Pathology Of Brain Tumors Research Articles

Updates in Microglial Research with Respect to Brain Cancer

Microglia resides in the microenvironment of the central nervous system (CNS) and is thought to play a key role in the development and progression of brain cancer. This is because it was shown that microglia comprised a large portion of the total brain tumour mass. Besides, the origin of microglia cells in brain tumours is worth understanding as it is important to distinguish the resident macrophages from the circulating macrophages when discussing the pathology of brain tumours. Activated microgliosis has been linked to increased inflammatory mediators like cytokines, growth factors, matrix metalloproteinases (MMPs) and many more, which would facilitate tumourigenesis. Brain tumour cells also proliferate under the influence of signalling pathways, such as the toll-like receptor 2 signals. Vascular endothelial growth factor (VEGF) which is an angiogenic factor aids in the growth of tumour cells. Brain cancer cells rely on suppressing the effector arm of immune system to evade attacks by downregulating major histocompatibility complexes (MHC) class II molecules and inducing the conversion of microglia to an immunosuppressive phenotype. Glioblastoma stem cells (GSCs) that give rise to brain cancers communicate with microglia cells, which determines their growth and invasion potential. Understanding the molecular interactions of brain cancer and microglia cells would help unlock novel treatments via means of immunotherapy, immunosuppressants and utilising microglia cells to deliver nanoparticle drugs to effectively target and treat brain cancer.

Development of microsurgical forceps equipped with haptic technology for in situ differentiation of brain tumors during microsurgery

The stiffness of human cancers may be correlated with their pathology, and can be used as a biomarker for diagnosis, malignancy prediction, molecular expression, and postoperative complications. Neurosurgeons perform tumor resection based on tactile sensations. However, it takes years of surgical experience to appropriately distinguish brain tumors from surrounding parenchymal tissue. Haptics is a technology related to the touch sensation. Haptic technology can amplify, transmit, record, and reproduce real sensations, and the physical properties (e.g., stiffness) of an object can be quantified. In the present study, glioblastoma (SF126-firefly luciferase-mCherry [FmC], U87-FmC, U251-FmC) and malignant meningioma (IOMM-Lee-FmC, HKBMM-FmC) cell lines were transplanted into nude mice, and the stiffness of tumors and normal brain tissues were measured using our newly developed surgical forceps equipped with haptic technology. We found that all five brain tumor tissues were stiffer than normal brain tissue (p < 0.001), and that brain tumor pathology (three types of glioblastomas, two types of malignant meningioma) was significantly stiffer than normal brain tissue (p < 0.001 for all). Our findings suggest that tissue stiffness may be a useful marker to distinguish brain tumors from surrounding parenchymal tissue during microsurgery, and that haptic forceps may help neurosurgeons to sense minute changes in tissue stiffness.

Patient-Centered Management of Brain Tumor-Related Epilepsy.

Brain tumor-related epilepsy is a heterogenous syndrome involving variability in incidence, timing, pathophysiology, and clinical risk factors for seizures across different brain tumor pathologies. Seizure risk and disability are dynamic over the course of disease and influenced by tumor-directed treatments, necessitating individualized patient-centered management strategies to optimize quality of life. Recent translational findings in diffuse gliomas indicate a dynamic bidirectional relationship between glioma growth and hyperexcitability. Certain non-invasive measures of hyperexcitability are correlated with survival outcomes, however it remains uncertain how to define and measure clinically relevant hyperexcitability serially over time. The extent of resection, timing of pre-operative and/or post-operative seizures, and the likelihood of tumor progression are critical factors impacting the risk of seizure recurrence. Newer anti-seizure medications are generally well-tolerated with similar efficacy in this population, and several rapid-onset seizure rescue agents are in development and available. Seizures in patients with brain tumors are strongly influenced by the underlying tumor biology and treatment. An improved understanding of the interactions between tumor cells and the spectrum of hyperexcitability will facilitate targeted therapies. Multidisciplinary management of seizures should occur with consideration of tumor-directed therapy and prognosis, and anti-seizure medication decision-making tailored to the individual priorities and quality of life of the patient.

Histopathological correlation of brain tumor recurrence vs. radiation effect post-radiosurgery as detected by MRI contrast clearance analysis: a validation study.

The differentiation between adverse radiation effects (ARE) and tumor recurrence or progression (TRP) is a major decision-making point in the follow-up of patients with brain tumors. The advent of immunotherapy, targeted therapy and radiosurgery has made this distinction difficult to achieve in several clinical situations. Contrast clearance analysis (CCA) is a useful technique that can inform clinical decisions but has so far only been histologically validated in the context of high-grade gliomas. This is a series of 7 patients, treated between 2018 and 2023, for various brain pathologies including brain metastasis, atypical meningioma, and high-grade glioma. MRI with contrast clearance analysis was used to inform clinical decisions and patients underwent surgical resection as indicated. The histopathology findings were compared with the CCA findings in all cases. All seven patients had been treated with gamma knife radiosurgery and were followed up with periodic MR imaging. All patients underwent CCA when the necessity to distinguish tumor recurrence from radiation necrosis arose, and subsequently underwent surgery as indicated. Concordance of CCA findings with histological findings was found in all cases (100%). Based on prior studies on GBM and the surgical findings in our series, delayed contrast extravasation MRI findings correlate well with histopathology across a wide spectrum of brain tumor pathologies. CCA can provide a quick diagnosis and have a direct impact on patients' treatment and outcomes.

Preface for Brain Tumor Pathology vol.41 issue 2 : (Special issue for the 41st Annual Meeting of the Japan Society of Brain Tumor Pathology).

Preface for Brain Tumor Pathology vol.41 issue 2 : (Special issue for the 41st Annual Meeting of the Japan Society of Brain Tumor Pathology).

NeuroML - Brain Tumor Classification using Machine Learning and Deep Learning

In this study we have proposed CNN model's efficacy in accurately classifying brain tumors into meningioma, glioma, and pituitary tumor categories, showcasing high sensitivity and specificity. The incorporation of deep learning techniques empowers the model to discern subtle and intricate patterns, contributing to heightened diagnostic precision. This study underscores the potential of advanced machine learning algorithms in medical imaging for specific brain tumor classification, offering a valuable tool for healthcare professionals. The research findings hold promise for improving the accuracy of neuro-oncological diagnoses, ultimately advancing patient care in the domain of brain tumor pathology. The study utilizes a diverse dataset comprising magnetic resonance imaging (MRI) scans of the brain, encompassing various tumor types and conditions. The preprocessing phase involves standardizing and augmenting the dataset to ensure optimal model training. A Convolutional Neural Network architecture is designed to automatically learn discriminative features from the input images, capturing intricate patterns indicative of tumor presence. Results demonstrate the proposed CNN model's efficacy in accurately classifying brain tumors into meningioma, glioma, and pituitary tumor categories, showcasing high sensitivity and specificity. The incorporation of deep learning techniques empowers the model to discern subtle and intricate patterns, contributing to heightened diagnostic precision. This study underscores the potential of advanced machine learning algorithms in medical imaging for specific brain tumor classification, offering a valuable tool for healthcare professionals. The research findings hold promise for improving the accuracy of neuro-oncological diagnoses, ultimately advancing patient care in the domain of brain tumor pathology. In our work, CNN gained an accuracy of 99.3 %, which is very compelling. The main aim of this paper is to distinguish between normal and abnormal pixels, based on texture based and statistical based features

SURG-01. DIFFERENTIATION OF GLIOBLASTOMA AND MALIGNANT MENINGIOMA FROM NORMAL BRAIN BY TISSUE STIFFNESS UTILIZING MICROSURGICAL FORCEPS EQUIPPED WITH HAPTICS TECHNOLOGY

Abstract Stiffness of human cancers strongly correlate with their pathology, and it is reported that stiffness can be used as a biomarker. Furthermore, comprehensive analysis has suggested the usefulness of cancer stiffness in diagnosis and malignancy prediction and its association with molecular expression, as well as its potential for predicting and diagnosing postoperative complications. Neurosurgeons perform tumor resection relying on the sensations that they feel with their own fingertips (tactile sensation), but it takes years of surgical experience to accurately distinguish brain tumor from the surrounding normal brain tissue. Haptics is a new technology regarding touching sensation. Especially, our technology, real haptics, can communicate, record, and reproduce the real sensation. It is also possible to measure and analyze the physical property such as stiffness of the touching object from the obtained haptic data. We developed a surgical forceps equipped with haptics technology to distinguish brain tumor tissue from surrounding normal brain tissue. three glioblastoma cell lines (SF126, U87, U251) and a malignant meningioma cell line (IOMM-Lee) were each transplanted intracranially into 5 nude mice. Tumor size was periodically monitored and mice were euthanized at appropriate time points. The stiffness of the tumor and normal brain tissue was measured using a master-slave integrated surgical forceps prototype. The results statistically showed that all four types of brain tumor tissues were stiffer than normal brain tissues. (p = 0.001) In addition, malignant meningioma was statistically stiffer than glioblastomas. (p = 0.025) This result is consistent with actual surgical sensations. We have shown that our surgical forceps has the ability to distinguish pathology of brain tumors as well as to distinguish brain tumor from normal brain tissue. The forceps is also helpful for the neurosurgeons to sense minute change in tissue stiffness.

A multimodal medical image contrastive learning algorithm with domain adaptive denormalization

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.

529 Neuro-Oncology Practice Patterns and Management Paradigms: A Global Epidemiological Survey

INTRODUCTION: Understanding of the epidemiology and biology of pediatric central nervous system (CNS) tumors has advanced dramatically over the last decade; however there remains a discrepancy in the understanding of epidemiologic and clinical capacity-oriented data between high income and low middle- or low-income countries. METHODS: A cross sectional epidemiological survey was conducted to determine the incidence, diagnosis, management, and follow-up paradigm for pediatric CNS tumors using REDCap. The survey was administered to neurosurgeons at partner hospitals, collecting aggregate data for patients managed over the preceding 3-months. The survey was sent to the sites 3 times at 3-months interval. Descriptive statistics analyses were performed. RESULTS: Fourteen percent of outpatient and 13% of operative volume was represented by pediatric neuro-oncology at the partner sites. Only one site had no diagnostic modality available at, others had either CT scan only or both CT scan and MRI. Of 214 patients evaluated, 128 (59.8%) underwent surgery and 16 (7.5%) were referred to other centers. Mean 29.2% patients did not undergo surgery due to inadequate resources. Mean 44.4% received adjuvant chemotherapy and 27.6% received adjuvant radiotherapy when indicated. The most common tumor location was posterior fossa (26.7%). The most common pathology diagnosis was low grade glioma (17.4%). None of the six partner sites had neuropathology expertise. CONCLUSIONS: A wide variety of pediatric brain and spine tumor pathology was managed at these six national referral sites in Sub-Saharan African countries. The histopathological profile of the tumors managed at these sites was akin to previously published studies. Further resource allocation focusing on setting up pediatric neuro-neuro-oncology practices might improve care for these patients.

Preface for Brain Tumor Pathology vol.40 issue 2

Preface for Brain Tumor Pathology vol.40 issue 2

Three-dimensional visualization of human brain tumors using the CUBIC technique.

Application of tissue clearing techniques on human brain tumors is still limited. This study was to investigate the application of CUBIC on 3D pathological studies of human brain tumors. Brain tumor specimens derived from 21 patients were cleared with CUBIC. Immunostaining was conducted on cleared specimens to label astrocytes, microglia and microvessels, respectively. All tumor specimens achieved transparency after clearing. Immunostaining and CUBIC are well compatible in a variety of human brain tumors. Spatial morphologies of microvessels, astrocytes and microglia of tumors were clearly visualized in 3D, and their 3D morphological parameters were easily quantified. By comparing the quantitative morphological parameters of microvessels among brain tumors of different malignancy, we found that mean vascular diameter was positively correlated with tumor malignancy. Our study demonstrates that CUBIC can be successfully applied to 3D pathological studies of various human brain tumors, and 3D studies of human brain tumors hold great promise in helping us better understand brain tumor pathology in the future.

DIFFUSE LEPTOMENINGEAL GLIONEURONAL TUMOR: A RARE CAUSE OF MALIGNANT PLEURAL EFFUSION

DIFFUSE LEPTOMENINGEAL GLIONEURONAL TUMOR: A RARE CAUSE OF MALIGNANT PLEURAL EFFUSION

P-Rex1 Signaling Hub in Lower Grade Glioma Patients, Found by In Silico Data Mining, Correlates With Reduced Survival and Augmented Immune Tumor Microenvironment.

Systematic analysis of tumor transcriptomes, combined with deep genome sequencing and detailed clinical assessment of hundreds of patients, constitutes a powerful strategy aimed to identify potential biomarkers and therapeutic targets to guide personalized treatments. Oncogenic signaling cascades are integrated by multidomain effector proteins such as P-Rex1, a guanine nucleotide exchange factor for the Rac GTPase (RacGEF), known to promote metastatic dissemination of cancer cells. We hypothesized that patients with high P-Rex1 expression and reduced survival might be characterized by a particular set of signaling proteins co-expressed with this effector of cell migration as a central component of a putative signaling hub indicative of poor prognosis. High P-Rex1 expression correlated with reduced survival of TCGA Lower Grade Glioma (LGG) patients. Thus, guided by PREX1 expression, we searched for signaling partners of this RacGEF by applying a systematic unbiased in silico data mining strategy. We identified 30 putative signaling partners that also correlated with reduced patient survival. These included GPCRs such as CXCR3, GPR82, FZD6, as well as MAP3K1, MAP2K3, NEK8, DYRK3 and RPS6KA3 kinases, and PTPN2 and PTPN22 phosphatases, among other transcripts of signaling proteins and phospho-substrates. This PREX1 signaling hub signature correlated with increased risk of shorter survival of LGG patients from independent datasets and coincided with immune and endothelial transcriptomic signatures, indicating that myeloid infiltration and tumor angiogenesis might contribute to worsen brain tumor pathology. In conclusion, P-Rex1 and its putative signaling partners in LGG are indicative of a signaling landscape of the tumor microenvironment that correlates with poor prognosis and might guide the characterization of signaling targets leading the eventual development of immunotherapeutic strategies.

Folker Hanefeld, 1937-2022.

Folker Hanefeld was born and grew up in Meiβen in Saxony/East Germany. After finishing school and because he was not allowed to study in East- Germany (GDR) he went to Cologne University, West Germany, where he studied Medicine from 1956 to 1961. In Cologne he also wrote his doctoral thesis at the Max-Planck-Institut für Hirnforschung (Max-Planck-institute for brain research) on a topic in experimental brain tumor pathology.

A Novel Method Based on GAN Using a Segmentation Module for Oligodendroglioma Pathological Image Generation.

Digital pathology analysis using deep learning has been the subject of several studies. As with other medical data, pathological data are not easily obtained. Because deep learning-based image analysis requires large amounts of data, augmentation techniques are used to increase the size of pathological datasets. This study proposes a novel method for synthesizing brain tumor pathology data using a generative model. For image synthesis, we used embedding features extracted from a segmentation module in a general generative model. We also introduce a simple solution for training a segmentation model in an environment in which the masked label of the training dataset is not supplied. As a result of this experiment, the proposed method did not make great progress in quantitative metrics but showed improved results in the confusion rate of more than 70 subjects and the quality of the visual output.

Preface for Brain Tumor Pathology vol. 39 issue 2

Preface for Brain Tumor Pathology vol. 39 issue 2

Central Nervous System Tumors in Children.

Central Nervous System Tumors in Children.

NI-16 Verification of APT image and relationship with T2/FLAIR mismatch sign in WHO2016 brain tumor pathology classification

Introduction: Amide Proton Transfer Imaging(APT)is an MRI imaging method that images the increased concentration of amide groups in tumors and is expected to be clinically applied to the diagnostic imaging of gliomas.On the other hand,T2/FLAIR mismatch sign(T2/FLms)has been proposed as an MRI finding specific to astrocytoma with IDH gene mutation.This time,in the WHO2016 Brain Tumor Pathological Classification,we report the verification of the pathological gene classification of APT and the retrospective verification based on the pathological diagnosis results of whether there is a relationship between APT and T2/FLms. Method: We examined 88 cases of preoperative glioma (Grade:G2/3/4)in which APT/T2/FLAIR was imaged.resultIt showed a high value in high malignancy and a significant difference was observed.In the verification of genetic classification, the measured APT values were 1.91 ±0.71 for oligodendroglioma(16 cases),2.58±0.17 for astrocytoma(2 cases),2.40±0.90 for anaplastic oligodendroglioma(12 cases),Anaplastic astrocytoma(20 cases)2.63±0.42,The oligodendroglioma system showed lower values than the astrocytoma system.For anaplastic astrocytoma IDH mutant and glioblastoma IDH mutant,APT measurement values were measured after evaluating the presence or absence of T2/FL ms. APT measured values are anaplastic astrocytoma IDH mutant T2/FL ms present(7 cases) 2.63±0.38,T2/FL ms not (5 cases) 2.76±0.37, glioblastoma IDH mutant T2/FL ms present(5 cases)2.67±0.50, no T2/FL ms(3 cases)3.48±0.27,suggesting low APT measured values with T2/FL ms,respectively.ConclusionIn the verification of genetic classification, the oligodendroglioma system shows a lower value than the astrocytoma system,and it is considered that it can be one of the options such as treatment policy.Regarding the relationship between T2/FL ms and APT,it was suggested that the APT measured value with T2/FL ms tended to be low,but since it wasreported that the sensitivity of T2/FL ms was 30%,it was verified by accumulating cases.is required.

APLN/APLNR Signaling Controls Key Pathological Parameters of Glioblastoma.

Simple SummaryThe neurovascular peptide Apelin and its receptor APLNR are upregulated during glioblastoma pathology. Here we summarize their role in the brain tumor microenvironment composed of neurons, astrocytes, and the vascular and immune systems. Targeting APLN/APLNR signaling promises to unfold multimodal actions in future GBM therapy, acting as an anti-angiogenic and an anti-invasive treatment, and offering the possibility to reduce neurological symptoms and increase overall survival simultaneously.Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adults. GBM-expansion depends on a dense vascular network and, coherently, GBMs are highly angiogenic. However, new intratumoral blood vessels are often aberrant with consequences for blood-flow and vascular barrier function. Hence, the delivery of chemotherapeutics into GBM can be compromised. Furthermore, leaky vessels support edema-formation, which can result in severe neurological deficits. The secreted signaling peptide Apelin (APLN) plays an important role in the formation of GBM blood vessels. Both APLN and the Apelin receptor (APLNR) are upregulated in GBM cells and control tumor cell invasiveness. Here we summarize the current evidence on the role of APLN/APLNR signaling during brain tumor pathology. We show that targeting APLN/APLNR can induce anti-angiogenic effects in GBM and simultaneously blunt GBM cell infiltration. In addition, we discuss how manipulation of APLN/APLNR signaling in GBM leads to the normalization of tumor vessels and thereby supports chemotherapy, reduces edema, and improves anti-tumorigenic immune reactions. Hence, therapeutic targeting of APLN/APLNR signaling offers an interesting option to address different pathological hallmarks of GBM.

HGG-03. THE GLYCOSPHINGOLIPIDS METABOLISM IS A NOVEL TARGET IN H3K27M-MUTANT DIFFUSE MIDLINE GLIOMA

H3K27M-mutant diffuse midline glioma (H3K27M-mutant DMG) is a rare malignant brain tumour entity in children and adults with a median overall survival of around 12 months. Genomic and proteomic analysis may help to identify new target structures, however not all relevant targets are covered by these analyses. Glycosphingolipids and particularly gangliosides play a major role in brain development and have been involved in the pathology of brain tumours. The conversion of ceramide to glucosylceramide via glucosylceramide synthase (GCS) is one of the first steps in the synthesis of glycosphingolipids. Therefore, targeting GCS will inhibit their synthesis. Here we analysed the glycosphingolipids composition and tested GCS inhibitors in an in vitro model of H3K27M-mutant DMG. Methods: H3K27M-mutant DMG primary cells were established from needle biopsies of two paediatric patients and characterised by immunohistochemistry. Glycosphingolipids were analysed by thin layer chromatography, liquid chromatography-coupled tandem mass spectrometry and flow cytometry. The effect of the GCS inhibitor eliglustat on the cell proliferation was examined. Results. The primary cells maintained the features of the tumour of origin, including the H3K27M mutation. Neutral glycosphingolipids with 1 to 4 monosaccharides and the ganglioside GD2 were expressed at high concentration. Eliglustat completely abrogated the proliferation of the H3K27M-mutant DMG primary cells. Conclusions. The glycosphingolipids oncometabolism represents a novel target in H3K27M-mutant DMG. The GCS inhibitors eliglusat and miglustat are already used to treat paediatric patients with the lysosomal storage disorders Niemann Pick’s and Gaucher′s disease and miglustat can cross the blood-brain barrier. Thus, our finding may accelerate the access of H3K27M-mutant DMG patients to novel innovative clinical studies based on the inhibition of the glycosphingolipids metabolism.