Advanced MRI shape analysis as a predictor of histologically aggressive supratentorial meningioma

Friconnet, Guillaume; Baudouin, Maxime; Brinjikji, Waleed; Saleme, Suzana; Espíndola, Víctor Hugo; Boncoeur-Martel, Marie Paule; Mounayer, Charbel; Rouchaud, Aymeric

doi:10.1016/j.neurad.2020.12.007

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…On the contrary, the results of our experiment showed that there were no correlations between tumor size and histological grade. Consistent with most studies, this study found that as the histopathological grade of meningioma increased, the tumor shape tended to increase in irregularity, 6,13 which may be due to invasiveness and changes in the growth modes of the tumors. In our study, there was also no correlation between necrosis/cystic degeneration and the histopathological grade, which was inconsistent with the conclusions of other studies 12,14 .…”

Section: Discussionsupporting

confidence: 89%

Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging

Yang

Xiao

Zhou

et al. 2022

J Comput Assist Tomogr

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Objective: To compare conventional magnetic resonance imaging (MRI), susceptibility-weighted imaging (SWI), and perfusion-weighted imaging (PWI) characteristics in different grades of trigone meningiomas.Methods: Thirty patients with trigone meningiomas were enrolled in this retrospective study. Conventional MRI was performed in all patients; SWI (17 cases), dynamic contrast-enhanced PWI (10 cases), and dynamic susceptibility contrast PWI (6 cases) were performed. Demographics, conventional MRI features, SWI-and PWI-derived parameters were compared between different grades of trigone meningiomas.Results: On conventional MRI, the irregularity of tumor shape (ρ = 0.497, P = 0.005) and the extent of peritumoral edema (ρ = 0.187, P = 0.022) might help distinguish low-grade and high-grade trigone meningiomas. On multiparametric functional MRI, rTTP max (1.17 ± 0.06 vs 1.30 ± 0.05, P = 0.048), K ep , V e , and iAUC demonstrated their potentiality to predict World Health Organization grades I, II, and III trigone meningiomas.Conclusions: Conventional MRI combined with dynamic susceptibility contrast and dynamic contrast-enhanced can help predict the World Health Organization grade of trigone meningiomas.

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Section: Discussionsupporting

confidence: 89%

Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging

Yang

Xiao

Zhou

et al. 2022

J Comput Assist Tomogr

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“…Therefore, TERTp mutation, which promotes increased cellular proliferation, may result in increased necrosis. Previous studies showed that 3D fractal parameters improve the prediction of meningioma grade and aggressiveness, and our study goes further by demonstrating that fractal parameters also reflect the status of molecular markers in meningioma [12,36].…”

Section: Plos Onesupporting

confidence: 78%

Three-dimensional fractal dimension and lacunarity features may noninvasively predict TERT promoter mutation status in grade 2 meningiomas

Won

Lee²,

Lee³

et al. 2022

PLoS ONE

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Purpose The 2021 World Health Organization classification includes telomerase reverse transcriptase promoter (TERTp) mutation status as a factor for differentiating meningioma grades. Therefore, preoperative prediction of TERTp mutation may assist in clinical decision making. However, no previous study has applied fractal analysis for TERTp mutation status prediction in meningiomas. The purpose of this study was to assess the utility of three-dimensional (3D) fractal analysis for predicting the TERTp mutation status in grade 2 meningiomas. Methods Forty-eight patients with surgically confirmed grade 2 meningiomas (41 TERTp-wildtype and 7 TERTp-mutant) were included. 3D fractal dimension (FD) and lacunarity values were extracted from the fractal analysis. A predictive model combining clinical, conventional, and fractal parameters was built using logistic regression analysis. Receiver operating characteristic curve analysis was used to assess the ability of the model to predict TERTp mutation status. Results Patients with TERTp-mutant grade 2 meningiomas were older (P = 0.029) and had higher 3D FD (P = 0.026) and lacunarity (P = 0.004) values than patients with TERTp-wildtype grade 2 meningiomas. On multivariable logistic analysis, higher 3D FD values (odds ratio = 32.50, P = 0.039) and higher 3D lacunarity values (odds ratio = 20.54, P = 0.014) were significant predictors of TERTp mutation status. The area under the curve, accuracy, sensitivity, and specificity of the multivariable model were 0.84 (95% confidence interval 0.71–0.93), 83.3%, 71.4%, and 85.4%, respectively. Conclusion 3D FD and lacunarity may be useful imaging biomarkers for predicting TERTp mutation status in grade 2 meningiomas.

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“…These features are independent of the gray level intensity distribution of ROI. Several clinical trial have demonstrated that shape features extracted from MRI serve as informative imaging biomarkers for predicting high WHO grade and histological brain invasion in meningioma [ 50 , 51 ].…”

Section: Discussionmentioning

confidence: 99%

Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma

Iglseder,

Beliveau

et al. 2023

J Neurooncol

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Objective This retrospective study aimed to analyse the correlation between somatostatin receptor subtypes (SSTR 1–5) and maximum standardized uptake value (SUVmax) in meningioma patients using Gallium-68 DOTA-D-Phe1-Tyr3-octreotide Positron Emission Tomography ([68Ga]Ga-DOTATOC PET). Secondly, we developed a radiomic model based on apparent diffusion coefficient (ADC) maps derived from diffusion weighted magnetic resonance images (DWI MRI) to reproduce SUVmax. Method The study included 51 patients who underwent MRI and [68Ga]Ga-DOTATOC PET before meningioma surgery. SUVmax values were quantified from PET images and tumour areas were segmented on post-contrast T1-weighted MRI and mapped to ADC maps. A total of 1940 radiomic features were extracted from the tumour area on each ADC map. A random forest regression model was trained to predict SUVmax and the model’s performance was evaluated using repeated nested cross-validation. The expression of SSTR subtypes was quantified in 18 surgical specimens and compared to SUVmax values. Results The random forest regression model successfully predicted SUVmax values with a significant correlation observed in all 100 repeats (p < 0.05). The mean Pearson’s r was 0.42 ± 0.07 SD, and the root mean square error (RMSE) was 28.46 ± 0.16. SSTR subtypes 2A, 2B, and 5 showed significant correlations with SUVmax values (p < 0.001, R2 = 0.669; p = 0.001, R2 = 0.393; and p = 0.012, R2 = 0.235, respectively). Conclusion SSTR subtypes 2A, 2B, and 5 correlated significantly with SUVmax in meningioma patients. The developed radiomic model based on ADC maps effectively reproduces SUVmax using [68Ga]Ga-DOTATOC PET.

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Advanced MRI shape analysis as a predictor of histologically aggressive supratentorial meningioma

Cited by 5 publications

References 21 publications

Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging

Grading Trigone Meningiomas Using Conventional Magnetic Resonance Imaging With Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging

Three-dimensional fractal dimension and lacunarity features may noninvasively predict TERT promoter mutation status in grade 2 meningiomas

Somatostatin receptor subtype expression and radiomics from DWI-MRI represent SUV of [68Ga]Ga-DOTATOC PET in patients with meningioma

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