Diffuse midline gliomas, H3 K27M-mutant are associated with less peritumoral edema and contrast enhancement in comparison to glioblastomas, H3 K27M-wildtype of midline structures

Banan, Rouzbeh; Akbarian, Arash; Samii, M.; Samii, Amir; Bertalanffy, Helmut; Lehmann, Ulrich; Hartmann, Christian; Brüning, R.

doi:10.1371/journal.pone.0249647

Cited by 16 publications

(19 citation statements)

References 49 publications

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“…In this study, both DMG (53.3 %) and GBM (64.3 %) were more common in males which is concordant with previous studies which showed a male preponderance in both DMG (54%) 8 and GBM (56.81%). 3,12 While previous studies showed that the most common location of DMG is the pons and that of GBM is in the temporal lobe, this study showed that the thalamus and frontal lobe are common locations for DMG and GBMs respectively.…”

Section: Discussionsupporting

confidence: 92%

“…This result is similar to that previously reported by other investigators which reported children and younger patients are more prone to DMG (mean age = 27 ± 13.8). 3,8 GBM usually occurs after the age of 40 years. 5,7,9,10 This study showed most patients with DMG were of 25 years or below age group, whereas the majority of GBMs were of above 50 years age group.…”

Section: Discussionmentioning

confidence: 99%

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Comparative study of diffuse midline glioma and glioblastoma: Magnetic Resonance Imaging (MRI) in the characteristics and demography

Joshi

et al. 2022

Nep J Neurosci

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Introduction: Diffuse midline glioma and glioblastoma are classified as grade IV CNS tumors (WHO). The entity ‘diffuse midline glioma, H3 K27 mutant’ was introduced in the 4th revised edition of the 2016 WHO classification of brain tumors; however, there are only a few reports on Magnetic Resonance Imaging of these tumors. Thus, we conducted a retrospective study focused on Magnetic Resonance Imaging features of diffuse midline glioma compared to glioblastoma. This study aims to evaluate and compare the demographic characteristics, anatomic location of lesions, and MRI characteristics of diffuse midline glioma and glioblastoma. Methods: We histologically confirmed 30 patients with diffuse midline glioma and 70 patients with glioblastoma were enrolled in this retrospective study. Pretreatment MRI of each patient was reviewed by a neuroradiology issuing physician and neuroradiology reporting physician for MRI characteristics of tumors. Comparative analysis was performed of the imaging pattern to show differences between diffuse midline glioma and glioblastoma with the p-value. Results: The age of patients with diffuse midline glioma (mean age =24.7 ±10.4) was significantly lower than in those with glioblastoma (mean age =48.2 ±1). The majority of patients with diffuse midline glioma (56.7%) and glioblastoma (51.4%) were ≤ 25 and ≥ 50 years age group respectively. The most common location of diffuse midline glioma and glioblastoma were the thalamus (73.3%) and frontal lobe (37.1%) respectively. The presence of hydrocephalus, edema, and invasion were statistically significantly differences in patients with diffuse midline glioma (hydrocephalus = 46.7%, edema = 53.3%, and invasion =30%) than in those with glioblastoma (hydrocephalus = 12.9%, edema = 88.6%, and invasion =5.7%) (P < 0.05 each). Conclusions: Despite having similar imaging features, diffuse midline glioma exhibited marked differences in age, edema, invasion, and hydrocephalus in MRI compared to Glioblastoma.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Comparative study of diffuse midline glioma and glioblastoma: Magnetic Resonance Imaging (MRI) in the characteristics and demography

Joshi

et al. 2022

Nep J Neurosci

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“…A significant difference in ADC, CBV, and CBF values (measured with the freehand regions of interest) has been reported between H3 K27M mutant and wild-type DMGs (28)(29)(30). Other studies found that several semantic and semiquantitative features on cMRI could be used to predict H3 K27M mutant status in DMG (31,32). But other non-radiomics studies using cMRI and DWI to predict H3 K27M mutant status showed converse results (33,34).…”

Section: Discussionmentioning

confidence: 99%

Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques

et al. 2022

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ObjectivesThe performance of multiparametric MRI-based radiomics models for predicting H3 K27M mutant status in diffuse midline glioma (DMG) has not been thoroughly evaluated. The optimal combination of multiparametric MRI and machine learning techniques remains undetermined. We compared the performance of various radiomics models across different MRI sequences and different machine learning techniques.MethodsA total of 102 patients with pathologically confirmed DMG were retrospectively enrolled (27 with H3 K27M-mutant and 75 with H3 K27M wild-type). Radiomics features were extracted from eight sequences, and 18 feature sets were conducted by independent combination. There were three feature matrix normalization algorithms, two dimensionality-reduction methods, four feature selectors, and seven classifiers, consisting of 168 machine learning pipelines. Radiomics models were established across different feature sets and machine learning pipelines. The performance of models was evaluated using receiver operating characteristic curves with area under the curve (AUC) and compared with DeLong’s test.ResultsThe multiparametric MRI-based radiomics models could accurately predict the H3 K27M mutant status in DMG (highest AUC: 0.807–0.969, for different sequences or sequence combinations). However, the results varied significantly between different machine learning techniques. When suitable machine learning techniques were used, the conventional MRI-based radiomics models shared similar performance to advanced MRI-based models (highest AUC: 0.875–0.915 vs. 0.807–0.926; DeLong’s test, p > 0.05). Most models had a better performance when generated with a combination of MRI sequences. The optimal model in the present study used a combination of all sequences (AUC = 0.969).ConclusionsThe multiparametric MRI-based radiomics models could be useful for predicting H3 K27M mutant status in DMG, but the performance varied across different sequences and machine learning techniques.

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“…Three readers with neuroradiological experience for at least 7 years (RBr, AA, PR) independently judged the MRI data while blinded to each other's results. The following aspects were scored (adapted from Banan et al [18]): (1) location (thalamus, brainstem, vermis, cerebellum), (2) absence or presence of intratumoral necrosis, (3) degree of contrast enhancement in a four-step grading scheme of the intensity (strong, intermediate, low or absent), (4) presence of peritumoral edema, (5) presence of mass effect, (6) presence of rim enhancement and (7) occurrence of multifocal tumor manifestations. If a disagreement of the readers was noted, a consensus reading was performed in order to reach a final decision (based on majority).…”

Section: Mr Imaging and Analysismentioning

confidence: 99%

“…In a previous study from a singular neurosurgical institution, we analyzed the MRI signature of 24 DMGs and compared these with a series of 19 glioblastomas WHO grade IV, H3 K27M wildtype, of midline structures (midGBM-H3wt) [18]. We have now expanded this series, adding patients from a second institution, including the evaluation of the diffusion weighted imaging/apparent diffusion coefficient (DWI/ADC) pattern of our cases and controls as an additional criterion.…”

Section: Introductionmentioning

confidence: 99%

Differences in the MRI Signature and ADC Values of Diffuse Midline Gliomas with H3 K27M Mutation Compared to Midline Glioblastomas

Raab

Banan

Akbarian

et al. 2022

Cancers

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We conducted a two-center retrospective survey on standard MRI features including apparent diffusion coefficient mapping (ADC) of diffuse midline gliomas H3 K27M-mutant (DMG) compared to midline glioblastomas H3 K27M-wildtype (midGBM-H3wt). We identified 39 intracranial DMG and 18 midGBM-H3wt tumors. Samples were microscopically re-evaluated for microvascular proliferations and necrosis. Image analysis focused on location, peritumoral edema, degree of contrast enhancement and DWI features. Within DMG, MRI features between tumors with or without histomorphological GBM features were compared. DMG occurred in 15/39 samples from the thalamus (38%), in 23/39 samples from the brainstem (59%) and in 1/39 tumors involving primarily the cerebellum (2%). Edema was present in 3/39 DMG cases (8%) versus 78% in the control (midGBM-H3wt) group (p < 0.001). Contrast enhancement at the tumor rim was detected in 17/39 DMG (44%) versus 67% in control (p = 0.155), and necrosis in 24/39 (62%) versus 89% in control (p = 0.060). Strong contrast enhancement was observed in 15/39 DMG (38%) versus 56% in control (p = 0.262). Apparent diffusion coefficient (ADC) histogram analysis showed significantly higher skewness and kurtosis values in the DMG group compared to the controls (p = 0.0016/p = 0.002). Minimum relative ADC (rADC) values, as well as the 10th and 25th rADC-percentiles, were lower in DMGs with GBM features within the DMG group (p < 0.001/p = 0.012/p = 0.027). In conclusion, DMG cases exhibited markedly less edema than midGBM-H3wt, even if histomorphological malignancy was present. Histologically malignant DMGs and midGBM-H3wt more often displayed strong enhancement, as well as rim enhancement, than DMGs without histomorphological malignancy. DMGs showed higher skewness and kurtosis values on ADC-histogram analysis compared to midGBM-H3wt. Lower minimum rADC values in DMGs indicated malignant histomorphological features, likely representing a more complex tissue microstructure.

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Diffuse midline gliomas, H3 K27M-mutant are associated with less peritumoral edema and contrast enhancement in comparison to glioblastomas, H3 K27M-wildtype of midline structures

Cited by 16 publications

References 49 publications

Comparative study of diffuse midline glioma and glioblastoma: Magnetic Resonance Imaging (MRI) in the characteristics and demography

Comparative study of diffuse midline glioma and glioblastoma: Magnetic Resonance Imaging (MRI) in the characteristics and demography

Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques

Differences in the MRI Signature and ADC Values of Diffuse Midline Gliomas with H3 K27M Mutation Compared to Midline Glioblastomas

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