Imaging Features and Pathological Correlation in Mixed Microcystic and Angiomatous Meningioma: A Case Report

Choi, Keonwoo; Kim, Dae Yoon; Kim, Hyun Jin; Hwang, Gyojun; Kim, Mi Kyung; Paik, Seungmok

doi:10.3348/jksr.2021.0119

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Microcystic meningiomas have been reported as the rarest of all subtypes and were only recognised by the WHO as distinct entities in 1993. 32 These uncommon, benign WHO Grade I meningiomas are characterised by high T2-WI signal intensity, as well as severe peritumoural oedema, mimicking more aggressive neoplasms. 33 Similar features are shared by secretory meningioma subtypes, one of which was encountered.…”

Section: Discussionmentioning

confidence: 99%

Intracranial meningiomas at a tertiary hospital: Spectrum of MRI findings with histopathologic correlation

Pienaar,

Varghese

2024

South African Journal of Radiology

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Background: Intracranial meningiomas consist of a heterogenous group of histological subtypes, some of which are rare. Data that may play an important role in neurosurgical decision-making regarding the incidence and MRI features of these histological subtypes in the South African population groups, are lacking.Objectives: This study aimed to assess the spectrum of MRI findings and histological subtypes of meningiomas in the South African context, with the goal of improving the paucity of literature on the topic.Method: A retrospective review of the MRI features of 41 cases of histologically confirmed intracranial meningiomas was performed at a tertiary hospital level. Imaging features were audited and correlated with histological subtypes during statistical analysis.Results: Eleven different histological subtypes of meningioma were encountered. World Health Organization (WHO) Grade I meningothelial meningiomas were the most common histological subgroup. Overall, meningiomas were found to be predominantly isointense to grey matter on T1-weighted imaging, irrespective of the histological subtype, with greater signal variability on T2-weighted imaging. Morphologies of specific subtypes are in keeping with the literature.Conclusion: Analysis of this series of intracranial meningiomas did not demonstrate statistically significant differences in MRI features between histological subtypes to allow for accurate preoperative prediction of meningioma subtype or WHO grade. This highlights the importance of definitive histopathological diagnosis rather than over-reliance on presumed benign imaging features.Contribution: This original research article discusses the impact of histological subtype on the MRI appearance of intracranial meningiomas, with the aim to improve the paucity of literature on the subject in the context of the South African population.

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Section: Discussionmentioning

confidence: 99%

Intracranial meningiomas at a tertiary hospital: Spectrum of MRI findings with histopathologic correlation

Pienaar,

Varghese

2024

South African Journal of Radiology

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“…Meningiomas can be found on any external surfaces of the brain, including within the ventricular system. This is due to the presence of arachnoid cap cells or meningocytes that were trapped in the cranial sutures during the brain's formation at birth [15]. 3.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Medical Images Using Fuzzy Convolutional Neural Networks

Mahdi,

Shujaa,

Zghair

2023

MMEP

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Brain tumors, characterized by the uncontrolled and rapid proliferation of cells, can result in fatal outcomes if not identified and treated promptly. Consequently, the development of a reliable and automated diagnostic system is of paramount importance. In this study, a Fuzzy Convolutional Neural Network (F-CNN) is employed for the efficient diagnosis of brain tumors (Glioma, Meningioma, Pituitary, and non-tumors), leveraging the computational capabilities of Google Colaboratory. The methodology comprises four stages: pre-processing, training, testing, and evaluation. The pre-processing stage entails rescaling the image, resizing, random flipping, and random rotation. The training phase involves the construction of an intelligent model, encompassing four blocks: convolution, ReLU, batch normalization, and max pooling. This is followed by flattening, a fuzzy inferences layer, and a dense layer with dropout. The model was trained using a Kaggle dataset comprising 7022 brain tumor MRI images and validated with a test set of 470 MRI images sourced from the Neurological Wholesale Hospital in Baghdad. The proposed F-CNN model achieved a high accuracy rate of 99.31% while maintaining low computational complexity and time. This work illustrates the potential of Deep Learning approaches, such as F-CNNs, in enhancing the precision and efficiency of medical imaging diagnostics.

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Tumor Diagnosis against Other Brain Diseases Using T2 MRI Brain Images and CNN Binary Classifier and DWT

et al. 2023

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Purpose: Brain tumors are diagnosed and classified manually and noninvasively by radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may exist due to human factors such as lack of time, fatigue, and relatively low experience. Deep learning methods have become increasingly important in MRI classification. To improve diagnostic accuracy, researchers emphasize the need to develop Computer-Aided Diagnosis (CAD) computational diagnostics based on artificial intelligence (AI) systems by using deep learning methods such as convolutional neural networks (CNN) and improving the performance of CNN by combining it with other data analysis tools such as wavelet transform. In this study, a novel diagnostic framework based on CNN and DWT data analysis is developed for the diagnosis of glioma tumors in the brain, among other tumors and other diseases, with T2-SWI MRI scans. It is a binary CNN classifier that treats the disease “glioma tumor” as positive and the other pathologies as negative, resulting in a very unbalanced binary problem. The study includes a comparative analysis of a CNN trained with wavelet transform data of MRIs instead of their pixel intensity values in order to demonstrate the increased performance of the CNN and DWT analysis in diagnosing brain gliomas. The results of the proposed CNN architecture are also compared with a deep CNN pre-trained on VGG16 transfer learning network and with the SVM machine learning method using DWT knowledge. Methods: To improve the accuracy of the CNN classifier, the proposed CNN model uses as knowledge the spatial and temporal features extracted by converting the original MRI images to the frequency domain by performing Discrete Wavelet Transformation (DWT), instead of the traditionally used original scans in the form of pixel intensities. Moreover, no pre-processing was applied to the original images. The images used are MRIs of type T2-SWI sequences parallel to the axial plane. Firstly, a compression step is applied for each MRI scan applying DWT up to three levels of decomposition. These data are used to train a 2D CNN in order to classify the scans as showing glioma or not. The proposed CNN model is trained on MRI slices originated from 382 various male and female adult patients, showing healthy and pathological images from a selection of diseases (showing glioma, meningioma, pituitary, necrosis, edema, non-enchasing tumor, hemorrhagic foci, edema, ischemic changes, cystic areas, etc.). The images are provided by the database of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) and the Ischemic Stroke Lesion Segmentation (ISLES) challenges on Brain Tumor Segmentation (BraTS) challenges 2016 and 2017, as well as by the numerous records kept in the public general hospital of Chania, Crete, “Saint George”. Results: The proposed frameworks are experimentally evaluated by examining MRI slices originating from 190 different patients (not included in the training set), of which 56% are showing gliomas by the longest two axes less than 2 cm and 44% are showing other pathological effects or healthy cases. Results show convincing performance when using as information the spatial and temporal features extracted by the original scans. With the proposed CNN model and with data in DWT format, we achieved the following statistic percentages: accuracy 0.97, sensitivity (recall) 1, specificity 0.93, precision 0.95, FNR 0, and FPR 0.07. These numbers are higher for this data format (respectively: accuracy by 6% higher, recall by 11%, specificity by 7%, precision by 5%, FNR by 0.1%, and FPR is the same) than it would be, had we used as input data the intensity values of the MRIs (instead of the DWT analysis of the MRIs). Additionally, our study showed that when our CNN takes into account the TL of the existing network VGG, the performance values are lower, as follows: accuracy 0.87, sensitivity (recall) 0.91, specificity 0.84, precision 0.86, FNR of 0.08, and FPR 0.14. Conclusions: The experimental results show the outperformance of the CNN, which is not based on transfer learning, but is using as information the MRI brain scans decomposed into DWT information instead of the pixel intensity of the original scans. The results are promising for the proposed CNN based on DWT knowledge to serve for binary diagnosis of glioma tumors among other tumors and diseases. Moreover, the SVM learning model using DWT data analysis performs with higher accuracy and sensitivity than using pixel values.

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Imaging Features and Pathological Correlation in Mixed Microcystic and Angiomatous Meningioma: A Case Report

Cited by 3 publications

References 8 publications

Intracranial meningiomas at a tertiary hospital: Spectrum of MRI findings with histopathologic correlation

Intracranial meningiomas at a tertiary hospital: Spectrum of MRI findings with histopathologic correlation

Diagnosis of Medical Images Using Fuzzy Convolutional Neural Networks

Tumor Diagnosis against Other Brain Diseases Using T2 MRI Brain Images and CNN Binary Classifier and DWT

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