Brain tumors

A novel PI3K inhibitor XH30 suppresses orthotopic glioblastoma and brain metastasis in mice models

Glioblastoma is carcinogenesis of glial cells in central nervous system and has the highest incidence among primary brain tumors. Brain metastasis, such as breast cancer and lung cancer, also leads to high mortality. The available medicines are limited due to blood–brain barrier. Abnormal activation of phosphatidylinositol 3-kinases (PI3K) signaling pathway is prevalent in glioblastoma and metastatic tumors. Here, we characterized a 2-amino-4-methylquinazoline derivative XH30 as a potent PI3K inhibitor with excellent anti-tumor activity against human glioblastoma. XH30 significantly repressed the proliferation of various brain cancer cells and decreased the phosphorylation of key proteins of PI3K signaling pathway, induced cell cycle arrest in G1 phase as well. Additionally, XH30 inhibited the migration of glioma cells and blocked the activation of PI3K pathway by interleukin-17A (IL-17A), which increased the migration of U87MG. Oral administration of XH30 significantly suppressed the tumor growth in both subcutaneous and orthotopic tumor models. XH30 also repressed tumor growth in brain metastasis models of lung cancers. Moreover, XH30 reduced IL-17A and its receptor IL-17RA in vivo. These results indicate that XH30 might be a potential therapeutic drug candidate for glioblastoma migration and brain metastasis.

Comprehensive commissioning and clinical implementation of GammaTiles STaRT for intracranial brain tumors

Purpose

To validate the dose calculation accuracy and dose distribution of GammaTiles for brain tumors, and to suggest a surgically targeted radiation therapy (STaRT) workflow for planning, delivery, radiation safety documentation, and posttreatment validation.

Methods and Materials

Novel surgically targeted radiation therapy, GammaTiles, uses Cs-131 radiation isotopes embedded in collagen-based tiles that can be resorbed after surgery. GammaTile target delineation and dose calculation were performed on MIM Symphony software. Point-based and complex seed distribution calculations in MIM Symphony were verified with hand calculations and BrachyVision calculations. Vendor-provided 2-dimensional dose distribution calculation accuracy was validated using gafchromic EBT3 film measurements at various depths. A workflow was established for safe and effective GammaTile implants.

Results

Good agreement was observed between different calculations. Calculation accuracy of less than 0.5% was achieved for all points except one, which had rounding issues for very low doses and resulted in just below 5% difference. Differences in anisotropy and geometry positioning were noticed in the delineation of Cs-131 IsoRay seeds in the compared systems, resulting in minor discrepancies in the calculated dosimetry distributions. Film measurements showed profiles with relatively good agreement of 0% to 5% in nongradient regions with higher differences between 5% to 10% in the sharp dose fall-off regions.

Conclusions

A comprehensive evaluation of GammaTile geometry, dose distribution, and clinical workflow was conducted. Safe intro-operative implantation of GammaTiles requires extensive preplanning and interdisciplinary collaboration. A STaRT workflow was outlined to provide a guideline for an accurate treatment planning and safe implant process at other institutions.

In Vivo 13C Magnetic Resonance Spectroscopy for Assessing Brain Biochemistry in Health and Disease

Magnetic resonance spectroscopy (MRS) is a non-invasive technique that contributes to the elucidation of brain biochemistry. ¹³C MRS enables the detection of specific neurochemicals and their neuroenergetic correlation with neuronal function. The synergistic outcome of ¹³C MRS and the infusion of ¹³C-labeled substrates provide an understanding of neurometabolism and the role of glutamate/gamma-aminobutyric acid (GABA) neurotransmission in diseases, such as Alzheimer's disease, schizophrenia, and bipolar disorder. Moreover, ¹³C MRS provides a window into the altered flux rate of different pathways, including the tricarboxylic acid cycle (TCA) and the glutamate/glutamine/GABA cycle, in health and in various diseases. Notably, the metabolic flux rate of the TCA cycle often decreases in neurodegenerative diseases. Additionally, ¹³C MRS can be used to investigate several psychiatric and neurological disorders as it directly reflects the real-time production and alterations of key brain metabolites. This review aims to highlight the chronology, the technological advancements, and the applications of ¹³C MRS in various brain diseases.

Multi-Classification of Brain Tumors on Magnetic Resonance Images Using an Ensemble of Pre-Trained Convolutional Neural Networks

BACKGROUND

Automatic classification of brain tumors is an important issue in computeraided diagnosis (CAD) for medical applications since it can efficiently improve the clinician's diagnostic performance and the current study focused on the CAD system of the brain tumors.

METHODS

Existing studies mainly focused on a single classifier either based on traditional machinelearning algorithms or deep learning algorithms with unsatisfied results. In this study, we proposed an ensemble of pre-trained convolutional neural networks to classify brain tumors into three types from their T1-weighted contrast-enhanced MRI (CE-MRI) images, which are meningioma, glioma, and pituitary tumor. Three pre-trained convolutional neural networks (Inception-v3, Resnet101, Densenet201) with the best classification performance (i.e. accuracy of 96.21%, 97.00%, 96.54%, respectively) on the CE-MRI benchmark dataset were selected as backbones of the ensemble model. The features extracted by backbone networks in the ensemble model were further classified by a support vector machine.

RESULTS

The ensemble system achieved an average classification accuracy of 98.14% under a five-fold cross-validation process, outperforming any single deep learning model in the ensemble system and other methods in the previous studies. Performance metrics for each brain tumor type, including area under the curve, sensitivity, specificity, precision, and F-score, were calculated to show the ensemble system's performance. Our work addressed a practical issue by evaluating the model with fewer training samples. The classification accuracy was reduced to 97.23%, 96.87%, and 93.96% when 75%, 50%, and 25% training data was used to train the ensemble model, respectively.

CONCLUSION

Our ensemble model has a great capacity and achieved the best performance in any single convolutional neural networks for brain tumors classification and is potentially applicable in real clinical practice.

Reoperation in adult patients with recurrent glioblastoma: A matched cohort analysis

Background

Despite maximal safe cytoreductive surgery and postoperative adjuvant therapies, glioblastoma (GBM) inevitably recurs and leads to deterioration of neurological status and eventual death. There is no consensus regarding the benefit of repeat resection for enhancing survival or quality of life in patients with recurrent GBM. We aimed to examine if reoperation for GBM recurrence incurs a survival benefit as well as examine its complication profile.

Methods

We performed a single-center retrospective chart review on all adult patients who underwent resection of supratentorial GBM between January 1, 2008 and December 1, 2013 at our center. Patients with repeat resection were manually matched for age, sex, tumor location, and Karnofsky Performance Status (KPS) with patients who underwent single resection to compare overall survival (OS), and postoperative morbidity.

Results

Of 237 patients operated with GBM, 204 underwent single resection and 33 were selected for repeat surgical resections. In a matched analysis there was no difference in the OS between groups (17.8 ± 17.6 months vs 17 ± 13.5 months, P = .221). In addition, repeat surgical resection had a higher rate of postoperative neurological complications compared to the initial surgery.

Conclusions

When compared with matched patients who underwent a single surgical resection, patients undergoing repeat surgical resection did not show significant increase in OS and may have incurred more neurological complications related to the repeat resection. Further studies are required to assess which patients would benefit from repeat surgical resection and optimize timing of the repeat resection in selected patients.

Red blood cell distribution width in glioblastoma

INTRODUCTION

Glioblastoma (GBM) is the most common and deadly adult brain tumor. Red blood cell distribution width (RDW) has been found in non-central nervous system neoplasms to be associated with survival. This study aims to assess the prognostic value of pre-operative RDW and trends in RDW over time during the disease course.

METHODS

This single-institution retrospective cohort study identified patients ≥ 18 years old with pathology-proved glioblastoma treated between April 2003-May 2017 from an institutional database. A Cox proportional hazards model was developed using known prognostic clinical variables to predict overall survival time; a second model incorporating continuously valued RDW was then created. The additional prognostic value of RDW was assessed with a joint model F-test. The variation of RDW-CV over time was evaluated with linear mixed model of RDW. A post-hoc exploratory analysis was performed to assess the trend in RDW lab value leading up to time of death.

RESULTS

346 adult GBM patients were identified; complete survival data was available for all patients. The addition of RDW to the multivariable Cox proportional hazards model did not increase prognostic value. There was an upward trend in RDW throughout the post-operative disease course. In a post-hoc analysis, there was an upward trend in RDW leading up to the time of death.

CONCLUSION

Although RDW has been prognostic of survival for many inflammatory, prothrombotic, and neoplastic diseases, pre-operative RDW was not associated with overall survival in GBM patients. RDW trended upwards throughout the disease course, suggesting possible systemic inflammatory effects of either glioblastoma or treatment.

A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients' quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

Long Noncoding RNA H19: A Novel Therapeutic Target Emerging in Oncology Via Regulating Oncogenic Signaling Pathways

Long noncoding RNA H19 (H19) is an imprinting gene with only maternal expression that is involved in regulating different processes in various types of cells. Previous studies have shown that abnormal H19 expression is involved in many pathological processes, such as cancer, mainly through sponging miRNAs, interacting with proteins, or regulating epigenetic modifications. Accumulating evidence has shown that several oncogenic signaling pathways lead to carcinogenesis. Recently, the regulatory relationship between H19 and oncogenic signaling pathways in various types of cancer has been of great interest to many researchers. In this review, we discussed the key roles of H19 in cancer development and progression via its regulatory function in several oncogenic signaling pathways, such as PI3K/Akt, canonical Wnt/β-catenin, canonical NF-κB, MAPK, JAK/STAT and apoptosis. These oncogenic signaling pathways regulated by H19 are involved in cell proliferation, proliferation, migration and invasion, angiogenesis, and apoptosis of various cancer cells. This review suggests that H19 may be a novel therapeutic target for cancers treatment by regulating oncogenic signaling pathways.

The PI3K Inhibitor XH30 Enhances Response to Temozolomide in Drug-Resistant Glioblastoma via the Noncanonical Hedgehog Signaling Pathway

Glioblastoma multiforme (GBM) is the most common malignant tumor of the central nervous system. Temozolomide (TMZ)-based adjuvant treatment has improved overall survival, but clinical outcomes remain poor; TMZ resistance is one of the main reasons for this. Here, we report a new phosphatidylinositide 3-kinase inhibitor, XH30; this study aimed to assess the antitumor activity of this compound against TMZ-resistant GBM. XH30 inhibited cell proliferation in TMZ-resistant GBM cells (U251/TMZ and T98G) and induced cell cycle arrest in the G1 phase. In an orthotopic mouse model, XH30 suppressed TMZ-resistant tumor growth. XH30 was also shown to enhance TMZ cytotoxicity both in vitro and in vivo. Mechanistically, the synergistic effect of XH30 may be attributed to its repression of the key transcription factor GLI1 via the noncanonical hedgehog signaling pathway. XH30 reversed sonic hedgehog-triggered GLI1 activation and decreased GLI1 activation by insulin-like growth factor 1 via the noncanonical hedgehog signaling pathway. These results indicate that XH30 may represent a novel therapeutic option for TMZ-resistant GBM.

Research Progress on Circular RNA in Glioma

The discovery of circular RNA (circRNA) greatly complements the traditional gene expression theory. CircRNA is a class of non-coding RNA with a stable cyclic structure. They are highly expressed, spatiotemporal-specific and conservative across species. Importantly, circRNA participates in the occurrence of many kinds of tumors and regulates the tumor development. Glioma is featured by limited therapy and grim prognosis. Cancer-associated circRNA compromises original function or creates new effects in glioma, thus contributing to oncogenesis. Therefore, this article reviews the biogenesis, metabolism, functions and properties of circRNA as a novel potential biomarker for gliomas. We elaborate the expression characteristics, interaction between circRNA and other molecules, aiming to identify new targets for early diagnosis and treatment of gliomas.

Brain tumor detection and classification using machine learning: a comprehensive survey

Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

Multifunctional lipidic nanocarriers for effective therapy of glioblastoma: recent advances in stimuli-responsive, receptor and subcellular targeted approaches

Background

Glioblastoma, or glioblastoma multiforme (GBM), remains a fatal cancer type despite the remarkable progress in understanding the genesis and propagation of the tumor. Current treatment modalities, comprising mainly of surgery followed by adjuvant chemoradiation, are insufficient for improving patients' survival owing to existing hurdles, including the blood–brain barrier (BBB). In contemporary practice, the prospect of long-term survival or cure continues to be a challenge for patients suffering from GBM. This review provides an insight into the drug delivery strategies and the significant efforts made in lipid-based nanoplatform research to circumvent the challenges in optimal drug delivery in GBM.

Area covered

Owing to the unique properties of lipid-based nanoplatforms and advancements in clinical translation, this article describes the application of various stimuli-responsive lipid nanocarriers and tumor subcellular organelle-targeted therapy to give an idea about the strategies that can be applied to enhance site-specific drug delivery for GBM. Furthermore, active targeting of drugs via surface-modified lipid-based nanostructures and recent findings in alternative therapeutic platforms such as gene therapy, immunotherapy, and multimodal therapy have also been overviewed.

Expert opinion

Lipid-based nanoparticles stand out among the other nanocarriers explored for GBM drug delivery, as they support both passive and active drug targeting by crossing/bypassing the BBB at the same time minimizing toxicity and projects better pharmacological parameters. Although these nanocarriers could be a plausible choice for treating GBM, in-depth research is essential to advance neuro-oncology research and enhance outcomes in patients with brain tumors.

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Simple Summary

One of the hottest areas in deep learning is computerized tumor diagnosis and treatment. The identification of tumor markers, the outline of tumor growth activity, and the staging of various tumor kinds are frequently included. There are several deep learning models based on convolutional neural networks that have high performance and accurate identification, with the potential to improve medical tasks. Breakthroughs and updates in computer algorithms and hardware devices, and intelligent algorithms applied in medical images have a diagnostic accuracy that doctors cannot match in some diseases. This paper reviews the progress of tumor detection from traditional computer-aided methods to convolutional neural networks and demonstrates the potential of the practical application of convolutional neural networks from practical cases to transform the detection model from experiment to clinical application.

Abstract

Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

The landscape of neuro-oncology in East Africa: a review of published records

INTRODUCTION

Primary central nervous system (CNS) tumors represent an important and growing cause of worldwide morbidity and mortality. There are global variations in the reported case burden of CNS tumors, with high-income countries reporting a higher incidence of cases than low- and middle-income countries. Variations are attributed to differences in access to care, diagnostic capacity, risk exposure, and under-reporting in LMICs. This study aims to review existing literature on the distribution of primary CNS tumors and neuro-oncologic care, and the contribution of scientists and institutions to neuro-oncologic research across 18 East African countries over the last 5 decades.

METHOD

A search was conducted using OVID Medline and PubMed databases to identify relevant East African neuro-oncologic studies published over the last 50 years.

RESULTS

The authors reviewed 36 neuro-oncology articles published across 8 of 18 East African countries. Kenya represented the highest number of published articles; ten countries queried yielded zero publications. A total of 2006 cases from all age groups were represented in published literature consisting of a wide spectrum of CNS tumors. One-third of reported cases were pediatric. Meningioma formed the largest proportion (43.3%) followed by glioma (33.7%). Sixty-seven percent of publications gave an overview of clinical care received by patients with most patients not receiving comprehensive neuro-oncologic care.

CONCLUSION

The modest collection of neuro-oncology publications from East Africa shows that the case diversity of primary CNS tumors in East Africa is comparable to the rest of the world. There is, however, poorer access to neurosurgical care and adjuvant therapy. Multidisciplinary efforts from clinicians, researchers, and healthcare agencies are needed to quantify and address the requisite neuro-oncology needs in this region.

An insight into the risk factors of brain tumors and their therapeutic interventions

Brain tumors are an abnormal growth of cells in the brain, also known as multifactorial groups of neoplasm. Incidence rates of brain tumors increase rapidly, and it has become a leading cause of tumor related deaths globally. Several factors have potential risks for intracranial neoplasm. To date, the International Agency for Research on Cancer has classified the ionizing radiation and the N-nitroso compounds as established carcinogens and probable carcinogens respectively. Diagnosis of brain tumors is based on histopathology and suitable imaging techniques. Labeled amino acids and fluorodeoxyglucose with or without contrast-enhanced MRI are used for the evaluation of tumor traces. T2-weighted MRI is an advanced diagnostic implementation, used for the detection of low-grade gliomas. Treatment decisions are based on tumor size, location, type, patient's age and health status. Conventional therapeutic approaches for tumor treatment are surgery, radiotherapy and chemotherapy. While the novel strategies may include targeted therapy, electric field treatments and vaccine therapy. Inhibition of cyclin-dependent kinase inhibitors is an attractive tumor mitigation strategy for advanced-stage cancers; in the future, it may prove to be a useful targeted therapy. The blood-brain barrier poses a major hurdle in the transport of therapeutics towards brain tissues. Moreover, nanomedicine has gained a vital role in cancer therapy. Nano drug delivery system such as liposomal drug delivery has been widely used in the cancer treatment. Liposome encapsulated drugs have improved therapeutic efficacy than free drugs. Numerous treatment therapies for brain tumors are in advanced clinical research.

Regulation of prognosis-related Siglecs in the glioma microenvironment

PURPOSE

The anti-inflammatory environment of glioma reduces the efficacy of immunotherapies. Therefore, it is vital to transform the immunosuppressive microenvironment of glioma into a pro-inflammatory environment. Sialic acid-binding immunoglobulin-type lectins (Siglecs) can serve as immune checkpoint targets that enhance the anti-tumor immune response. However, the roles of Siglecs in the glioma microenvironment are unknown. This study was conducted to identify targets to inhibit the anti-inflammatory environment to improve therapeutic outcomes in patients with glioma.

METHODS

We analyzed the regulatory effect of prognosis-related Siglecs identified from data available in The Cancer Genome Atlas database (TCGA) and China Glioma Genome Atlas Data portal on the immunosuppressive microenvironment of glioma. The effects of prognosis-related Siglecs on the glioma microenvironment were investigated by determining the Pearson correlation coefficients of the Siglecs in transcriptome data from the TCGA database.

RESULTS

Siglec-1, -9, -10, and -14 were closely associated with the prognosis of patients with glioma. The expression of these four Siglecs was significantly increased in the high-risk group and positively correlated with anti-inflammatory cytokine levels in the glioma microenvironment.

CONCLUSION

Our study provides insights into the effects of prognosis-related Siglecs in glioma immunotherapy, suggesting that targeted prognosis-related Siglecs can modify the microenvironment of glioma and improve the sensitivity of patients with glioma to immunotherapy.

Clinical and genomic factors associated with seizures in meningiomas

OBJECTIVE

The association of seizures with meningiomas is poorly understood. Moreover, any relationship between seizures and the underlying meningioma genomic subgroup has not been studied. Herein, the authors report on their experience with identifying clinical and genomic factors associated with preoperative and postoperative seizure presentation in meningioma patients.

METHODS

Clinical and genomic sequencing data on 394 patients surgically treated for meningioma at Yale New Haven Hospital were reviewed. Correlations between clinical, histological, or genomic variables and the occurrence of preoperative and postoperative seizures were analyzed. Logistic regression models were developed for assessing multiple risk factors for pre- and postoperative seizures. Mediation analyses were also conducted to investigate the causal pathways between genomic subgroups and seizures.

RESULTS

Seventeen percent of the cohort had presented with preoperative seizures. In a univariate analysis, patients with preoperative seizures were more likely to have tumors with a somatic NF2 mutation (p = 0.020), WHO grade II or III tumor (p = 0.029), atypical histology (p = 0.004), edema (p < 0.001), brain invasion (p = 0.009), and worse progression-free survival (HR 2.68, 95% CI 1.30-5.50). In a multivariate analysis, edema (OR 3.11, 95% CI 1.46-6.65, p = 0.003) and atypical histology (OR 2.00, 95% CI 1.03-3.90, p = 0.041) were positive predictors of preoperative seizures, while genomic subgroup was not, such that the effect of an NF2 mutation was indirectly mediated through atypical histology and edema (p = 0.012). Seizure freedom was achieved in 83.3% of the cohort, and only 20.8% of the seizure-free patients, who were more likely to have undergone gross-total resection (p = 0.031), were able to discontinue antiepileptic drug use postoperatively. Preoperative seizures (OR 3.54, 95% CI 1.37-9.12, p = 0.009), recurrent tumors (OR 2.89, 95% CI 1.08-7.74, p = 0.035), and tumors requiring postoperative radiation (OR 2.82, 95% CI 1.09-7.33, p = 0.033) were significant predictors of postoperative seizures in a multivariate analysis.

CONCLUSIONS

Seizures are relatively common at meningioma presentation. While NF2-mutated tumors are significantly associated with preoperative seizures, the association appears to be mediated through edema and atypical histology. Patients who undergo radiation and/or have a recurrence are at risk for postoperative seizures, regardless of the extent of resection. Preoperative seizures may indeed portend a more potentially aggressive molecular entity and challenging clinical course with a higher risk of recurrence.

Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method

The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset.

An application of neutrosophic hypersoft mapping to diagnose brain tumor and propose appropriate treatment

Brain tumors are one of the leading causes of death around the globe. More than 10 million people fall prey to it every year. This paper aims to characterize the discussions related to the diagnosis of tumors with their related problems. After examining the side effects of tumors, it encases similar indications, and it is hard to distinguish the precise type of tumors with their seriousness. Since in practical assessment, the indeterminacy and falsity parts are frequently dismissed, and because of this issue, it is hard to notice the precision in the patient’s progress history and cannot foresee the period of treatment. The Neutrosophic Hypersoft set (NHS) and the NHS mapping with its inverse mapping has been design to overcome this issue since it can deal with the parametric values of such disease in more detail considering the sub-parametric values; and their order and arrangement. These ideas are capable and essential to analyze the issue properly by interfacing it with scientific modeling. This investigation builds up a connection between symptoms and medicines, which diminishes the difficulty of the narrative. A table depending on a fuzzy interval between [0, 1] for the sorts of tumors is constructed. The calculation depends on NHS mapping to adequately recognize the disease and choose the best medication for each patient’s relating sickness. Finally, the generalized NHS mapping is presented, which will encourage a specialist to extricate the patient’s progress history and to foresee the time of treatment till the infection is relieved.

Multidimensional hydrogel models reveal endothelial network angiocrine signals increase glioblastoma cell number, invasion, and temozolomide resistance

Glioblastoma (GBM) is the most common primary malignant brain tumor. The tissue microenvironment adjacent to vasculature, termed the perivascular niche, has been implicated in promoting biological processes involved in glioblastoma progression such as invasion, proliferation, and therapeutic resistance. However, the exact nature of the cues that support tumor cell aggression in this niche is largely unknown. Soluble angiocrine factors secreted by tumor-associated vasculature have been shown to support such behaviors in other cancer types. Here, we exploit macroscopic and microfluidic gelatin hydrogel platforms to profile angiocrine factors secreted by self-assembled endothelial networks and evaluate their relevance to glioblastoma biology. Aggregate angiocrine factors support increases in U87-MG cell number, migration, and therapeutic resistance to temozolomide. We also identify a novel role for TIMP1 in facilitating glioblastoma tumor cell migration. Overall, this work highlights the use of multidimensional hydrogel models to evaluate the role of angiocrine signals in glioblastoma progression.

Anti-proliferative and apoptotic effects of green synthesized silver nanoparticles using Lavandula angustifolia on human glioblastoma cells

In this study, we aimed at the green synthesis of silver nanoparticles (AgNPs) using Lavandula angustifolia extract and the investigation of the anti-proliferative and apoptotic inducing effects of these nanoparticles in the U87MG glioblastoma cancer cell line. Green synthesized silver nanoparticles were characterized by various analytical techniques such as UV-Visible Spectrophotometer (UV-Vis), scanning electron microscopy (SEM) and Energy Dispersive X-ray (EDX). UV-Vis spectroscopy displayed a specific silver plasmon peak at 430 nm. U87MG cells were treated at increased concentrations with Lavandula angustifolia-AgNPs (La-AgNPs) (0-20 µg/mL) for 72 h and the anti-proliferative effects of green synthesized silver nanoparticles on U87MG cells were evaluated by MTT assay. The La- AgNPs induced a statistically significant dose-dependent decrease in proliferation and increased cytotoxicity in U87MG cells. The IC50 value is 7.536 µg/mL. Furthermore, the expression of apoptosis proteins caspase-3, caspase-8 and caspase-9 was analyzed using ELISA and caspase-3 and p53 using western blotting. The results suggest that La-AgNPs induce cell death in U87MG cells through the p53 mediated intrinsic apoptotic pathway. Together, the present findings suggest that La-AgNPs could be considered as a potential option for the treatment of glioblastoma.

Incidence, Mortality, and Survival Trends of Primary CNS Tumors in Cali, Colombia, From 1962 to 2019

PURPOSE

Global studies have shown varying trends of CNS tumors within geographic regions. In Colombia, the epidemiologic characteristics of CNS neoplasms are not well elucidated. We aimed to provide a summary of the descriptive epidemiology of primary CNS tumors among the urban population of Cali, Colombia.

METHODS

We conducted a time-trend study from 1962 to 2019 using the Population-Based Cali Cancer Registry. The age-standardized rates per 100,000 person-years were obtained by direct method using the world standard population. Results were stratified by sex, age group at diagnosis, and histologic subtype. We used Joinpoint regression analysis to detect trends and obtain annual percentage change (APC) with 95% CIs. We estimated 5-year net survival using the Pohar-Perme method.

RESULTS

During 1962 to 2016, 4,732 new cases of CNS tumors were reported. From 1985 to 2019, a total of 2,475 deaths from malignant CNS tumors were registered. A statistically significant increase in the trends of incidence (APC, 2.8; 95% CI, 2.1 to 3.5) and mortality (APC, 1.5; 95% CI, 1.1 to 2.0) rates was observed during the study. The most common malignant CNS tumor was glioblastoma (17.8% of all tumors), and the most frequent benign tumor was meningioma (17.2%). Malignancy was more common in males than in females. Unspecified malignant neoplasms represented 32% of all cases. The highest 5-year net survival was 31.4% during 2012 to 2016.

CONCLUSION

Our findings demonstrate an increasing burden of primary CNS tumors for the last 60 years, with a steady rate from the early 2010s. There was an improvement of 5-year net survival for the last decade. Males had higher mortality than did females. Additional efforts are needed to fully explore the geographic, environmental, and genetic contributors of CNS malignancies within the region.

The PROGRAM study: awake mapping versus asleep mapping versus no mapping for high-grade glioma resections: study protocol for an international multicenter prospective three-arm cohort study

INTRODUCTION

The main surgical dilemma during glioma resections is the surgeon's inability to accurately identify eloquent areas when the patient is under general anaesthesia without mapping techniques. Intraoperative stimulation mapping (ISM) techniques can be used to maximise extent of resection in eloquent areas yet simultaneously minimise the risk of postoperative neurological deficits. ISM has been widely implemented for low-grade glioma resections backed with ample scientific evidence, but this is not yet the case for high-grade glioma (HGG) resections. Therefore, ISM could thus be of important value in HGG surgery to improve both surgical and clinical outcomes.

METHODS AND ANALYSIS

This study is an international, multicenter, prospective three-arm cohort study of observational nature. Consecutive HGG patients will be operated with awake mapping, asleep mapping or no mapping with a 1:1:1 ratio. Primary endpoints are: (1) proportion of patients with National Institute of Health Stroke Scale deterioration at 6 weeks, 3 months and 6 months after surgery and (2) residual tumour volume of the contrast-enhancing and non-contrast-enhancing part as assessed by a neuroradiologist on postoperative contrast MRI scans. Secondary endpoints are: (1) overall survival and (2) progression-free survival at 12 months after surgery; (3) oncofunctional outcome and (4) frequency and severity of serious adverse events in each arm. Total duration of the study is 5 years. Patient inclusion is 4 years, follow-up is 1 year.

ETHICS AND DISSEMINATION

The study has been approved by the Medical Ethics Committee (METC Zuid-West Holland/Erasmus Medical Center; MEC-2020-0812). The results will be published in peer-reviewed academic journals and disseminated to patient organisations and media.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov ID number NCT04708171 (PROGRAM-study), NCT03861299 (SAFE-trial).

KYP-2047, an Inhibitor of Prolyl-Oligopeptidase, Reduces GlioBlastoma Proliferation through Angiogenesis and Apoptosis Modulation

Simple Summary

Glioblastoma (GB) is the most aggressive brain tumor characterized by necrosis, excessive proliferation, and invasiveness. Despite relevant progress in conventional treatments, the survival rate for patients with GB remains low. The present study investigated the potential effect of KYP-2047, an inhibitor of the prolyl-oligopeptidase (POP or PREP), in an in vivo U87-xenograft model and in an in vitro study on human GB cells. This study demonstrated the abilities of KYP-2047 to counteract and reduce GB progression through angiogenesis and apoptosis modulation.

Abstract

Glioblastoma (GB) is the most aggressive tumor of the central nervous system (CNS), characterized by excessive proliferation, necrosis and invasiveness. The survival rate for patients with GB still remains low. Angiogenesis and apoptosis play a key role in the development of GB. Thus, the modulation of angiogenesis and apoptosis processes represent a possible strategy to counteract GB progression. This study aimed to investigate the potential effect of KYP-2047, an inhibitor of the prolyl-oligopeptidase (POP), known to modulate angiogenesis, in an in vivo U87-xenograft model and in an in vitro study on human GB cells. Our results showed that KYP-2047 at doses of 2.5 mg/kg and 5 mg/kg was able to reduce tumor burden in the xenograft-model. Moreover, KYP-2047 significantly reduced vascular endothelial-growth-factor (VEGF), angiopoietins (Ang) and endothelial-nitric-oxide synthase (eNOS) expression. In vitro study revealed that KYP-2047 at different concentrations reduced GB cells’ viability. Additionally, KYP-2047 at the concentrations of 50 µM and 100 µM was able to increase the pro-apoptotic protein Bax, p53 and caspase-3 expression whereas Bcl-2 expression was reduced. Thus, KYP-2047 could represent a potential therapeutic treatment to counteract or reduce GB progression, thanks its abilities to modulate angiogenesis and apoptosis pathways.

Influence of Concurrent and Adjuvant Temozolomide on Health-Related Quality of Life of Patients with Grade III Gliomas: A Secondary Analysis of a Randomized Clinical Trial (KNOG-1101 Study)

Purpose

The KNOG-1101 study showed improved 2-year progression-free survival (PFS) with temozolomide during and after radio-therapy compared to radiotherapy alone for patients with anaplastic gliomas. This trial investigates the effect of concurrent and adjuvant temozolomide on health-related quality of life (HRQoL).

Materials and Methods

In this randomized, open-label, phase II trial, 90 patients with World Health Organization grade III glioma were enrolled across multiple centers in South Korea between March 2012 to February 2015 and followed up through 2017. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire 30 (EORTC QLQ-C30) and 20-item EORTC QLQ-Brain Neoplasm (QLQ-BN20) were used to compare HRQoL between patients assigned to concurrent chemoradiotherapy with temozolomide followed by 6 cycles of adjuvant temozolomide (arm A) and radiotherapy (RT) alone (arm B).

Results

Of the 90 patients in the study, 84 patients (93.3%) completed the baseline HRQoL questionnaire. Emotional functioning, fatigue, nausea and vomiting, dyspnea, constipation, appetite loss, diarrhea, seizures, itchy skin, drowsiness, hair loss, and bladder control were not affected by the addition of temozolomide. All other items did not differ significantly between arm A and arm B throughout treatment. Global health status particularly stayed consistent at the end of adjuvant temozolomide (p=0.47) and at the end of RT (p=0.33).

Conclusion

The addition of concurrent and adjuvant temozolomide did not show negative influence on HRQoL with improvement of PFS for patients with anaplastic gliomas. The absence of systematic and clinically relevant changes in HRQoL suggests that an overall long-term net clinical benefit exists for concurrent and adjuvant temozolomide.

Toward precision immunotherapy using multiplex immunohistochemistry and in silico methods to define the tumor immune microenvironment

Recent developments in cancer immunotherapy promise better outcomes for cancer patients, although clinical trials for difficult to treat cancers such as malignant brain cancer present special challenges, showing little response to first generation immunotherapies. Reasons for differences in immunotherapy response in some cancer types are likely due to the nature of tumor microenvironment, which harbors multiple cell types which interact with tumor cells to establish immunosuppression. The cell types which appear to hold the key in regulating tumor immunosuppression are the tumor-infiltrating immune cells. The current standard treatment for difficult to treat cancer, including the most malignant brain cancer, glioblastoma, continues to offer a bleak outlook for patients. Immune-profiling and correlation with pathological and clinical data will lead to a deeper understanding of the tumor immune microenvironment and contribute toward the selection, optimization and development of novel precision immunotherapies. Here, we review the current understanding of the tumor microenvironmental landscape in glioblastoma with a focus on next-generation technologies including multiplex immunofluorescence and computational approaches to map the brain tumor microenvironment to decipher the role of the immune system in this lethal malignancy.

Are X-ray Safety Glasses Alone Enough for Adequate Ocular Protection in Complex Radiological Interventions?

ABSTRACT

The maximum annual radiation ocular dose limit for medical staff has been reduced to 20 mSv in the current European directive 2013/59/Euratom. This multi-centric study aims at reporting the protected and unprotected eye lens doses in different fluoroscopically guided interventions and to evaluate any other factors that could influence the ocular dose. From July 2018 to July 2019, ocular radiation doses of six interventionists of four departments during complex interventions were recorded with a thermoluminescent dosimeter in front of and behind radiation protection glasses to measure the protected and unprotected doses. The position of personnel, intervention type, fluoroscopy time, total body dose and use of pre-installed protection devices like lead acrylic shields were also systematically recorded. Linear regression analysis was used to estimate the doses at 2 y and 5 y. The annual unprotected/protected ocular doses of six interventionists were 67/21, 32.7/3.3, 27.4/5.1, 7/0, 21.8/2.2, and 0/0 mSv, respectively. The unprotected dose crossed the 20-mSv annual limits for four interventionists and protected dose for one less experienced interventionist. The estimated 5-y protected ocular dose of this interventionist was 101.318 mSv (95%CI 96.066-106.57), also crossing the 5-y limit. The use of a lead acrylic shield was observed to have a significant effect in reducing ocular doses. The annual unprotected and protected ocular doses for interventionists dealing with complex interventions could cross the present permitted yearly limit. The measurement of significant protected ocular dose behind the radiation protection glasses emphasizes the additional indispensable role of pre-installed radiation protection devices and training in reducing radiation doses for complex procedures.

Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

Brain tumors diagnosis in children is a scientific concern due to rapid anatomical, metabolic, and functional changes arising in the brain and non-specific or conflicting imaging results. Pediatric brain tumors diagnosis is typically centralized in clinical practice on the basis of diagnostic clues such as, child age, tumor location and incidence, clinical history, and imaging (Magnetic resonance imaging MRI / computed tomography CT) findings. The implementation of deep learning has rapidly propagated in almost every field in recent years, particularly in the medical images’ evaluation. This review would only address critical deep learning issues specific to pediatric brain tumor imaging research in view of the vast spectrum of other applications of deep learning. The purpose of this review paper is to include a detailed summary by first providing a succinct guide to the types of pediatric brain tumors and pediatric brain tumor imaging techniques. Then, we will present the research carried out by summarizing the scientific contributions to the field of pediatric brain tumor imaging processing and analysis. Finally, to establish open research issues and guidance for potential study in this emerging area, the medical and technical limitations of the deep learning-based approach were included.

Nanogels as a Versatile Drug Delivery System for Brain Cancer

Chemotherapy and radiation remain as mainstays in the treatment of a variety of cancers globally, yet some therapies exhibit limited specificity and result in harsh side effects in patients. Brain tissue differs from other tissue due to restrictions from the blood-brain barrier, thus systemic treatment options are limited. The focus of this review is on nanogels as local and systemic drug delivery systems in the treatment of brain cancer. Nanogels are a unique local or systemic drug delivery system that is tailorable and consists of a three-dimensional polymeric network formed via physical or chemical assembly. For example, thermosensitive nanogels show promise in their ability to incorporate therapeutic agents in nano-structured matrices, be applied in the forms of sprays or sols to the area from which a tumor has been removed, form adhesive gels to fill the cavity and deliver treatment locally. Their usage does come with complications, such as handling, storage, chemical stability, and degradation. Despite these limitations, the current ongoing development of nanogels allows patient-centered treatment that can be considered as a promising tool for the management of brain cancer.

Laser thermal therapy in the management of high-grade gliomas

Laser interstitial thermal therapy (LITT) is a minimally invasive therapy that have been used for brain tumors, epilepsy, chronic pain, and other spine pathologies. This therapy is performed under imaging and stereotactic guidance to precisely direct the probe and ablate the area of interest using real-time magnetic resonance (MR) thermography. LITT has gained popularity as a treatment for glioma because of its minimally invasive nature, small skin incision, repeatability, shorter hospital stay, and the possibility of receiving adjuvant therapy shortly after surgery instead of several weeks as required after open surgical resection. Several reports have demonstrated the usefulness of LITT in the treatment of newly-diagnosed and recurrent gliomas. In this review, we will summarize the recent evidence of this therapy in the field of glioma surgery and the future perspectives of the use of LITT combined with other treatment strategies for this devastating disease.

Phosphorous Magnetic Resonance Spectroscopy to Detect Regional Differences of Energy and Membrane Metabolism in Naïve Glioblastoma Multiforme

Simple Summary

Glioblastoma multiforme is a highly aggressive brain tumor, tending to infiltrate even larger zones of brain tissue than visible on conventional magnetic resonance imaging. By application of phosphorus magnetic resonance spectroscopy in patients with naïve glioblastoma multiforme, we tried to demonstrate changes in energy and membrane metabolism not only in affected regions but also in distant brain regions, the opposite brain hemisphere, and in comparison to healthy volunteers. We found reduced energetic states and signs of increased cell membrane turnover in regions of visible tumor and differences to and between the “normal-appearing” brains of glioblastoma patients and the brains of healthy volunteers. Our pilot study confirmed the feasibility of the method, so differences between various genetic mutations or clinical applicability for follow-up monitoring can be assessed in larger cohorts.

Abstract

Background: Glioblastoma multiforme (GBM) is a highly malignant primary brain tumor with infiltration of, on conventional imaging, normal-appearing brain parenchyma. Phosphorus magnetic resonance spectroscopy (31P-MRS) enables the investigation of different energy and membrane metabolites. The aim of this study is to investigate regional differences of 31P-metabolites in GBM brains. Methods: In this study, we investigated 32 patients (13 female and 19 male; mean age 63 years) with naïve GBM using 31P-MRS and conventional MRI. Contrast-enhancing (CE), T2-hyperintense, adjacent and distant ipsilateral areas of the contralateral brain and the brains of age- and gender-matched healthy volunteers were assessed. Moreover, the 31P-MRS results were correlated with quantitative diffusion parameters. Results: Several metabolite ratios between the energy-dependent metabolites and/or the membrane metabolites differed significantly between the CE areas, the T2-hyperintense areas, the more distant areas, and even the brains of healthy volunteers. pH values and Mg²⁺ concentrations were highest in visible tumor areas and decreased with distance from them. These results are in accordance with the literature and correlated with quantitative diffusion parameters. Conclusions: This pilot study shows that 31P-MRS is feasible to show regional differences of energy and membrane metabolism in brains with naïve GBM, particularly between the different “normal-appearing” regions and between the contralateral hemisphere and healthy controls. Differences between various genetic mutations or clinical applicability for follow-up monitoring have to be assessed in a larger cohort.

Observing deep radiomics for the classification of glioma grades

Deep learning is a promising method for medical image analysis because it can automatically acquire meaningful representations from raw data. However, a technical challenge lies in the difficulty of determining which types of internal representation are associated with a specific task, because feature vectors can vary dynamically according to individual inputs. Here, based on the magnetic resonance imaging (MRI) of gliomas, we propose a novel method to extract a shareable set of feature vectors that encode various parts in tumor imaging phenotypes. By applying vector quantization to latent representations, features extracted by an encoder are replaced with a fixed set of feature vectors. Hence, the set of feature vectors can be used in downstream tasks as imaging markers, which we call deep radiomics. Using deep radiomics, a classifier is established using logistic regression to predict the glioma grade with 90% accuracy. We also devise an algorithm to visualize the image region encoded by each feature vector, and demonstrate that the classification model preferentially relies on feature vectors associated with the presence or absence of contrast enhancement in tumor regions. Our proposal provides a data-driven approach to enhance the understanding of the imaging appearance of gliomas.

Meningiomas in Gynecology and Reproduction: an Updated Overview for Clinical Practice

There is various evidence to suggest a relationship between female hormones and meningiomas; as clinicians, we often come to face challenging situations involving female patients diagnosed with meningiomas during the post-pubertal phases of their life. We aimed to review the specific circumstances (pregnancy, postpartum, hormonal contraception and hormone replacement therapy, gender-affirming hormonal treatment) clinicians might come to face during their daily clinical practice, given the absence of available guidelines. We therefore conducted a narrative review on articles found in PubMed and Embase databases using appropriate keywords. Ninety-six relevant articles were included. The available evidence on managing meningiomas in post-pubertal women often implies personal strategies, highlighting the lack of a unified approach. The knowledge of the biological links between female hormones and meningiomas is fundamental to correctly counsel patients in various life phases. Prospective randomized studies are required to improve available guidelines on how to best manage meningiomas in female post-pubertal patients.

Rethinking CRITID Procedure of Brain Targeting Drug Delivery: Circulation, Blood Brain Barrier Recognition, Intracellular Transport, Diseased Cell Targeting, Internalization, and Drug Release

The past decades have witnessed great progress in nanoparticle (NP)-based brain-targeting drug delivery systems, while their therapeutic potentials are yet to be fully exploited given that the majority of them are lost during the delivery process. Rational design of brain-targeting drug delivery systems requires a deep understanding of the entire delivery process along with the issues that they may encounter. Herein, this review first analyzes the typical delivery process of a systemically administrated NPs-based brain-targeting drug delivery system and proposes a six-step CRITID delivery cascade: circulation in systemic blood, recognizing receptor on blood-brain barrier (BBB), intracellular transport, diseased cell targeting after entering into parenchyma, internalization by diseased cells, and finally intracellular drug release. By dissecting the entire delivery process into six steps, this review seeks to provide a deep understanding of the issues that may restrict the delivery efficiency of brain-targeting drug delivery systems as well as the specific requirements that may guarantee minimal loss at each step. Currently developed strategies used for troubleshooting these issues are reviewed and some state-of-the-art design features meeting these requirements are highlighted. The CRITID delivery cascade can serve as a guideline for designing more efficient and specific brain-targeting drug delivery systems.

Modeling motor task activation from resting-state fMRI using machine learning in individual subjects

Resting-state functional MRI (rs-fMRI) has provided important insights into brain physiology. It has become an increasingly popular method for presurgical mapping, as an alternative to task-based functional MRI wherein the subject performs a task while being scanned. However, there is no commonly acknowledged gold standard approach for detecting eloquent brain areas using rs-fMRI data in clinical settings. In this study, a general linear model-based machine learning (GLM-ML) approach was tested to predict individual motor task activation based on rs-fMRI data. Its accuracy was then compared to a conventional independent component analysis (ICA) approach. 47 healthy subjects were scanned using resting state, active and passive motor task fMRI experiments using a clinically applicable low-resolution fMRI protocol. The model was trained to associate rs-fMRI network maps with that of hand movement task fMRI, then used to predict task activation maps for unseen subjects solely based on their rs-fMRI data. Our results showed that the GLM-ML approach can accurately predict individual differences in task activation using rs-fMRI data and outperform conventional ICA to detect task activation in the primary sensorimotor region. Furthermore, the predicted activation maps using the GLM -ML model matched well with the activation of passive hand movement fMRI on an individual basis. These results suggest that GLM-ML approach can robustly predict individual differences of task activation based on conventional low-resolution rs-fMRI data and has important implications for future clinical applications.

Green nanomedicine: the path to the next generation of nanomaterials for diagnosing brain tumors and therapeutics?

Introduction: Current brain cancer treatments, based on radiotherapy and chemotherapy, are sometimes successful, but they are not free of drawbacks.Areas covered: Traditional methods for the treatment of brain tumors are discussed here with new solutions presented, among which the application of nanotechnology has demonstrated promising results over the past decade. The traditional synthesis of nanostructures, which relies on the use of physicochemical methodologies are discussed, and their associated concerns in terms of environmental and health impact due to the production of toxic by-products, need for toxic catalysts, and their lack of biocompatibility are presented. An overview of the current situation for treating brain tumors using nanotechnological-based approaches is introduced, and some of the latest advances in the application of green nanomaterials (NMs) for the effective targeting of brain tumors are presented.Expert opinion: Green nanotechnology is introduced as a potential solution to toxic NMs through the application of environmentally friendly and cost-effective protocols using living organisms and biomolecules. The current status of this field, such as those involving clinical trials, is included, and the possible limitations of green-NMs and potential ways to avoid those limitations are discussed so that the field can potentially evolve.

A Novel Deep Learning Method for Recognition and Classification of Brain Tumors from MRI Images

A brain tumor is an abnormal growth in brain cells that causes damage to various blood vessels and nerves in the human body. An earlier and accurate diagnosis of the brain tumor is of foremost important to avoid future complications. Precise segmentation of brain tumors provides a basis for surgical planning and treatment to doctors. Manual detection using MRI images is computationally complex in cases where the survival of the patient is dependent on timely treatment, and the performance relies on domain expertise. Therefore, computerized detection of tumors is still a challenging task due to significant variations in their location and structure, i.e., irregular shapes and ambiguous boundaries. In this study, we propose a custom Mask Region-based Convolution neural network (Mask RCNN) with a densenet-41 backbone architecture that is trained via transfer learning for precise classification and segmentation of brain tumors. Our method is evaluated on two different benchmark datasets using various quantitative measures. Comparative results show that the custom Mask-RCNN can more precisely detect tumor locations using bounding boxes and return segmentation masks to provide exact tumor regions. Our proposed model achieved an accuracy of 96.3% and 98.34% for segmentation and classification respectively, demonstrating enhanced robustness compared to state-of-the-art approaches.

Case report of primary dural lymphoma mimicking a cerebellar meningioma and brief review of literature

Primary dural lymphoma (PDL) is an extremely rare subtype of primary central nervous system lymphoma arising from the dura mater in absence of systemic disease. The most common histological type is the low-grade marginal zone lymphoma, whereas high-grade lymphomas are unusual. We present a case of primary diffuse large B-cell lymphoma, presenting as PDL in the posterior fossa, originating from the dura mater of the petrous bone covering the surface of the left cerebellum, a location not previously described. A 65-year-old woman presented with sudden onset of severe dizziness was admitted in otolaryngology department then transferred to neurosurgery ward. CT scan revealed a large lesion involving left cerebellum, subsequent MRI of the brain demonstrated an enhancing mass suggestive for petrous bone meningioma. The tumor was excised, and the histopathological examination unexpectedly revealed a diffuse large B-cell lymphoma. The patient received postoperative chemoradiotherapy. 20 months after surgery a good outcome was registered. Due to the rarity of primary dural lymphomas no standard treatment is available, however, gross total or subtotal resection followed by adjuvant therapy seems to be a good choice to manage the pathology.

The Management of Brain Metastases-Systematic Review of Neurosurgical Aspects

The multidisciplinary management of patients with brain metastases (BM) consists of surgical resection, different radiation treatment modalities, cytotoxic chemotherapy, and targeted molecular treatment. This review presents the current state of neurosurgical technology applied to achieve maximal resection with minimal morbidity as a treatment paradigm in patients with BM. In addition, we discuss the contribution of neurosurgical resection on functional outcome, advanced systemic treatment strategies, and enhanced understanding of the tumor biology.

Assessment of Isocitrate Dehydrogenase 1 Mutation by Immunohistochemistry in Egyptian Patients with High-grade Gliomas

BACKGROUND: At present, the classification of central nervous system tumors relies on molecular factors in addition to histologic features to identify many tumor types. This should subsequently results in more accurate diagnosis as well as addressing specific markers of potential prognostic and predictive value. AIM: This study was conducted to emphasize the importance of including isocitrate dehydrogenase 1 (IDH1) evaluation as a crucial part of the diagnosis and categorization of high-grade glioma cases. This also may help to individualize the treatment of high-grade glioma patients. MATERIALS AND METHODS: The current study included 60 cases of high-grade gliomas, studied histologically and immunohistochemically for the detection of IDH1 mutation. The results were correlated with different clinicopathologic variables and course of the disease. RESULTS: IDH1 immunohistochemical expression was positive in 46.67% of the studied high-grade glioma cases. A statistically significant relationship was detected between IDH1 expression and tumor histologic grade as 100% of Grade III anaplastic oligodendroglioma cases and 80% of the Grade III anaplastic astrocytoma cases were IDH1 positive while only 40.4% of Grade IV glioblastoma cases were IDH1 positive (p = 0.03). In addition, patients who were IDH1 mutant were in a better category of response to radiotherapy (p = 0.019) and also to chemotherapy (p < 0.001). Moreover, patients who expressed IDH1 had prolonged overall survival (OS) and progression-free survival than those who showed negative IDH1expression (p < 0.001). On the other hand, no statistically significant relationship was detected between IDH1 expression and patients age, sex, tumor site, tumor size, motor symptoms, sensory symptoms, and increased intracranial tension (p > 0.05). CONCLUSIONS: It is suggested that IDH1 is a good prognostic marker for gliomas and is a good predictive marker for response to treatment. IDH1 is a promising target for therapy in high-grade gliomas through the emerging IDH1 inhibitors. Immunohistochemical testing for IDH1 is a practical and cost-effective method that should be applied in all glioma cases. Further study on a larger sample size is recommended to validate the current results. Moreover, applying molecular analysis to detect IDH1 mutation is recommended to be able to precisely detect the IDH1 wild-type tumor

Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology

Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patient's brain to a normal one. Many patient images are associated with irregularly distributed lesions, resulting in further distortion of normal tissue structures and complicating registration's similarity measure. In this work, we follow a multi-step context-aware image inpainting framework to generate synthetic tissue intensities in the tumor region. The coarse image-to-image translation is applied to make a rough inference of the missing parts. Then, a feature-level patch-match refinement module is applied to refine the details by modeling the semantic relevance between patch-wise features. A symmetry constraint reflecting a large degree of anatomical symmetry in the brain is further proposed to achieve better structure understanding. Deformable registration is applied between inpainted patient images and normal brains, and the resulting deformation field is eventually used to deform original patient data for the final alignment. The method was applied to the Multimodal Brain Tumor Segmentation (BraTS) 2018 challenge database and compared against three existing inpainting methods. The proposed method yielded results with increased peak signal-to-noise ratio, structural similarity index, inception score, and reduced L1 error, leading to successful patient-to-normal brain image registration.

The Glioblastoma Microenvironment: Morphology, Metabolism, and Molecular Signature of Glial Dynamics to Discover Metabolic Rewiring Sequence

Different functional states determine glioblastoma (GBM) heterogeneity. Brain cancer cells coexist with the glial cells in a functional syncytium based on a continuous metabolic rewiring. However, standard glioma therapies do not account for the effects of the glial cells within the tumor microenvironment. This may be a possible reason for the lack of improvements in patients with high-grade gliomas therapies. Cell metabolism and bioenergetic fitness depend on the availability of nutrients and interactions in the microenvironment. It is strictly related to the cell location in the tumor mass, proximity to blood vessels, biochemical gradients, and tumor evolution, underlying the influence of the context and the timeline in anti-tumor therapeutic approaches. Besides the cancer metabolic strategies, here we review the modifications found in the GBM-associated glia, focusing on morphological, molecular, and metabolic features. We propose to analyze the GBM metabolic rewiring processes from a systems biology perspective. We aim at defining the crosstalk between GBM and the glial cells as modules. The complex networking may be expressed by metabolic modules corresponding to the GBM growth and spreading phases. Variation in the oxidative phosphorylation (OXPHOS) rate and regulation appears to be the most important part of the metabolic and functional heterogeneity, correlating with glycolysis and response to hypoxia. Integrated metabolic modules along with molecular and morphological features could allow the identification of key factors for controlling the GBM-stroma metabolism in multi-targeted, time-dependent therapies.

Phase 0 and window of opportunity clinical trial design in neuro-oncology: a RANO review

Glioblastoma is a devastating disease with poor prognosis. Few effective chemotherapeutics are currently available, and much effort has been expended to identify new drugs capable of slowing tumor progression. The phase 0 trial design was developed to facilitate early identification of promising agents for cancer that should undergo accelerated approval. This design features an early in-human study that enrolls a small number of patients who receive subtherapeutic doses of medication with the goals of describing pharmacokinetics through drug blood level measurements and determining intratumoral concentrations of the investigational compound as well as pharmacodynamics by studying the biochemical and physiological effects of drugs. In neuro-oncology, however, the presence of the blood-brain barrier and difficulty in obtaining brain tumor tissue warrant a separate set of considerations. In this paper, we critically reviewed the protocols used in all brain tumor related in-human phase 0 and phase 0-like ("window of opportunity") studies between 1993 and 2018, as well as ongoing clinical trials, and identified major challenges in trial design as applied to central nervous system tumors that include surgical specimen collection and storage, brain tumor drug level analysis, and confirmation of drug action. We therefore propose that phase 0 trials in neuro-oncology should include (i) only patients in whom a resection of the tumor is planned, (ii) use of clinical doses of an investigational agent, (iii) tissue sampling from enhancing and non-enhancing portions of the tumor, and (iv) assessment of drug-specific target effects. Standardization of clinical protocols for phase 0/window of opportunity studies can help accelerate the development of effective treatments for glioblastoma.

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

Presenting Psychiatric and Neurological Symptoms and Signs of Brain Tumors before Diagnosis: A Systematic Review

Brain tumors can present with various psychiatric symptoms, with or without neurological symptoms, an aspect that complicates the clinical picture. However, no systematic description of symptoms that should prompt a neurological investigation has been provided. This review aims to summarize available case reports describing patients with brain tumors showing psychiatric symptoms before brain tumor diagnosis, in order to provide a comprehensive description of these symptoms as well as their potential relationship with delay in the diagnosis. A systematic literature review on case reports of brain tumors and psychiatric symptoms from 1970 to 2020 was conducted on PubMed, Ovid, Psych Info, and MEDLINE. Exclusion criteria comprised tumors not included in the World Health Organization (WHO) Classification 4th edition and cases in which psychiatric symptoms were absent or followed the diagnosis. A total of 165 case reports were analyzed. In a subset of patients with brain tumors, psychiatric symptoms can be the only manifestation or precede focal neurological signs by months or even years. The appearance of focal or generalized neurological symptoms after, rather than along with, psychiatric symptoms was associated with a significant delay in the diagnosis in adults. A timely assessment of psychiatric symptoms might help to improve early diagnosis of brain tumors.

Targeted brain tumor imaging by using discrete biopolymer-coated nanodiamonds across the blood-brain barrier

Short circulation lifetime, poor blood-brain barrier (BBB) permeability and low targeting specificity limit nanovehicles from crossing the vascular barrier and reaching the tumor site. Consequently, the precise diagnosis of malignant brain tumors remains a great challenge. This study demonstrates the imaging of photostable biopolymer-coated nanodiamonds (NDs) with tumor targeting properties inside the brain. NDs are labeled with PEGylated denatured bovine serum albumin (BSA) and tumor vasculature targeting tripeptides RGD. The modified NDs show high colloidal stability in different buffer systems. Moreover, it is found that discrete dcBSA-PEG-NDs cross the in vitro BBB model more effectively than aggregated NDs. Importantly, compared with the non-targeting NDs, RGD-dcBSA-PEG-NDs can selectively target the tumor site in U-87 MG bearing mice after systemic injection. Overall, this discrete ND system enables efficacious brain tumor visualization with minimal toxicity to other major organs, and is worthy of further investigation into the applications as a unique platform for noninvasive theragnostics and/or thermometry at different stages of human diseases in the brain.

Prenylated 4-phenylcoumarins and 4-alkylcoumarins from Kielmeyera argentea and their cytotoxic activity

Two new prenylated 4-phenylcoumarins, named kielcoumarin A (1) and kielcoumarin B (2) together with three known compounds, mammea B/BA (3), mammea B/BA cyclo F (4) and ferruol A (5), were obtained from stems and roots of Kielmeyera argentea (Calophyllaceae). Their structures were elucidated based on spectroscopic data. Cytotoxic activity of the 4-alkylcoumarins 3-5 was evaluated in vitro against human U251 glioblastoma cell line. Compound 3 showed significative activity with EC₅₀ value of 6.6 μM while compounds 4 and 5 showed respective EC₅₀ values of 52.0 and 37.0 μM.