Meningiomas

Perry, Arie

doi:10.1016/b978-0-323-44941-0.00013-8

Cited by 15 publications

(7 citation statements)

References 151 publications

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“…In human grading, mitotic cut‐offs were reported to be 4 and 20 mitoses in 1.60 mm 238,39 . Nevertheless, in most papers, and in the application of human grading to dog meningioma, the 1.60 mm 2 surface area was replaced by the more commonly used 10 high power fields (HPFs, old cut‐off), 12,33–35,40–42 creating reduced reproducibility in human and veterinary medicine 22,43 .…”

Section: Discussionmentioning

confidence: 99%

Standardization of canine meningioma grading: Validation of new guidelines for reproducible histopathologic criteria

Belluco,

Marano,

Lurier

et al. 2023

Vet Comparative Oncology

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Canine meningiomas are currently graded using the human grading system. Recently published guidelines have adapted the human grading system for use in dogs. The goal of this study was to validate the new guidelines for canine meningiomas. To evaluate the inter‐observer agreement, 5 veterinary surgical pathologists graded 158 canine meningiomas following the human grading system alone or with the new guidelines. The inter‐observer agreement for histologic grade and each of the grading criteria (mitotic grade, invasion, spontaneous necrosis, macronucleoli, small cells, hypercellularity, pattern loss and anaplasia) was evaluated using the Fleiss kappa index. The diagnostic accuracy (sensitivity and specificity) was assessed by comparing the diagnoses obtained with the 2 grading systems with a consensus grade (considered the reference classification). The consensus histologic grade was obtained by agreement between 4 experienced veterinary neuropathologists following the guidelines. Compared with the human grading alone, the canine‐specific guidelines increased the inter‐observer agreement for: histologic grade (κ = 0.52); invasion (κ = 0.67); necrosis (κ = 0.62); small cells (κ = 0.36); pattern loss (κ = 0.49) and anaplasia (κ = 0.55). Mitotic grade agreement remained substantial (κ = 0.63). The guidelines improved the sensitivity in identifying grade 1 (95.6%) and the specificity in identifying grade 2 (96.2%) meningiomas. In conclusion, the new grading guidelines for canine meningiomas are associated with an overall improvement in the inter‐observer agreement and higher diagnostic accuracy in diagnosing grade 1 and grade 2 meningiomas.

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Section: Discussionmentioning

confidence: 99%

Standardization of canine meningioma grading: Validation of new guidelines for reproducible histopathologic criteria

Belluco,

Marano,

Lurier

et al. 2023

Vet Comparative Oncology

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“…Meningiomas are the second most common type of primary brain tumor, accounting for around thirty percent of all CNS tumors. Meningioma is typically noncancerous in early stages and occurs in the meninges, which are the membranes surrounding the brain and spinal cord [10, 55, 68]. For the purposes of this work, data contains meningioma tumors only are present in the brain.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Meningioma can cause seizures or loss of sensory function, depending on the localization of the tumor. Because meningioma is present throughout the brain and sometimes in the spinal cord, it can also cause memory loss, motor loss, and other neurode-generative issues [55]. For this reason, although benign meningiomas are typically non-cancerous and slow growing, physicians recommend immediate surgery.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Multimodal CNN Models Allow for the Accurate Classification and Grading of Preoperative Meningioma Tumors

Rane

2023

Preprint

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Magnetic resonance imaging (MRI) scanning and Computed Tomography (CT) proves to be a reliable form of imaging for modern medical use, providing clear images for physician and radiologist diagnosis. MRI and CT scans are especially important for neuroimaging of tumors for neuro-oncology after a patient lists symptoms indicating brain cancer. Although imaging does produce a lucid depiction of possible cancerous growth in the brain, inspection by a physician could be challenging due to subtleties in the image or human error. A diagnosis could also never be exact, as a biopsy is the only diagnostic test that can ascertain meningioma growth. A physician could confuse a noncancerous cyst located near the meninges of the brain for a meningioma tumor. Furthermore, World Health Organization (WHO) grading of each tumor could be complicated to differentiate. One possible solution to the human handicap is a Convolutional Neural Network (CNN), a commonly used machine learning method for image extrapolation and classification. For the purposes of this primary research, a multimodal CNN was given testing and training data of different types of brain cancers to test if it could properly classify different forms of CT and MRI scans of meningioma compared to glioma, pituitary, and scans with no tumor. The no tumor dataset included noncancerous cysts, as mentioned before, that could be confused with meningioma. Furthermore, a separate CNN was given different testing and training data on meningioma tumors with WHO grades one to three. The CNNs were run on a private GPU environment on Visual Studio Jupyter Notebook and were given input data in the form of standardized JPEG image files from research institutes around the world. The patient data came from various ages, different nationalities, and both genders. The concept of transfer learning was used to train the model, where the solution to one problem is used to solve another problem. The results of the models show high accuracies above 98\% with an upward trend through the twelve epochs ran, indicating stability. The recall and precision scores were also high, indicating quality. Finally, the AUC scores were all above .99, describing the CNN's capability to include threshold-invariance and scale-invariance. Finally, an attention study demonstrated the CNN's tendency to apply most attention to the tumor mass itself rather than extraneous variables.

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“…Meningiomas originate from arachnoid cap cells and are the most common primary central nervous system (CNS) tumors ( Perry, 2018 ). Based on their histopathological features, the 2021 World Health Organization (WHO) CNS tumor grading criteria classified meningiomas into three histological grades: grade I (benign), grade II (atypical), and grade III (anaplastic) tumors ( Louis et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

LncRNA-IMAT1 Promotes Invasion of Meningiomas by Suppressing KLF4/hsa-miR22-3p/Snai1 Pathway

Ding

Wang

et al. 2022

Molecules and Cells

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Malignant meningiomas often show invasive growth that makes complete tumor resection challenging, and they are more prone to recur after radical resection. Invasive meningioma associated transcript 1 (IMAT1) is a long noncoding RNA located on Homo sapiens chromosome 17 that was identified by our team based on absolute expression differences in invasive and non-invasive meningiomas. Our studies indicated that IMAT1 was highly expressed in invasive meningiomas compared with non-invasive meningiomas. In vitro studies showed that IMAT1 promoted meningioma cell invasion through the inactivation of the Krüppel-like factor 4 (KLF4)/hsa-miR22-3p/Snai1 pathway by acting as a sponge for hsa-miR22-3p, and IMAT1 knockdown effectively restored the tumor suppressive properties of KLF4 by preserving its tumor suppressor pathway. In vivo experiments confirmed that IMAT1 silencing could significantly inhibit the growth of subcutaneous tumors and prolong the survival period of tumor-bearing mice. Our findings demonstrated that the high expression of IMAT1 is the inherent reason for the loss of the tumor suppressive properties of KLF4 during meningioma progression. Therefore, we believe that IMAT1 may be a potential biological marker and treatment target for meningiomas.

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Meningiomas

Cited by 15 publications

References 151 publications

Standardization of canine meningioma grading: Validation of new guidelines for reproducible histopathologic criteria

Standardization of canine meningioma grading: Validation of new guidelines for reproducible histopathologic criteria

Multimodal CNN Models Allow for the Accurate Classification and Grading of Preoperative Meningioma Tumors

LncRNA-IMAT1 Promotes Invasion of Meningiomas by Suppressing KLF4/hsa-miR22-3p/Snai1 Pathway

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