Use of advanced neuroimaging and artificial intelligence in meningiomas

Galldiks, Norbert; Angenstein, Frank; Werner, Jan-Michael; Bauer, Elena; Gutsche, Robin; Fink, Gereon R.; Langen, Karl‐Josef; Lohmann, Philipp

doi:10.1111/bpa.13015

Cited by 12 publications

(7 citation statements)

References 134 publications

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“…In contrast, radiomics-based imaging analyses provides both objective and reliable data as well as high-through-put analyses for large patient series. Over the last years, a number of studies have shown that radiomics-based prediction of high-grade histology and sufficient assessment of the risk of postoperative tumor progression is feasible 12 , 14 , 22 . However, only few studies have focused on radiomics-based prediction of distinct prognostic histological characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, by considering both clinical and histopathological data as well as quantitative MRI imaging data one can gain unprecedented insights into the current disease state or the expected course of a patient's disease 8 – 13 . Concerning meningiomas, a few previous studies have explored the utility of radiomics for the prediction of recurrence in surgically treated meningiomas 14 . Previous studies have already shown that statements regarding the probability of complete surgical resectability of meningiomas can be made by radiomics-based analysis of MRI images 15 .…”

Section: Introductionmentioning

confidence: 99%

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A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

Krähling¹,

Musigmann²,

Akkurt³

et al. 2023

Sci Rep

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The aim of this study was to develop a magnetic resonance imaging (MRI) based radiomics model to predict mitosis cycles in intracranial meningioma grading prior to surgery. Preoperative contrast-enhanced T1-weighted (T1CE) cerebral MRI data of 167 meningioma patients between 2015 and 2020 were obtained, preprocessed and segmented using the 3D Slicer software and the PyRadiomics plugin. In total 145 radiomics features of the T1CE MRI images were computed. The criterion on the basis of which the feature selection was made is whether the number of mitoses per 10 high power field (HPF) is greater than or equal to zero. Our analyses show that machine learning algorithms can be used to make accurate predictions about whether the number of mitoses per 10 HPF is greater than or equal to zero. We obtained our best model using Ridge regression for feature pre-selection, followed by stepwise logistic regression for final model construction. Using independent test data, this model resulted in an AUC (Area under the Curve) of 0.8523, an accuracy of 0.7941, a sensitivity of 0.8182, a specificity of 0.7500 and a Cohen’s Kappa of 0.5576. We analyzed the performance of this model as a function of the number of mitoses per 10 HPF. The model performs well for cases with zero mitoses as well as for cases with more than one mitosis per 10 HPF. The worst model performance (accuracy = 0.6250) is obtained for cases with one mitosis per 10 HPF. Our results show that MRI-based radiomics may be a promising approach to predict the mitosis cycles in intracranial meningioma prior to surgery. Specifically, our approach may offer a non-invasive means of detecting the early stages of a malignant process in meningiomas prior to the onset of clinical symptoms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

Krähling¹,

Musigmann²,

Akkurt³

et al. 2023

Sci Rep

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“…Currently, MRI is the standard of care for the thorough assessment of meningiomas from an imaging standpoint. With further advancement in artificial intelligence, radiomics could play a role in the diagnosis of and classification of meningioma [94]. Radiomics is the use of MRI, CT, or PET/CT to produce mathematical models which allow for a more detailed analysis of bodily structures based on the texture, shape, and intensity of lesions provided by basic imaging [95][96][97].…”

Section: Current Treatment Developments For Meningiomamentioning

confidence: 99%

“…This accuracy can be further increased when radiomics is applied to diffusion-weighted imaging (advanced MRI) [ 102 ]. Radiomics, therefore, presents a new way to assess and diagnose meningiomas [ 94 ].…”

Section: Current Treatment Developments For Meningiomamentioning

confidence: 99%

Review of meningioma diagnosis and management

et al. 2023

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Meningiomas are the most common intracranial tumors in adult patients. Although the majority of meningiomas are diagnosed as benign, approximately 20% of cases are high-grade tumors that require significant clinical treatment. The gold standard for grading central nervous system tumors comes from the World Health Organization Classification of Tumors of the central nervous system. Treatment options also depend on the location, imaging, and histopathological features of the tumor. This review will cover diagnostic strategies for meningiomas, including 2021 updates to the World Health Organization’s grading of meningiomas. Meningioma treatment plans are variable and highly dependent on tumor grading. This review will also update the reader on developments in the treatment of meningiomas, including surgery, radiation therapy and monoclonal antibody treatment.

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“…Radiomics performed on magnetic resonance imaging (MRI) were shown to predict molecular alterations such as IDH1/2 mutations [7,8], 1p19q co-deletion [9], ATRX status [10], MGMT promoter methylation [11] and also TERT promoter mutations [12] in gliomas with remarkable accuracy. In meningiomas, numerous studies analyzed correlations of radiomics features with intraoperative and histological characteristics or prognosis [13,14], while the predictive value for molecular alterations has not been investigated yet. Of note, conventional MRI analyses revealed distinct anatomical distributions of key genetic and epigenetic alterations such as DNA methylation [15] or hotspot mutations including TERT [16][17][18][19] as well as their correlations with further imaging characteristics [16,18,20], indicating the immense potential of imaging data to predict molecular alterations in meningiomas.…”

Section: Introductionmentioning

confidence: 99%

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas

Akkurt,

Spille,

Peetz-Dienhart

et al. 2023

Cancers

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Purpose: In meningiomas, TERT promotor mutations are rare but qualify the diagnosis of anaplasia, directly impacting adjuvant therapy. Effective screening for patients at risk for promotor mutations could enable more targeted molecular analyses and improve diagnosis and treatment. Methods: Semiautomatic segmentation of intracranial grade 2/3 meningiomas was performed on preoperative magnetic resonance imaging. Discriminatory power to predict TERT promoter mutations was analyzed using a random forest algorithm with an increasing number of radiomic features. Two final models with five and eight features with both fixed and differing radiomics features were developed and adjusted to eliminate random effects and to avoid overfitting. Results: A total of 117 image sets including training (N = 94) and test data (N = 23) were analyzed. To eliminate random effects and demonstrate the robustness of our approach, data partitioning and subsequent model development and testing were repeated a total of 100 times (each time with repartitioned training and independent test data). The established five- and eight-feature models with both fixed and different radiomics features enabled the prediction of TERT with similar but excellent performance. The five-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 91.8%/94.3%, mean accuracy of 85.5%/88.9%, mean sensitivity of 88.6%/91.4%, mean specificity of 83.2%/87.0%, and a mean Cohen’s Kappa of 71.0%/77.7%. The eight-feature (different/fixed) model predicted TERT promotor mutation status with a mean AUC of 92.7%/94.6%, mean accuracy of 87.3%/88.9%, mean sensitivity of 89.6%/90.6%, mean specificity of 85.5%/87.5%, and a mean Cohen’s Kappa of 74.4%/77.6%. Of note, the addition of further features of up to N = 8 only slightly increased the performance. Conclusions: Radiomics-based machine learning enables prediction of TERT promotor mutation status in meningiomas with excellent discriminatory performance. Future analyses in larger cohorts should include grade 1 lesions as well as additional molecular alterations.

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Use of advanced neuroimaging and artificial intelligence in meningiomas

Cited by 12 publications

References 134 publications

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma

Review of meningioma diagnosis and management

Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas

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