apparent diffusion coefficient and signal intensity on MRI to quantify subtle internal difference between tumor-adjacent and -distant liver tissues in TBMC medical imaging · 2026Article
- Phenotype augmentation using generative AI for isocitrate dehydrogenase mutation prediction in glioma.Scientific reports · 2025Article
- Noninvasive assessment of glymphatic dysfunction and IDH mutation in glioma with DTI-ALPS and DTI metrics.Quantitative imaging in medicine and surgery · 2025Article
- Histopathological grades as prognostic factors of H3 K27-altered diffuse midline glioma and their prediction using multiparametric MR imaging.Neuroradiology · 2025Article
- The value of morphological magnetic resonance imaging features combined with diffusion kurtosis imaging for predicting Ki-67 expression levels in high-grade gliomas.Quantitative imaging in medicine and surgery · 2025Article
- Preoperative prediction of diffuse glioma type and grade in adults: a gadolinium-free MRI-based decision tree.European radiology · 2025Article
- Histogram analysis of diffusion weighted magnetic resonance imaging for predicting isocitrate dehydrogenase 1 mutation status in brain gliomas.Current journal of neurology · 2025Article
- A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma.Frontiers in oncology · 2025Article
- Apparent diffusion coefficient predicts MGMT status in adult-type diffuse gliomas and is correlated with Ki-67 proliferation index.Frontiers in oncology · 2025Article
- Identification of T2W hypointense ring as a novel noninvasive indicator for glioma grade and IDH genotype.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Article
- The diagnostic efficiency of integration of 2HG MRS and IVIM versus individual parameters for predicting IDH mutation status in gliomas in clinical scenarios: A retrospective study.Journal of neuro-oncology · 2024Article
- Prognostic Value of Apparent Diffusion Coefficient (ADC) in Patients with Diffuse Gliomas.Cancers · 2024Article
- Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading.Neuroradiology · 2023Article
- An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma.Journal of translational medicine · 2023Article
- Combining Multi-Shell Diffusion with Conventional MRI Improves Molecular Diagnosis of Diffuse Gliomas with Deep Learning.Cancers · 2023Article
- Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review.Biomedicines · 2022Review
- Apparent diffusion coefficient for genetic characterization of untreated adult gliomas: A meta-analysis stratified by methods.Neuro-oncology advancesArticle
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Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: Gliomas are one of the most common tumors in the central nervous system. This study aimed to explore the correlation between MRI morphological characteristics, apparent diffusion coefficient (ADC) parameters and pathological grades, as well as IDH gene phenotypes of gliomas. Methods: Preoperative MRI data from 166 glioma patients with pathological confirmation were retrospectively analyzed to compare the differences of MRI characteristics and ADC parameters between the low-grade and high-grade gliomas (LGGs vs. HGGs), IDH mutant and wild-type gliomas (IDH Results: Two multivariable logistic regression models were developed by incorporating age, ADC parameters, and MRI morphological characteristics to predict pathological grades, and IDH gene phenotypes of gliomas, respectively. The Noninvasive Grading Model classified tumor grades with areas under the ROC curve (AUROC) of 0.934 (95% CI=0.895-0.973), sensitivity of 91.2%, and specificity of 78.6%. The Noninvasive IDH Genotyping Model differentiated IDH types with an AUROC of 0.857 (95% CI=0.787-0.926), sensitivity of 88.2%, and specificity of 63.8%. Conclusion: MRI features were correlated with glioma grades and IDH mutation status. Multivariable logistic regression models combined with MRI morphological characteristics and ADC parameters may provide a noninvasive and preoperative approach to predict glioma grades and IDH mutation status.
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