Main genetic differences in high-grade gliomas may present different MR imaging and MR spectroscopy correlates

Bernabéu-Sanz, Ángela; Fuentes-Baile, María; Alenda, Cristina

doi:10.1007/s00330-020-07138-4

Cited by 6 publications

(4 citation statements)

References 53 publications

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“…However, in our study, cystic components were more likely to be found in IDH-wildtype tumors than in IDH-mutant tumors. Considering that IDHwildtype tumors are more necrotic than IDH-mutant tumors (38), we speculate that subjectivity and overlap with necrotic components limit the reproducibility of this correlation.…”

Section: Discussionmentioning

confidence: 92%

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

et al. 2021

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PurposeTo evaluate isocitrate dehydrogenase (IDH) status in clinically diagnosed grade II~IV glioma patients using the 2016 World Health Organization (WHO) classification based on MRI parameters.Materials and MethodsOne hundred and seventy-six patients with confirmed WHO grade II~IV glioma were retrospectively investigated as the study set, including lower-grade glioma (WHO grade II, n = 64; WHO grade III, n = 38) and glioblastoma (WHO grade IV, n = 74). The minimum apparent diffusion coefficient (ADCmin) in the tumor and the contralateral normal-appearing white matter (ADCn) and the rADC (ADCmin to ADCn ratio) were defined and calculated. Intraclass correlation coefficient (ICC) analysis was carried out to evaluate interobserver and intraobserver agreement for the ADC measurements. Interobserver agreement for the morphologic categories was evaluated by Cohen’s kappa analysis. The nonparametric Kruskal-Wallis test was used to determine whether the ADC measurements and glioma subtypes were related. By univariable analysis, if the differences in a variable were significant (P<0.05) or an image feature had high consistency (ICC >0.8; κ >0.6), then it was chosen as a predictor variable. The performance of the area under the receiver operating characteristic curve (AUC) was evaluated using several machine learning models, including logistic regression, support vector machine, Naive Bayes and Ensemble. Five evaluation indicators were adopted to compare the models. The optimal model was developed as the final model to predict IDH status in 40 patients with glioma as the subsequent test set. DeLong analysis was used to compare significant differences in the AUCs.ResultsIn the study set, six measured variables (rADC, age, enhancement, calcification, hemorrhage, and cystic change) were selected for the machine learning model. Logistic regression had better performance than other models. Two predictive models, model 1 (including all predictor variables) and model 2 (excluding calcification), correctly classified IDH status with an AUC of 0.897 and 0.890, respectively. The test set performed equally well in prediction, indicating the effectiveness of the trained classifier. The subgroup analysis revealed that the model predicted IDH status of LGG and GBM with accuracy of 84.3% (AUC = 0.873) and 85.1% (AUC = 0.862) in the study set, and with the accuracy of 70.0% (AUC = 0.762) and 70.0% (AUC = 0.833) in the test set, respectively.ConclusionThrough the use of machine-learning algorithms, the accurate prediction of IDH-mutant versus IDH-wildtype was achieved for adult diffuse gliomas via noninvasive MR imaging characteristics, including ADC values and tumor morphologic features, which are considered widely available in most clinical workstations.

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Section: Discussionmentioning

confidence: 92%

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

et al. 2021

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“…Indeed, lack of calcification was strongly correlated with the IDH WT status. Cystic components were more common in WT tumors compared with the mutant ones in their patient cohort, probably reflecting the nature of being more necrotic (15). The ADC r of mutant gliomas was found to be higher than those of the WT gliomas in WHO grade 2–4 gliomas and low-grade glioma subgroup, but not so in the GBM subgroup, which was attributed to the small sample size and heterogeneity in the patients with mutant GBM.…”

Section: Discussionmentioning

confidence: 99%

Predictive role of magnetic resonance imaging in the distinction of isocitrate dehydrogenase (IDH) mutant grade 4 astrocytomas versus glioblastomas

et al. 2023

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Background Isocitrate dehydrogenase (IDH) mutation status is a crucial prognostic factor in high-grade glial tumors. Purpose To investigate whether magnetic resonance imaging (MRI) features can display a diagnostic performance in the determination of IDH mutation in high-grade gliomas. Material and Methods A total of 170 patients including 24 IDH mutant grade 4 astrocytomas and 146 glioblastomas (GBM) were retrospectively examined via contrast-enhanced (CE) MRI before surgery. Immunohistochemistry and genomic sequence analyses were performed on specimen materials for the determination of IDH mutational status. Certain morphological and diffusion-weighted imaging (DWI) parameters were utilized to see if they could play a role to be non-invasive potential imaging predictors in the discrimination of IDH mutant versus wild-type (WT) high-grade gliomas. Results On histopathological examination, IDH mutation was detected in 24 patients with high-grade glioma and 146 of the patients were found to be WT. Certain morphological criteria of tumor location and involvement, tumor margins, visual detection of diffusion restriction on DWI, and quantitative apparent diffusion coefficient (ADC) parameters consisting of ADCmean, ADCmin, and ADCr could be used as imaging predictors in the discrimination of high-grade IDH mutant versus WT tumors. Conclusion Certain MRI morphologic features and visual detection of diffusion restriction on DWI and quantitative ADC parameters consisting of ADCmean, ADCmin, and ADCr can be considered non-invasive, significant independent imaging predictors in the discrimination and can obviate invasive procedures for histopathological diagnosis.

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“…Considering the internal consistency of the tumors, IDH-WT tumors are more necrotic than IDH1-MUT ones [ 32 , 33 ] and might present hemorrhage [ 33 , 34 ]. Assessment of the tumors on T2w images may assist in making a differential diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review

et al. 2022

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The introduction of molecular criteria into the classification of diffuse gliomas has added interesting practical implications to glioma management. This has created a new clinical need for correlating imaging characteristics with glioma genotypes, also known as radiogenomics or imaging genomics. Although many studies have primarily focused on the use of advanced magnetic resonance imaging (MRI) techniques for radiogenomics purposes, conventional MRI sequences remain the reference point in the study and characterization of brain tumors. A summary of the conventional imaging features of glioma molecular subtypes should be useful as a tool for daily diagnostic brain tumor management. Hence, this article aims to summarize the conventional MRI features of glioma molecular subtypes in light of the recent literature.

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Main genetic differences in high-grade gliomas may present different MR imaging and MR spectroscopy correlates

Cited by 6 publications

References 53 publications

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Predictive Role of the Apparent Diffusion Coefficient and MRI Morphologic Features on IDH Status in Patients With Diffuse Glioma: A Retrospective Cross-Sectional Study

Predictive role of magnetic resonance imaging in the distinction of isocitrate dehydrogenase (IDH) mutant grade 4 astrocytomas versus glioblastomas

Conventional MRI-Derived Biomarkers of Adult-Type Diffuse Glioma Molecular Subtypes: A Comprehensive Review

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