Meningioma and psychiatric symptoms: An individual patient data analysis

Gyawali, Shreeya; Sharma, Pallavi; Mahapatra, Ananya

doi:10.1016/j.ajp.2019.03.029

Cited by 17 publications

(15 citation statements)

References 50 publications

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“…The clinical presentation of meningiomas depends on the site affected, commonly associated with cephalea secondary to tumor growth, focal neurological deficit, convulsions directly or indirectly associated with the tumor, and changes in vision and behavior. 12 13 The case studies presented by Gyawali et al 16 demonstrate that one of the more common sites for the development of meningiomas, in general, is the frontal lobe, and patients may develop psychiatric symptoms such as severe depression and, in more serious cases, Godot syndrome, which is characterized by the association of accentuated symptoms of anxiety and dementia. Obsessive symptoms are more frequently linked to lesions situated in the left frontal lobe, while the involvement of the right frontal lobe is more commonly related to bipolar disorders, alcohol abuse, and visual and auditory hallucinations.…”

Section: Resultsmentioning

confidence: 99%

“…Tumors at the base of the skull usually present a low K i -67 index. 16 Progesterone receptors are less prominent in grade-II meningiomas and may have an inverse relationship with K i -67 rates. 18 Bromodeoxyuridine (BrdU) has a close relationship with the rates of tumor recurrence: when greater than 5%, a 100% probability of recurrence is denoted; moreover, the high a A foundation within the Brazilian Ministry of Education whose central purpose is to coordinate efforts to improve the quality of Brazil's faculty and staff in higher education through grant programs.…”

Section: Anatomical Pathologymentioning

confidence: 99%

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Peculiarities of Atypical Meningiomas: Literature Review

Santos

Rampazzo

Zicarelli

et al. 2022

Arquivos Brasileiros de Neurocirurgia: Brazilian Neurosurgery

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Introduction Meningiomas are common tumors of the central nervous system that represent around 30% of primary tumors. However, the incidence of atypical meningiomas (AMs) is lower, of approximately 15% of all meningiomas, and they present high rates of relapse and mortality. Aim To review peculiarities of AMs. Methodology A literature review of articles published in English between 2009 and 2020 on the PubMed and Biblioteca Virtual em Saúde (BVS) databases using the terms meningioma and atypical. Results Atypical meningiomas are rare and more prevalent in older males. The clinical condition depends on the site of the tumor, and a definitive diagnosis of AM is only possible via anatomical pathology. Moreover, prominent studies have indicated a relationship between the presence of the anti-Ki67 antibody/mindbomb (Ki-67/MIB1) marker as an aid in the definition of AM and one of the determinants of tumor aggressiveness. Imaging studies have also advanced in terms of seeking criteria using magnetic resonance that may suggest the malignancy of a lesion. As far as treatment is concerned, total resection remains the main therapeutic option, and it has a direct relationship with survival and time until disease progression. Lastly, there are many factors involved in the prognosis of AM. Conclusion Atypical meningiomas continue to be a challenge, requiring further and more specific studies to provide a better understanding of it.

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Section: Resultsmentioning

confidence: 99%

Section: Anatomical Pathologymentioning

confidence: 99%

Peculiarities of Atypical Meningiomas: Literature Review

Santos

Rampazzo

Zicarelli

et al. 2022

Arquivos Brasileiros de Neurocirurgia: Brazilian Neurosurgery

View full text Add to dashboard Cite

show abstract

“…Most meningiomas occur between the ages of 30 years and 70 years and rarely occur in children [3]. Frontal meningiomas commonly demonstrate psychiatric symptoms, including mood disorders, psychosis, memory issues, personality changes, anxiety, and anorexia nervosa [3,5,6]. As shown in Cases 1 and 2, being elderly or a menopausal woman can delay or prevent the search for the cause of cognitive dysfunction caused by frontal meningioma.…”

Section: Discussionmentioning

confidence: 99%

Neuroimaging Recommendations in Outpatients With Dementia: Three Cases of Frontal Meningioma Demonstrating Reversible Dementia

Matsuo¹,

Amano²,

Miyamatsu³

et al. 2021

Cureus

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Benign brain tumors largely affect the brain and can lead to reversible dementia, which can be resolved following the treatment of the primary etiology. Herein, we report three cases of relatively large frontal meningiomas in patients who presented with cognitive impairment as initial symptoms. The three participants demonstrated notable dementia alongside frontal meningioma. Following resection, all patients showed dramatic cognitive function improvement, and they successfully returned to society. Our cases illustrate the benefit of active surveillance with neuroimaging in selected patients, especially those who present with acute or subacute dementia.

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“…Benign tumors, including meningioma and pituitary tumors, are slow-growing and typically cause no symptoms. However, neuropsychiatric symptoms such as anxiety, psychosis, personality changes, memory disturbances, or anorexia nervosa are common in patients with meningioma [ 6 ]. When only psychiatric symptoms are present, the diagnosis of meningioma could be delayed.…”

Section: Introductionmentioning

confidence: 99%

“…Meningioma and almost all benign tumors are more likely to cause psychiatric symptoms and behavioral manifestations in individuals [ 7 ]. Gyawali et al in [ 6 ] emphasize the need for neurological evaluation and neuroimaging in psychiatric patients, particularly those with unusual symptoms. Similarly, fatigue, seizures, edema, endocrinopathy, and psychiatric disorders are symptoms commonly found in patients with glioma tumors [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

et al. 2022

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Brain tumors are a pernicious cancer with one of the lowest five-year survival rates. Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain tumor. Automated computer-assisted tools can help them speed up the diagnosis process and reduce the burden on the health care systems. Recent advances in deep learning for medical imaging have shown remarkable results, especially in the automatic and instant diagnosis of various cancers. However, we need a large amount of data (images) to train the deep learning models in order to obtain good results. Large public datasets are rare in medicine. This paper proposes a framework based on unsupervised deep generative neural networks to solve this limitation. We combine two generative models in the proposed framework: variational autoencoders (VAEs) and generative adversarial networks (GANs). We swap the encoder–decoder network after initially training it on the training set of available MR images. The output of this swapped network is a noise vector that has information of the image manifold, and the cascaded generative adversarial network samples the input from this informative noise vector instead of random Gaussian noise. The proposed method helps the GAN to avoid mode collapse and generate realistic-looking brain tumor magnetic resonance images. These artificially generated images could solve the limitation of small medical datasets up to a reasonable extent and help the deep learning models perform acceptably. We used the ResNet50 as a classifier, and the artificially generated brain tumor images are used to augment the real and available images during the classifier training. We compared the classification results with several existing studies and state-of-the-art machine learning models. Our proposed methodology noticeably achieved better results. By using brain tumor images generated artificially by our proposed method, the classification average accuracy improved from 72.63% to 96.25%. For the most severe class of brain tumor, glioma, we achieved 0.769, 0.837, 0.833, and 0.80 values for recall, specificity, precision, and F1-score, respectively. The proposed generative model framework could be used to generate medical images in any domain, including PET (positron emission tomography) and MRI scans of various parts of the body, and the results show that it could be a useful clinical tool for medical experts.

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Meningioma and psychiatric symptoms: An individual patient data analysis

Cited by 17 publications

References 50 publications

Peculiarities of Atypical Meningiomas: Literature Review

Peculiarities of Atypical Meningiomas: Literature Review

Neuroimaging Recommendations in Outpatients With Dementia: Three Cases of Frontal Meningioma Demonstrating Reversible Dementia

Brain Tumor Classification Using a Combination of Variational Autoencoders and Generative Adversarial Networks

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