Brain tumors

Handling Missing MRI Data in Brain Tumors Classification Tasks: Usage of Synthetic Images vs. Duplicate Images and Empty Images

BACKGROUND

Deep-learning is widely used for lesion classification. However, in the clinic patient data often has missing images.

PURPOSE

To evaluate the use of generated, duplicate and empty(black) images for replacing missing MRI data in AI brain tumor classification tasks.

STUDY TYPE

Retrospective.

POPULATION

224 patients (local-dataset; low-grade-glioma (LGG) = 37, high-grade-glioma (HGG) = 187) and 335 patients (public-dataset (BraTS); LGG = 76, HGG = 259). The local-dataset was divided into training (64), validation (16), and internal-test-data (20), while the public-dataset was an independent test-set.

FIELD STRENGTH/SEQUENCE

T1WI, T1WI+C, T2WI, and FLAIR images (1.5T/3.0T-MR), obtained from different suppliers.

ASSESSMENT

Three image-to-image translation generative-adversarial-network (Pix2Pix-GAN) models were trained on the local-dataset, to generate T1WI, T2WI, and FLAIR images. The rating-and-preference-judgment assessment was performed by three human-readers (radiologist (MD) and two MRI-technicians). Resnet152 was used for classification, and inference was performed on both datasets, with baseline input, and with missing data replaced by 1) generated images; 2) duplication of existing images; and 3) black images.

STATISTICAL TESTS

The similarity between the generated and the original images was evaluated using the peak-signal-to-noise-ratio (PSNR) and the structural-similarity-index-measure (SSIM). Classification results were evaluated using accuracy, F1-score and the Kolmogorov-Smirnov test and distance.

RESULTS

For baseline-state, the classification model reached to accuracy = 0.93,0.82 on the local and public-datasets. For the missing-data methods, high similarity was obtained between the generated and the original images with mean PSNR = 35.65,32.94 and SSIM = 0.87,0.91 on the local and public-datasets; 39% of the generated-images were labeled as real images by the human-readers. The classification model using generated-images to replace missing images produced the highest results with mean accuracy = 0.91,0.82 compared to 0.85,0.79 for duplicated and 0.77,0.68 for use of black images; DATA CONCLUSION: The feasibility for inference classification model on an MRI dataset with missing images using the Pix2pix-GAN generated images, was shown. The stability and generalization ability of the model was demonstrated by producing consistent results on two independent datasets.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 5.

Hollow magnetic vortex nanorings loaded with quercetin encapsulated in polydopamine: A high-performance, intelligent nanotheranostic platform for enhanced tumor imaging and dual thermal treatment

Nanoparticle-mediated thermotherapeutic research strives innovative, multifunctional, efficient, and safe treatments. Our study introduces a novel nanoplatform: the hollow magnetic vortex nanorings within a polydopamine layer (HMVNp), which exhibit dual functionality as magnetic and photothermal agents. Utilizing a "Dual-mode" approach, combining an alternating magnetic field (AMF) with near-infrared (NIR) laser irradiation, HMVNp demonstrated a significant enhancement in heating efficacy (58 ± 8 %, SAR = 1441 vs 1032 W/g) over traditional solid magnetite nanoparticles coated with polydopamine (SMNp). The unique geometry larger surface area to volume ratio facilitates efficient magnetic vortex dynamics and enhanced heat transfer. Addressing the challenge of heat resistant heat shock protein (Hsp) expression, encapsulated quercetin (Q) within HMVNp leverages tumor acidity and dual-mode thermal therapy to enhance release, showing a 28.8 ± 6.81 % increase in Q loading capacity compared to traditional SMNp. Moreover, HMVNp significantly improves contrast for both magnetic resonance imaging (MRI) and photoacoustic imaging (PAI), with an approximately 62 % transverse relaxation (R2 = 81.5 vs 31.6 mM-1s-1 [Fe]). In vivo studies showed that while single treatments slowed tumor growth, dual-mode therapy with quercetin significantly reduced tumors and effectively prevented metastases. Our study highlights the potential of HMVNp/Q as a versatile agent in thermotherapeutic interventions, offering improved diagnostic imaging capabilities.

Design and experimental validation of a metamaterial-based sensor for microwave imaging in breast, lung, and brain cancer detection

This study proposes an innovative geometry of a microstrip sensor for high-resolution microwave imaging (MWI). The main intended application of the sensor is early detection of breast, lung, and brain cancer. The proposed design consists of a microstrip patch antenna fed by a coplanar waveguide with a metamaterial (MTM) layer-based lens implemented on the back side, and an artificial magnetic conductor (AMC) realized on as a separate layer. The analysis of the AMC's permeability and permittivity demonstrate that the structure exhibits negative epsilon (ENG) qualities near the antenna resonance point. In addition, reflectivity, transmittance, and absorption are also studied. The sensor prototype has been manufactures using the FR4 laminate. Excellent electrical and field characteristics of the structure are confirmed through experimental validation. At the resonance frequency of 4.56 GHz, the realized gain reaches 8.5 dBi, with 3.8 dBi gain enhancement contributed by the AMC. The suitability of the presented sensor for detecting brain tumors, lung cancer, and breast cancer has been corroborated through extensive simulation-based experiments performed using the MWI system model, which employs four copies of the proposed sensor, as well as the breast, lung, and brain phantoms. As demonstrated, the directional radiation pattern and enhanced gain of the sensor enable precise tumor size discrimination. The proposed sensor offers competitive performance in comparison the state-of-the-art sensors described in the recent literature, especially with respect to as gain, pattern directivity, and impedance matching, all being critical for MWI.

Lower ratio of IMPDH1 to IMPDH2 sensitizes gliomas to chemotherapy

Gliomas are the most common primary tumors of the central nervous system, with approximately half of patients presenting with the most aggressive form of glioblastoma. Although several molecular markers for glioma have been identified, they are not sufficient to predict the prognosis due to the extensive genetic heterogeneity within glioma. Our study reveals that the ratio of IMPDH1 to IMPDH2 expression levels serves as a molecular indicator for glioma treatment prognosis. Patients with a higher IMPDH1/IMPDH2 ratio exhibit a worse prognosis, while those with a lower ratio display a more favorable prognosis. We further demonstrate that IMPDH1 plays a crucial role in maintaining cellular GTP/GDP levels following DNA damage compared to IMPDH2. In the absence of IMPDH1, cells experience an imbalance in the GTP/GDP ratio, impairing DNA damage repair capabilities and rendering them more sensitive to TMZ. This study not only introduces a novel prognostic indicator for glioma clinical diagnosis but also offers innovative insights for precise and stratified glioma treatment.

Leveraging single-cell sequencing to classify and characterize tumor subgroups in bulk RNA-sequencing data

PURPOSE

Accurate classification of cancer subgroups is essential for precision medicine, tailoring treatments to individual patients based on their cancer subtypes. In recent years, advances in high-throughput sequencing technologies have enabled the generation of large-scale transcriptomic data from cancer samples. These data have provided opportunities for developing computational methods that can improve cancer subtyping and enable better personalized treatment strategies.

METHODS

Here in this study, we evaluated different feature selection schemes in the context of meningioma classification. To integrate interpretable features from the bulk (n = 77 samples) and single-cell profiling (∼ 10 K cells), we developed an algorithm named CLIPPR which combines the top-performing single-cell models, RNA-inferred copy number variation (CNV) signals, and the initial bulk model to create a meta-model.

RESULTS

While the scheme relying solely on bulk transcriptomic data showed good classification accuracy, it exhibited confusion between malignant and benign molecular classes in approximately ∼ 8% of meningioma samples. In contrast, models trained on features learned from meningioma single-cell data accurately resolved the sub-groups confused by bulk-transcriptomic data but showed limited overall accuracy. CLIPPR showed superior overall accuracy and resolved benign-malignant confusion as validated on n = 789 bulk meningioma samples gathered from multiple institutions. Finally, we showed the generalizability of our algorithm using our in-house single-cell (∼ 200 K cells) and bulk TCGA glioma data (n = 711 samples).

CONCLUSION

Overall, our algorithm CLIPPR synergizes the resolution of single-cell data with the depth of bulk sequencing and enables improved cancer sub-group diagnoses and insights into their biology.

Metabolic Insight into Glioma Heterogeneity: Mapping Whole Exome Sequencing to In Vivo Imaging with Stereotactic Localization and Deep Learning

Intratumoral heterogeneity (ITH) complicates the diagnosis and treatment of glioma, partly due to the diverse metabolic profiles driven by underlying genomic alterations. While multiparametric imaging enhances the characterization of ITH by capturing both spatial and functional variations, it falls short in directly assessing the metabolic activities that underpin these phenotypic differences. This gap stems from the challenge of integrating easily accessible, colocated pathology and detailed genomic data with metabolic insights. This study presents a multifaceted approach combining stereotactic biopsy with standard clinical open-craniotomy for sample collection, voxel-wise analysis of MR images, regression-based GAM, and whole-exome sequencing. This work aims to demonstrate the potential of machine learning algorithms to predict variations in cellular and molecular tumor characteristics. This retrospective study enrolled ten treatment-naïve patients with radiologically confirmed glioma. Each patient underwent a multiparametric MR scan (T1W, T1W-CE, T2W, T2W-FLAIR, DWI) prior to surgery. During standard craniotomy, at least 1 stereotactic biopsy was collected from each patient, with screenshots of the sample locations saved for spatial registration to pre-surgical MR data. Whole-exome sequencing was performed on flash-frozen tumor samples, prioritizing the signatures of five glioma-related genes: IDH1, TP53, EGFR, PIK3CA, and NF1. Regression was implemented with a GAM using a univariate shape function for each predictor. Standard receiver operating characteristic (ROC) analyses were used to evaluate detection, with AUC (area under curve) calculated for each gene target and MR contrast combination. Mean AUC for five gene targets and 31 MR contrast combinations was 0.75 ± 0.11; individual AUCs were as high as 0.96 for both IDH1 and TP53 with T2W-FLAIR and ADC, and 0.99 for EGFR with T2W and ADC. These results suggest the possibility of predicting exome-wide mutation events from noninvasive, in vivo imaging by combining stereotactic localization of glioma samples and a semi-parametric deep learning method. The genomic alterations identified, particularly in IDH1, TP53, EGFR, PIK3CA, and NF1, are known to play pivotal roles in metabolic pathways driving glioma heterogeneity. Our methodology, therefore, indirectly sheds light on the metabolic landscape of glioma through the lens of these critical genomic markers, suggesting a complex interplay between tumor genomics and metabolism. This approach holds potential for refining targeted therapy by better addressing the genomic heterogeneity of glioma tumors.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Metabolic dysregulation of tricarboxylic acid cycle and oxidative phosphorylation in glioblastoma

Glioblastoma multiforme (GBM) exhibits genetic alterations that induce the deregulation of oncogenic pathways, thus promoting metabolic adaptation. The modulation of metabolic enzyme activities is necessary to generate nucleotides, amino acids, and fatty acids, which provide energy and metabolic intermediates essential for fulfilling the biosynthetic needs of glioma cells. Moreover, the TCA cycle produces intermediates that play important roles in the metabolism of glucose, fatty acids, or non-essential amino acids, and act as signaling molecules associated with the activation of oncogenic pathways, transcriptional changes, and epigenetic modifications. In this review, we aim to explore how dysregulated metabolic enzymes from the TCA cycle and oxidative phosphorylation, along with their metabolites, modulate both catabolic and anabolic metabolic pathways, as well as pro-oncogenic signaling pathways, transcriptional changes, and epigenetic modifications in GBM cells, contributing to the formation, survival, growth, and invasion of glioma cells. Additionally, we discuss promising therapeutic strategies targeting key players in metabolic regulation. Therefore, understanding metabolic reprogramming is necessary to fully comprehend the biology of malignant gliomas and significantly improve patient survival.

Multiple Synergistic Effects of the Microglia Membrane-Bionic Nanoplatform on Mediate Tumor Microenvironment Remodeling to Amplify Glioblastoma Immunotherapy

Glioblastoma (GBM) is a lethal brain tumor with high levels of malignancy. Most chemotherapy agents show serious systemic cytotoxicity and restricted delivery effectiveness due to the impediments of the blood-brain barrier (BBB). Immunotherapy has developed great potential for aggressive tumor treatments. Disappointingly, its efficacy against GBM is hindered by the immunosuppressive tumor microenvironment (TME) and BBB. Herein, a multiple synergistic immunotherapeutic strategy against GBM was developed based on the nanomaterial-biology interaction. We have demonstrated that this BM@MnP-BSA-aPD-1 can transverse the BBB and target the TME, resulting in amplified synergetic effects of metalloimmunotherapy and photothermal immunotherapy (PTT). The journey of this nanoformulation within the TME contributed to the activation of the stimulator of the interferon gene pathway, the initiation of the immunogenic cell death effect, and the inhibition of the programmed cell death-1/programmed cell death ligand 1 (PD-1/PD-L1) signaling axis. This nanomedicine revitalizes the immunosuppressive TME and evokes the cascade effect of antitumor immunity. Therefore, the combination of BM@MnP-BSA-aPD-1 and PTT without chemotherapeutics presents favorable benefits in anti-GBM immunotherapy and exhibits immense potential for clinical translational applications.

A comprehensive survey on the use of deep learning techniques in glioblastoma

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.

Immune cell infiltration and inflammatory landscape in primary brain tumours

BACKGROUND

Primary malignant brain tumours are more than one-third of all brain tumours and despite the molecular investigation to identify cancer driver mutations, the current therapeutic options available are challenging due to high intratumour heterogeneity. In addition, an immunosuppressive and inflammatory tumour microenvironment strengthens cancer progression. Therefore, we defined an immune and inflammatory profiling of meningioma and glial tumours to elucidate the role of the immune infiltration in these cancer types.

METHODS

Using tissue microarrays of 158 brain tumour samples, we assessed CD3, CD4, CD8, CD20, CD138, Granzyme B (GzmB), 5-Lipoxygenase (5-LOX), Programmed Death-Ligand 1 (PD-L1), O-6-Methylguanine-DNA Methyltransferase (MGMT) and Transglutaminase 2 (TG2) expression by immunohistochemistry (IHC). IHC results were correlated using a Spearman correlation matrix. Transcript expression, correlation, and overall survival (OS) analyses were evaluated using public datasets available on GEPIA2 in Glioblastoma (GBM) and Lower Grade Glioma (LGG) cohorts.

RESULTS

Seven out of ten markers showed a significantly different IHC expression in at least one of the evaluated cohorts whereas CD3, CD4 and 5-LOX were differentially expressed between GBMs and astrocytomas. Correlation matrix analysis revealed that 5-LOX and GzmB expression were associated in both meningiomas and GBMs, whereas 5-LOX expression was significantly and positively correlated to TG2 in both meningioma and astrocytoma cohorts. These findings were confirmed with the correlation analysis of TCGA-GBM and LGG datasets. Profiling of mRNA levels indicated a significant increase in CD3 (CD3D, CD3E), and CD138 (SDC1) expression in GBM compared to control tissues. CD4 and 5-LOX (ALOX5) mRNA levels were significantly more expressed in tumour samples than in normal tissues in both GBM and LGG. In GBM cohort, GzmB (GZMB), SDC1 and MGMT gene expression predicted a poor overall survival (OS). Moreover, in LGG cohort, an increased expression of CD3 (CD3D, CD3E, CD3G), CD8 (CD8A), GZMB, CD20 (MS4A1), SDC1, PD-L1, ALOX5, and TG2 (TGM2) genes was associated with worse OS.

CONCLUSIONS

Our data have revealed that there is a positive and significant correlation between the expression of 5-LOX and GzmB, both at RNA and protein level. Further evaluation is needed to understand the interplay of 5-LOX and immune infiltration in glioma progression.

Head model dataset for mixed reality navigation in neurosurgical interventions for intracranial lesions

Mixed reality navigation (MRN) technology is emerging as an increasingly significant and interesting topic in neurosurgery. MRN enables neurosurgeons to "see through" the head with an interactive, hybrid visualization environment that merges virtual- and physical-world elements. Offering immersive, intuitive, and reliable guidance for preoperative and intraoperative intervention of intracranial lesions, MRN showcases its potential as an economically efficient and user-friendly alternative to standard neuronavigation systems. However, the clinical research and development of MRN systems present challenges: recruiting a sufficient number of patients within a limited timeframe is difficult, and acquiring low-cost, commercially available, medically significant head phantoms is equally challenging. To accelerate the development of novel MRN systems and surmount these obstacles, the study presents a dataset designed for MRN system development and testing in neurosurgery. It includes CT and MRI data from 19 patients with intracranial lesions and derived 3D models of anatomical structures and validation references. The models are available in Wavefront object (OBJ) and Stereolithography (STL) formats, supporting the creation and assessment of neurosurgical MRN applications.

Simulating the Impact of Tumor Mechanical Forces on Glymphatic Networks in the Brain Parenchyma

Background

The brain glymphatic system is currently being explored in the context of many neurological disorders and diseases, including traumatic brain injury, Alzheimer's disease, and ischemic stroke. However, little is known about the impact of brain tumors on glymphatic function. Mechanical forces generated during tumor development and growth may be responsible for compromised glymphatic transport pathways, reducing waste clearance and cerebrospinal fluid (CSF) transport in the brain parenchyma. One such force is solid stress, i.e., growth-induced forces from cell hyperproliferation and excess matrix deposition. Because there are no prior studies assessing the impact of tumor-derived solid stress on glymphatic system structure and performance in the brain parenchyma, this study serves to fill an important gap in the field.

Methods

We adapted a previously developed Electrical Analog Model using MATLAB Simulink for glymphatic transport coupled with Finite Element Analysis for tumor mechanical stresses and strains in COMSOL. This allowed simulation of the impact of tumor mechanical force generation on fluid transport within brain parenchymal glymphatic units - which include paravascular spaces, astrocytic networks, interstitial spaces, and capillary basement membranes. We conducted a parametric analysis to compare the contributions of tumor size, tumor proximity, and ratio of glymphatic subunits to the stress and strain experienced by the glymphatic unit and corresponding reduction in flow rate of CSF.

Results

Mechanical stresses intensify with proximity to the tumor and increasing tumor size, highlighting the vulnerability of nearby glymphatic units to tumor-derived forces. Our stress and strain profiles reveal compressive deformation of these surrounding glymphatics and demonstrate that varying the relative contributions of astrocytes vs. interstitial spaces impact the resulting glymphatic structure significantly under tumor mechanical forces. Increased tumor size and proximity caused increased stress and strain across all glymphatic subunits, as does decreased astrocyte composition. Indeed, our model reveals an inverse correlation between extent of astrocyte contribution to the composition of the glymphatic unit and the resulting mechanical stress. This increased mechanical strain across the glymphatic unit decreases the venous efflux rate of CSF, dependent on the degree of strain and the specific glymphatic subunit of interest. For example, a 20% mechanical strain on capillary basement membranes does not significantly decrease venous efflux (2% decrease in flow rates), while the same magnitude of strain on astrocyte networks and interstitial spaces decreases efflux flow rates by 7% and 22%, respectively.

Conclusion

Our simulations reveal that solid stress from brain tumors directly reduces glymphatic fluid transport, independently from biochemical effects from cancer cells. Understanding these pathophysiological implications is crucial for developing targeted interventions aimed at restoring effective waste clearance mechanisms in the brain.This study opens potential avenues for future experimental research in brain tumor-related glymphatic dysfunction.

SUPRAMAX-study: supramaximal resection versus maximal resection for glioblastoma patients: study protocol for an international multicentre prospective cohort study (ENCRAM 2201)

INTRODUCTION

A greater extent of resection of the contrast-enhancing (CE) tumour part has been associated with improved outcomes in glioblastoma. Recent results suggest that resection of the non-contrast-enhancing (NCE) part might yield even better survival outcomes (supramaximal resection, SMR). Therefore, this study evaluates the efficacy and safety of SMR with and without mapping techniques in high-grade glioma (HGG) patients in terms of survival, functional, neurological, cognitive and quality of life outcomes. Furthermore, it evaluates which patients benefit the most from SMR, and how they could be identified preoperatively.

METHODS AND ANALYSIS

This study is an international, multicentre, prospective, two-arm cohort study of observational nature. Consecutive glioblastoma patients will be operated with SMR or maximal resection at a 1:1 ratio. Primary endpoints are (1) overall survival and (2) proportion of patients with National Institute of Health Stroke Scale deterioration at 6 weeks, 3 months and 6 months postoperatively. Secondary endpoints are (1) residual CE and NCE tumour volume on postoperative T1-contrast and FLAIR (Fluid-attenuated inversion recovery) MRI scans; (2) progression-free survival; (3) receipt of adjuvant therapy with chemotherapy and radiotherapy; and (4) quality of life at 6 weeks, 3 months and 6 months postoperatively. The 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.

Design, synthesis and biological evaluation of Nrf2 modulators for the treatment of glioblastoma multiforme

Glioblastoma multiforme (GBM) is a prevalent primary brain tumor. However, no specific therapeutic drug has been developed for it. Nuclear factor erythroid 2-related factor 2 (Nrf2) is a crucial transcription factor involved in the cellular response to oxidative stress. Numerous studies have demonstrated that Nrf2 plays a pivotal role in GBM angiogenesis, and inhibiting Nrf2 can significantly enhance patient prognosis. Using virtual screening technology, we examined our in-house library and identified pinosylvin as a potential compound with high activity. Pinosylvin exhibited robust hydrogen bond and Π-Π interaction with Nrf2. Cell experiments revealed that pinosylvin effectively reduced the proliferation of U87 tumor cells by regulating Nrf2 and demonstrated greater inhibitory activity than temozolomide. Consequently, we believe that this study will offer valuable guidance for the future development of highly efficient therapeutic drugs for GBM.

[Brain metastases]

Cerebral metastases in patients with metastatic lung cancer are found in more than 30% of patients at baseline and manifest themselves in two out of three patients during disease evolution. For a long time, the cerebral manifestation of the disease was classified as prognostically unfavorable and hence such patients were regularly excluded from therapy studies. In the context of targeted molecular therapy strategies and established immuno-oncological systemic therapies, the blood-brain barrier no longer represents an insurmountable barrier. However, the treatment of brain metastases requires decision making in a multidisciplinary team within dedicated lung cancer and/or oncology centers. The differentiated treatment decision is based on the number, size and location of the brain metastases, neurology and general condition, comorbidities, potential life expectancy and the patient's wishes, but also tumor biology including molecular targets, extra-cranial tumor burden and availability of a CNS-effective therapy. Systemic therapies as well as neurosurgical and radiotherapeutic concepts are now often combined for optimized and prognosis-improving therapeutic strategies.

Extracellular Matrix Cues Regulate Mechanosensing and Mechanotransduction of Cancer Cells

Extracellular biophysical properties have particular implications for a wide spectrum of cellular behaviors and functions, including growth, motility, differentiation, apoptosis, gene expression, cell-matrix and cell-cell adhesion, and signal transduction including mechanotransduction. Cells not only react to unambiguously mechanical cues from the extracellular matrix (ECM), but can occasionally manipulate the mechanical features of the matrix in parallel with biological characteristics, thus interfering with downstream matrix-based cues in both physiological and pathological processes. Bidirectional interactions between cells and (bio)materials in vitro can alter cell phenotype and mechanotransduction, as well as ECM structure, intentionally or unintentionally. Interactions between cell and matrix mechanics in vivo are of particular importance in a variety of diseases, including primarily cancer. Stiffness values between normal and cancerous tissue can range between 500 Pa (soft) and 48 kPa (stiff), respectively. Even the shear flow can increase from 0.1-1 dyn/cm2 (normal tissue) to 1-10 dyn/cm2 (cancerous tissue). There are currently many new areas of activity in tumor research on various biological length scales, which are highlighted in this review. Moreover, the complexity of interactions between ECM and cancer cells is reduced to common features of different tumors and the characteristics are highlighted to identify the main pathways of interaction. This all contributes to the standardization of mechanotransduction models and approaches, which, ultimately, increases the understanding of the complex interaction. Finally, both the in vitro and in vivo effects of this mechanics-biology pairing have key insights and implications for clinical practice in tumor treatment and, consequently, clinical translation.

Neuroinflammation in Glioblastoma: The Role of the Microenvironment in Tumour Progression

Glioblastoma (GBM) stands as the most aggressive and lethal among the main types of primary brain tumors. It exhibits malignant growth, infiltrating the brain tissue, and displaying resistance toward treatment. GBM is a complex disease characterized by high degrees of heterogeneity. During tumour growth, microglia and astrocytes, among other cells, infiltrate the tumour microenvironment and contribute extensively to gliomagenesis. Tumour-associated macrophages (TAMs), either of peripheral origin or representing brain-intrinsic microglia, are the most numerous nonneoplastic populations in the tumour microenvironment in GBM. The complex heterogeneous nature of GBM cells is facilitated by the local inflammatory tumour microenvironment, which mostly induces tumour aggressiveness and drug resistance. The immunosuppressive tumour microenvironment of GBM provides multiple pathways for tumour immune evasion, contributing to tumour progression. Additionally, TAMs and astrocytes can contribute to tumour progression through the release of cytokines and activation of signalling pathways. In this review, we summarize the role of the microenvironment in GBM progression, focusing on neuroinflammation. These recent advancements in research of the microenvironment hold the potential to offer a promising approach to the treatment of GBM in the coming times.

Spatiotemporally sequential delivery of biomimetic liposomes potentiates glioma chemotherapy

As one of the most challenging cancers, glioma still lacks efficient therapeutic treatment in clinics. The dilemmas of nanodrug-based therapies for glioma are due not only the limited permeability of the blood-brain barrier (BBB) but also the deficiency of targeting tumor lesions. Thus, spatiotemporally sequential delivery of therapeutics from BBB-crossing to glioma accumulation is considered a strategy to obtain better outcomes. Here, we developed a biomimetic chemotherapy nanodrug composed of the hybrid membrane envelope of U87 cell membranes and RAW264.7 cell membranes, and the core of paclitaxel (PTX)-loaded liposome (PTX@C-MMCL). In the research, PTX@C-MMCL showed superior ability to cross the BBB via RAW264.7 cell membranes and accurate targeting to the brain tumor lesions relying on the homotypic targeting capacity of U87 cell membranes. Furthermore, PTX@C-MMCL can maintain a prolonged circulation in vivo. Importantly, PTX@C-MMCL effectively inhibited the development of glioma. Conclusively, our biomimetic nanodrug holds great potential for brain tumor targeting therapy.

Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets

BACKGROUND

Convolutional neural networks (CNNs) have produced state-of-the-art results in meningioma segmentation on magnetic resonance imaging (MRI). However, images obtained from different institutions, protocols, or scanners may show significant domain shift, leading to performance degradation and challenging model deployment in real clinical scenarios.

OBJECTIVE

This research aims to investigate the realistic performance of a well-trained meningioma segmentation model when deployed across different health care centers and verify the methods to enhance its generalization.

METHODS

This study was performed in four centers. A total of 606 patients with 606 MRIs were enrolled between January 2015 and December 2021. Manual segmentations, determined through consensus readings by neuroradiologists, were used as the ground truth mask. The model was previously trained using a standard supervised CNN called Deeplab V3+ and was deployed and tested separately in four health care centers. To determine the appropriate approach to mitigating the observed performance degradation, two methods were used: unsupervised domain adaptation and supervised retraining.

RESULTS

The trained model showed a state-of-the-art performance in tumor segmentation in two health care institutions, with a Dice ratio of 0.887 (SD 0.108, 95% CI 0.903-0.925) in center A and a Dice ratio of 0.874 (SD 0.800, 95% CI 0.854-0.894) in center B. Whereas in the other health care institutions, the performance declined, with Dice ratios of 0.631 (SD 0.157, 95% CI 0.556-0.707) in center C and 0.649 (SD 0.187, 95% CI 0.566-0.732) in center D, as they obtained the MRI using different scanning protocols. The unsupervised domain adaptation showed a significant improvement in performance scores, with Dice ratios of 0.842 (SD 0.073, 95% CI 0.820-0.864) in center C and 0.855 (SD 0.097, 95% CI 0.826-0.886) in center D. Nonetheless, it did not overperform the supervised retraining, which achieved Dice ratios of 0.899 (SD 0.026, 95% CI 0.889-0.906) in center C and 0.886 (SD 0.046, 95% CI 0.870-0.903) in center D.

CONCLUSIONS

Deploying the trained CNN model in different health care institutions may show significant performance degradation due to the domain shift of MRIs. Under this circumstance, the use of unsupervised domain adaptation or supervised retraining should be considered, taking into account the balance between clinical requirements, model performance, and the size of the available data.

LncRNA-associated competing endogenous RNA network analysis uncovered key lncRNAs involved in temozolomide resistance and tumor recurrence of glioblastoma

Temozolomide (TMZ) is a common alkylating chemotherapeutic agent used to treat brain tumors such as glioblastoma multiforme (GBM) and anaplastic astrocytoma. GBM patients develop resistance to this drug, which has an unclear and complicated molecular mechanism. The competing endogenous RNAs (ceRNAs) play critical roles in tumorigenesis, drug resistance, and tumor recurrence in cancers. This study aims to predict ceRNAs, their possible involvement, and underlying molecular mechanisms in TMZ resistance. Therefore, we analyzed coding and non-coding RNA expression levels in TMZ-resistant GBM samples compared to sensitive GBM samples and performed pathway analysis of mRNAs differentially expressed (DE) in TMZ-resistant samples. We next applied a mathematical model on 950 DE long non-coding RNAs (lncRNAs), 116 microRNAs (miRNAs), and 7977 mRNAs and obtained 10 lncRNA-associated ceRNAs that may be regulating potential target genes involved in cancer-related pathways by sponging 25 miRNAs in TMZ-resistant GBM. Among these, two lncRNAs named ARFRP1 and RUSC2 regulate five target genes (IRS1, FOXG1, GNG2, RUNX2, and CACNA1E) involved in AMPK, AKT, mTOR, and TGF-β signaling pathways that activate or inhibit autophagy causing TMZ resistance. The novel lncRNA-associated ceRNA network predicted in GBM offers a fresh viewpoint on TMZ resistance, which might contribute to treating this malignancy.

Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures

BACKGROUND

Studies have shown that deep-learning radiomics (DLR) could help differentiate glioblastoma (GBM) from solitary brain metastasis (SBM), but whether integrating demographic-MRI and DLR features can more accurately distinguish GBM from SBM remains uncertain.

PURPOSE

To construct and validate a demographic-MRI deep-learning radiomics nomogram (DDLRN) integrating demographic-MRI and DLR signatures to differentiate GBM from SBM preoperatively.

STUDY TYPE

Retrospective.

POPULATION

Two hundred and thirty-five patients with GBM (N = 115) or SBM (N = 120), randomly divided into a training cohort (90 GBM and 98 SBM) and a validation cohort (25 GBM and 22 SBM).

FIELD STRENGTH/SEQUENCE

Axial T2-weighted fast spin-echo sequence (T2WI), T2-weighted fluid-attenuated inversion recovery sequence (T2-FLAIR), and contrast-enhanced T1-weighted spin-echo sequence (CE-T1WI) using 1.5-T and 3.0-T scanners.

ASSESSMENT

The demographic-MRI signature was constructed with seven imaging features ("pool sign," "irregular ring sign," "regular ring sign," "intratumoral vessel sign," the ratio of the area of peritumoral edema to the enhanced tumor, the ratio of the lesion area on T2-FLAIR to CE-T1WI, and the tumor location) and demographic factors (age and sex). Based on multiparametric MRI, radiomics and deep-learning (DL) models, DLR signature, and DDLRN were developed and validated.

STATISTICAL TESTS

The Mann-Whitney U test, Pearson test, least absolute shrinkage and selection operator, and support vector machine algorithm were applied for feature selection and construction of radiomics and DL models.

RESULTS

DDLRN showed the best performance in differentiating GBM from SBM with area under the curves (AUCs) of 0.999 and 0.947 in the training and validation cohorts, respectively. Additionally, the DLR signature (AUC = 0.938) outperformed the radiomics and DL models, and the demographic-MRI signature (AUC = 0.775) was comparable to the T2-FLAIR radiomics and DL models in the validation cohort (AUC = 0.762 and 0.749, respectively).

DATA CONCLUSION

DDLRN integrating demographic-MRI and DLR signatures showed excellent performance in differentiating GBM from SBM.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

Advances and Applications of Brain Organoids

Understanding the fundamental processes of human brain development and diseases is of great importance for our health. However, existing research models such as non-human primate and mouse models remain limited due to their developmental discrepancies compared with humans. Over the past years, an emerging model, the "brain organoid" integrated from human pluripotent stem cells, has been developed to mimic developmental processes of the human brain and disease-associated phenotypes to some extent, making it possible to better understand the complex structures and functions of the human brain. In this review, we summarize recent advances in brain organoid technologies and their applications in brain development and diseases, including neurodevelopmental, neurodegenerative, psychiatric diseases, and brain tumors. Finally, we also discuss current limitations and the potential of brain organoids.

Evaluation of multimodal MR imaging for differentiating infiltrative versus reactive edema in brain gliomas

OBJECTIVE

To determine the border of glial tumors by diffusion weighted imaging (DWI), apparent diffusion co-efficient (ADC), magnetic resonance spectroscopy (MRS) and perfusion brain MRI.

PATIENTS AND METHODS

Ten patients with brain gliomas were enrolled [mean age: 35.3 ± 13.2, range: 20-62]. Conventional MRI was performed for all patients. Besides, tumor mapping based on Choline (Cho)/Creatine (Cr) color map in MRS, perfusion and diffusion color maps, were gathered. Different tumoral and peritumoral regions [normal tissue, reactive edema, infiltrative edema, and tumor core] were defined. MRI criteria were evaluated in areas targeted for biopsy and histopathologic evaluation was determined.

RESULTS

Tumor cell positive samples [one necrosis, 26 infiltrative and nine tumor cores] composed 36 (75%) of the 48 samples. Seven (19.4%) of the positive samples were interpreted as not tumor on MRI. Five were identified as reactive edema and two as normal tissue] [kappa: .67, p-value < .001]. Mean of ADC, median of N-acetylaspartate (NAA) and NAA/Cho were statistically different between positive and negative samples (p = .02 and p < .001, respectively). Mean ADC and median Cho/NAA were statistically different in missed tumor containing tissue presented as reactive edema compared to normal and correctly diagnosed reactive edema samples together (p-values < .05).

CONCLUSIONS

Multimodal MRI could define infiltrated borders of brain gliomas.

UniVisNet: A Unified Visualization and Classification Network for accurate grading of gliomas from MRI

Accurate grading of brain tumors plays a crucial role in the diagnosis and treatment of glioma. While convolutional neural networks (CNNs) have shown promising performance in this task, their clinical applicability is still constrained by the interpretability and robustness of the models. In the conventional framework, the classification model is trained first, and then visual explanations are generated. However, this approach often leads to models that prioritize classification performance or complexity, making it difficult to achieve a precise visual explanation. Motivated by these challenges, we propose the Unified Visualization and Classification Network (UniVisNet), a novel framework that aims to improve both the classification performance and the generation of high-resolution visual explanations. UniVisNet addresses attention misalignment by introducing a subregion-based attention mechanism, which replaces traditional down-sampling operations. Additionally, multiscale feature maps are fused to achieve higher resolution, enabling the generation of detailed visual explanations. To streamline the process, we introduce the Unified Visualization and Classification head (UniVisHead), which directly generates visual explanations without the need for additional separation steps. Through extensive experiments, our proposed UniVisNet consistently outperforms strong baseline classification models and prevalent visualization methods. Notably, UniVisNet achieves remarkable results on the glioma grading task, including an AUC of 94.7%, an accuracy of 89.3%, a sensitivity of 90.4%, and a specificity of 85.3%. Moreover, UniVisNet provides visually interpretable explanations that surpass existing approaches. In conclusion, UniVisNet innovatively generates visual explanations in brain tumor grading by simultaneously improving the classification performance and generating high-resolution visual explanations. This work contributes to the clinical application of deep learning, empowering clinicians with comprehensive insights into the spatial heterogeneity of glioma.

The effect of long-term exposure to toxic air pollutants on the increased risk of malignant brain tumors

Toxic air pollutants are one of the most agent that have many acute, chronic and non-communicable diseases (NCDs) on human health under long or short-term exposure has been raised from the past to the present. The aim of this study was investigation effect of long-term exposure to toxic air pollutants on the increased risk of malignant brain tumors. Databases used to for searched were the PubMed, Web of Science, Springer and Science Direct (Scopus) and Google Scholar. 71 papers based on abstract and article text filtered. In the end after sieve we selected 7 papers. Identify all relevant studies published 1970-2022. The literature showed that exposure to toxic air pollutants and their respiration can cause disorders in different parts of the brain by transmission through the circulatory system and other mechanisms. Various unpleasant abnormalities are caused by the inhalation of toxic air pollutants in the human body that some of the most common of them include chronic lung disease, coronary heart disease and heart attacks, strokes and brain diseases (Parkinson's, Alzheimer's and multiple Sclerosis), cancers (liver, blood, prostate and brain) and eventually death. According to the finding brain health and proper functioning can be easily disrupted by various genetic or external factors such as air pollution, causing a wide range of abnormalities in the brain and malignant brain tumors. The results of this study showed that reducing the concentration of toxic pollutants in the air, that exposure to them play an increasing role in the development of brain diseases can slow down the process of abnormalities in the brain and will have significant impacts on reducing the number of people affected by them.

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

Clinicogenetic characteristics and the effect of radiation on the neural stem cell niche in subventricular zone-contacting glioblastoma

BACKGROUND AND PURPOSE

Neural stem cells (NSCs) in the subventricular zone (SVZ) are recognized as the cellular origin of glioblastoma (GBM) and a potential therapeutic target. However, the characteristics of SVZ contacting GBM (SVZ + GBM) and radiotherapeutic strategies for NSCs are still controversial. Here, we investigated the clinicogenetic features of SVZ + GBM and evaluated the dose effect of NSC irradiation depending on SVZ involvement.

MATERIALS AND METHODS

We identified 125 patients with GBM treated with surgery followed by chemoradiotherapy. The genomic profiles were obtained by next-generation sequencing targeting 82 genes. NSCs in the SVZ and hippocampus were contoured using standardized methods, and dosimetric factors were analyzed. SVZ + GBM was defined as GBM with SVZ involvement in a T1 contrast-enhanced image. Progression-free survival (PFS) and overall survival (OS) were used as endpoints.

RESULTS

The number of patients with SVZ + GBM was 95 (76%). SVZ + GBM showed lower PFS than GBM without SVZ involvement (SVZ-GBM) (median 8.6 vs. 11.5 months, p = 0.034). SVZ contact was not associated with any specific genetic profile but was an independent prognostic factor in multivariate analysis. In SVZ + GBM, patients receiving high doses to the ipsilateral NSC region showed significantly better OS (HR = 1.89, p = 0.011) and PFS (HR = 1.77, p = 0.013). However, in SVZ-GBM, high doses to the ipsilateral NSC region were associated with worse OS (HR = 0.27, p = 0.013) and PFS (HR = 0.37, p = 0.035) in both univariate and multivariate analyses.

CONCLUSION

SVZ involvement in GBM was not associated with distinct genetic features. However, irradiation of NSCs was associated with better prognosis in patients with tumors contacting the SVZ.

Diagnostic Value of Nestin Expression in Adult Gliomas

Introduction. Nestin, a type VI intermediate filament, is expressed in neuroepithelial cells during embryogenesis and has been expressed in various human tumors. Recent studies reported that the expression was associated with poor prognosis in brain tumors, but the results were inconclusive. In this study, we evaluated usefulness of nestin expression as a prognostic biomarker in consideration of IDH mutation and the World Health Organization (WHO) classification fifth edition. Methods. To investigate nestin expression, immunohistochemistry was performed on 92 adult brain gliomas using tissue microarrays. We analyzed the clinical characteristics and survival outcomes according to nestin expression and examined whether nestin expression alone affects the prognosis, independent of IDH mutation. Results. Sixty patients (65.2%) were nestin-positive (weak and strong). Nestin expression and intensity were significantly correlated with pathologic diagnosis and IDH mutation. The patients with high-grade gliomas showed a higher frequency and stronger intensity of nestin expression than those with low-grade gliomas (P < .001). The majority (93.6%) of gliomas with IDH mutation showed no expression or weak positivity. Multivariate Cox proportional hazard regression analysis for overall survival demonstrated that nestin expression (weak, hazard ratio [HR] 5.39, P = .036; strong, HR 8.43, P = .007) was an independent prognostic factor. Moreover, patients with nestin-expressing glioma showed shorter survival (P < .001). Conclusions. Nestin seems to be strongly expressed in the vast majority of glioblastomas, IDH-wildtype and rarely in IDH-mutant gliomas. Clear correlation between nestin expression and pathologic diagnosis makes an accurate patient diagnosis. Expression and intensity of nestin were significantly correlated with worse survival.

Neuropsychological impairment in primary malignant brain tumor patients with awake craniotomy: a hospital-based registration study

PURPOSE

Neuroplasticity is an ability to maintain neural circuit function when facing damages. It is one of the reasons that making brain tumors notorious. Therefore, we evaluated the characteristics of patients with primary brain tumors, compared neuropsychological deficits between patients who had awake craniotomy with left- or right-sided tumors, and analyzed the association between white matter tracts and neuropsychological deficits in patients with right-sided tumors.

METHODS

Using the registration dataset of Chang Gung Memory Hospital between 2014 and 2020, this study included a total of 698 adult patients who received craniotomy for primary brain tumors (538 of conventional craniotomy; 160 of awake craniotomy). Neuropsychological assessments were arranged in patients as preoperative evaluation for awake craniotomies.

RESULTS

A lower proportion of right-sided tumors was noted in patient who had awake craniotomy than those who had conventional craniotomy (33.8% and 51.5%, p < 0.001). In awake craniotomy, 88.7% of patients with left-sided tumors and 77.8% of patients with right-sided tumors had neuropsychological impairment. Patients with left-sided tumors had worse preoperative performance compared to those with right-sided tumors in global function (36.2% and 8.0%, p < 0.001), language domain (57.6% and 22.2%, p < 0.001), and attention (36.0% and 18.5%, p = 0.02). Furthermore, in those with right-sided low-grade gliomas, patients involving pathway of superior longitudinal fasciculus (SLF) I had a higher risk of deficits than those without involvement in verbal memory (p = 0.001, Odd ratio = 11.2, 95% CI = 1.8 ~ 71.4) and visual memory (p = 0.048, Odd ratio = 10.5, 95% CI = 1.0 ~ 111).

CONCLUSION

In awake craniotomy, patients with left-sided brain tumors had worse cognitive function than those with right-sided tumors in terms of global function, language, and attention. 77% of patients with right-sided tumors had neuropsychological impairment. Therefore, a comprehensive neuropsychological evaluation and awake craniotomy are necessary for patients with brain tumors.

Spatial distribution and functional relevance of FGFR1 and FGFR2 expression for glioblastoma tumor invasion

Glioblastoma is the most lethal brain cancer in adults. These incurable tumors are characterized by profound heterogeneity, therapy resistance, and diffuse infiltration. These traits have been linked to cancer stem cells, which are important for glioblastoma tumor progression and recurrence. The fibroblast growth factor receptor 1 (FGFR1) signaling pathway is a known regulator of therapy resistance and cancer stemness in glioblastoma. FGFR1 expression shows intertumoral heterogeneity and higher FGFR1 expression is associated with a significantly poorer survival in glioblastoma patients. The role of FGFR1 in tumor invasion has been studied in many cancers, but whether and how FGFR1 mediates glioblastoma invasion remains to be determined. Here, we investigated the distribution and functional relevance of FGFR1 and FGFR2 in human glioblastoma xenograft models. We found FGFR1, but not FGFR2, expressed in invasive glioblastoma cells. Loss of FGFR1, but not FGFR2, significantly reduced cell migration in vitro and tumor invasion in human glioblastoma xenografts. Comparative analysis of RNA-sequencing data of FGFR1 and FGFR2 knockdown glioblastoma cells revealed a FGFR1-specific gene regulatory network associated with tumor invasion. Our study reveals new gene candidates linked to FGFR1-mediated glioblastoma invasion.

Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features

Brain tumors, along with other diseases that harm the neurological system, are a significant contributor to global mortality. Early diagnosis plays a crucial role in effectively treating brain tumors. To distinguish individuals with tumors from those without, this study employs a combination of images and data-based features. In the initial phase, the image dataset is enhanced, followed by the application of a UNet transfer-learning-based model to accurately classify patients as either having tumors or being normal. In the second phase, this research utilizes 13 features in conjunction with a voting classifier. The voting classifier incorporates features extracted from deep convolutional layers and combines stochastic gradient descent with logistic regression to achieve better classification results. The reported accuracy score of 0.99 achieved by both proposed models shows its superior performance. Also, comparing results with other supervised learning algorithms and state-of-the-art models validates its performance.

Ferroptosis and PPAR-gamma in the limelight of brain tumors and edema

Human malignant brain tumors such as gliomas are devastating due to the induction of cerebral edema and neurodegeneration. A major contributor to glioma-induced neurodegeneration has been identified as glutamate. Glutamate promotes cell growth and proliferation in variety of tumor types. Intriguently, glutamate is also an excitatory neurotransmitter and evokes neuronal cell death at high concentrations. Even though glutamate signaling at the receptor and its downstream effectors has been extensively investigated at the molecular level, there has been little insight into how glutamate enters the tumor microenvironment and impacts on metabolic equilibration until recently. Surprisingly, the 12 transmembrane spanning tranporter xCT (SLC7A11) appeared to be a major player in this process, mediating glutamate secretion and ferroptosis. Also, PPARγ is associated with ferroptosis in neurodegeneration, thereby destroying neurons and causing brain swelling. Although these data are intriguing, tumor-associated edema has so far been quoted as of vasogenic origin. Hence, glutamate and PPARγ biology in the process of glioma-induced brain swelling is conceptually challenging. By inhibiting xCT transporter or AMPA receptors in vivo, brain swelling and peritumoral alterations can be mitigated. This review sheds light on the role of glutamate in brain tumors presenting the conceptual challenge that xCT disruption causes ferroptosis activation in malignant brain tumors. Thus, interfering with glutamate takes center stage in forming the basis of a metabolic equilibration approach.

Proapoptotic and proautophagy effect of H1-receptor antagonist desloratadine in human glioblastoma cell lines

Glioblastomas are aggressive and usually incurable high-grade gliomas without adequate treatment. In this study, we aimed to investigate the potential of desloratadine to induce apoptosis/autophagy as genetically regulated processes that can seal cancer cell fates. All experiments were performed on U251 human glioblastoma cell line and primary human glioblastoma cell culture. Cytotoxic effect of desloratadine was investigated using MTT and CV assays, while oxidative stress, apoptosis, and autophagy were detected by flow cytometry and immunoblot. Desloratadine treatment decreased cell viability of U251 human glioblastoma cell line and primary human glioblastoma cell culture (IC50 value 50 µM) by an increase of intracellular reactive oxygen species and caspase activity. Also, desloratadine decreased the expression of main autophagy repressor mTOR and its upstream activator Akt and increased the expression of AMPK. Desloratadine exerted dual cytotoxic effect inducing both apoptosis- and mTOR/AMPK-dependent cytotoxic autophagy in glioblastoma cells and primary glioblastoma cell culture.

mTORC2 interactome and localization determine aggressiveness of high-grade glioma cells through association with gelsolin

mTOR complex 2 (mTORC2) has been implicated as a key regulator of glioblastoma cell migration. However, the roles of mTORC2 in the migrational control process have not been entirely elucidated. Here, we elaborate that active mTORC2 is crucial for GBM cell motility. Inhibition of mTORC2 impaired cell movement and negatively affected microfilament and microtubule functions. We also aimed to characterize important players involved in the regulation of cell migration and other mTORC2-mediated cellular processes in GBM cells. Therefore, we quantitatively characterized the alteration of the mTORC2 interactome under selective conditions using affinity purification-mass spectrometry in glioblastoma. We demonstrated that changes in cell migration ability specifically altered mTORC2-associated proteins. GSN was identified as one of the most dynamic proteins. The mTORC2-GSN linkage was mostly highlighted in high-grade glioma cells, connecting functional mTORC2 to multiple proteins responsible for directional cell movement in GBM. Loss of GSN disconnected mTORC2 from numerous cytoskeletal proteins and affected the membrane localization of mTORC2. In addition, we reported 86 stable mTORC2-interacting proteins involved in diverse molecular functions, predominantly cytoskeletal remodeling, in GBM. Our findings might help expand future opportunities for predicting the highly migratory phenotype of brain cancers in clinical investigations.

Establishment and validation of a novel integrin-based prognostic gene signature that sub-classifies gliomas and effectively predicts immunosuppressive microenvironment

The integrin family members play a key role in cancer immunomodulation and prognosis. We comprehensively analyzed the expression patterns and clinical significance of integrin family-related genes in gliomas. A total of 2293 gliomas from the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and Gliovis platform were enrolled for analyses. Twenty-six integrin coding genes showed different expression patterns between glioma and normal brain tissues. We screened an integrin family-related gene signature (ITGA5, ITGA9, ITGAE, ITGB7 and ITGB8) that showed independent prognostic value and sub-classified gliomas into different prognostic and molecular clusters, further composed an integrin-based risk score model associated with glioma malignant clinical features, overall survival (OS), and immune microenvironment alterations. Besides, glioma patients with high-risk scores showed chemotherapeutic resistance and more immune cells infiltration as well as high immune checkpoints expression. Concurrently, we also revealed that high-risk score group presented resistance to T cell-mediated cancer killing process and lower rates of response to immune checkpoint blockade (ICB) treatment. In conclusion, our study identified a valuable integrin gene signature that predicted gliomas OS effectively, and sub-classified them into different phenotypes and accompanied with immunological changes, possibly acted as a biomarker for ICB treatment.

The Evolution of Laser-Induced Thermal Therapy for the Treatment of Gliomas

Laser-induced thermal therapy (LITT) has evolved over the past two decades to treat a number of intracranial pathologies. Although it initially emerged as a salvage treatment of surgically inoperable tumors or recurrent lesions that had exhausted more conventional treatments, it is now being used as a primary, first-line treatment in certain instances with outcomes comparable to traditional surgical resection. The authors discuss the evolution of LITT in the treatment of gliomas and future directions, which may further enhance the efficacy of this procedure.

Recovery of neurosurgical high-frequency electroporation injury in the canine brain can be accelerated by 7,8-dihydroxyflavone

BACKGROUND

Although traumatic brain injury (TBI) occurs in a very short time, the biological consequence of a TBI, such as Alzheimer's disease, may last a lifetime. To date, effective interventions are not available to improve recovery from a TBI. Herein we aimed to ascertain whether recovery of neurosurgical high-frequency irreversible electroporation (HFIRE) injury in brain tissues can be accelerated by 7,8-dihydroxyflavone (7,8-DHF).

METHODS

The HFIRE injury was induced in the right parietal cortex of 8 adult healthy and neurologically intact male dogs. Two weeks before HFIRE injury, each dog was administered orally with or without 7,8-DHF (30 mg/kg) once daily for consecutive 2 weeks (n = 4 for each group). The values of blood-brain barrier (BBB) disruption, brain edema, and cerebral infarction volumes were measured. The concentrations of beta-amyloid, interleukin-1β, interleukin-6 and tumor necrosis factor-α in the cerebrospinal fluid were measured biochemically.

RESULTS

The BBB disruption, brain edema, infarction volumes, and maximal cross-section area caused by HFIRE injury in canine brain were significantly attenuated by 7,8-DHF therapy (P < 0.0001). Additionally, 7,8-DHF significantly reduced the HFIRE-induced cerebral overproduction of beta-amyloid and proinflammatory cytokines in the cerebrospinal fluid (P < 0.0001) in dogs with HFIRE.

CONCLUSIONS

Recovery of neurosurgical HFIRE injury in canine brain tissues can be accelerated by 7,8-DHT via ameliorating BBB disruption as well as cerebral overproduction of both beta-amyloid and proinflammatory cytokines.

Mismatch between Bioluminescence Imaging (BLI) and MRI When Evaluating Glioblastoma Growth: Lessons from a Study Where BLI Suggested "Regression" while MRI Showed "Progression"

Orthotopic glioblastoma xenografts are paramount for evaluating the effect of innovative anti-cancer treatments. In longitudinal studies, tumor growth (or regression) of glioblastoma can only be monitored by noninvasive imaging. For this purpose, bioluminescence imaging (BLI) has gained popularity because of its low cost and easy access. In the context of the development of new nanomedicines for treating glioblastoma, we were using luciferase-expressing GL261 cell lines. Incidentally, using BLI in a specific GL261 glioblastoma model with cells expressing both luciferase and the green fluorescent protein (GL261-luc-GFP), we observed an apparent spontaneous regression. By contrast, the magnetic resonance imaging (MRI) analysis revealed that the tumors were actually growing over time. For other models (GL261 expressing only luciferase and U87 expressing both luciferase and GFP), data from BLI and MRI correlated well. We found that the divergence in results coming from different imaging modalities was not due to the tumor localization nor the penetration depth of light but was rather linked to the instability in luciferase expression in the viral construct used for the GL261-luc-GFP model. In conclusion, the use of multi-modality imaging prevents possible errors in tumor growth evaluation, and checking the stability of luciferase expression is mandatory when using BLI as the sole imaging modality.

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.

Why Don't the Mutant Cells That Evade DNA Repair Cause Cancer More Frequently? Importance of the Innate Immune System in the Tumor Microenvironment

The standard of care for most malignant solid tumors still involves tumor resection followed by chemo- and radiation therapy, hoping to eliminate the residual tumor cells. This strategy has been successful in extending the life of many cancer patients. Still, for primary glioblastoma (GBM), it has not controlled recurrence or increased the life expectancies of patients. Amid such disappointment, attempts to design therapies using the cells in the tumor microenvironment (TME) have gained ground. Such "immunotherapies" have so far overwhelmingly used genetic modifications of Tc cells (Car-T cell therapy) or blocking of proteins (PD-1 or PD-L1) that inhibit Tc-cell-mediated cancer cell elimination. Despite such advances, GBM has remained a "Kiss of Death" for most patients. Although the use of innate immune cells, such as the microglia, macrophages, and natural killer (NK) cells, has been considered in designing therapies for cancers, such attempts have not reached the clinic yet. We have reported a series of preclinical studies highlighting strategies to "re-educate" GBM-associated microglia and macrophages (TAMs) so that they assume a tumoricidal status. Such cells then secrete chemokines to recruit activated, GBM-eliminating NK cells and cause the rescue of 50-60% GBM mice in a syngeneic model of GBM. This review discusses a more fundamental question that most biochemists harbor: "since we are generating mutant cells in our body all the time, why don't we get cancer more often?" The review visits publications addressing this question and discusses some published strategies for re-educating the TAMs to take on the "sentry" role they initially maintained in the absence of cancer.

Identification of TSGA10 and GGNBP2 splicing variants in 5' untranslated region with distinct expression profiles in brain tumor samples

Introduction

Brain tumors (BTs) are perceived as one of the most common malignancies among children. The specific regulation of each gene can play a critical role in cancer progression. The present study aimed to determine the transcripts of the TSGA10 and GGNBP2 genes, considering the alternative 5'UTR region, and investigating the expression of these different transcripts in BTs.

Material and methods

Public data on brain tumor microarray datasets in GEO were analyzed with R software to evaluate the expression levels of TSGA10 and GGNBP2 genes (the Pheatmap package in R was also used to plot DEGs in a heat map). In addition, to validate our in-silico data analysis, RT-PCR was performed to determine the splicing variants of TSGA10 and GGNBP2 genes in testis and brain tumor samples. The expression levels of splice variants of these genes were analyzed in 30 brain tumor samples and two testicular tissue samples as a positive control.

Results

In silico results show that the differential expression levels of TSGA10 and GGNBP2 were significant in the GEO datasets of BTs compared to normal samples (with adjusted p-value<0.05 and log fold change > 1). This study's experimental results showed that the TSGA10 gene produces four different transcripts with two distinct promoter regions and splicing exon 4. The relative mRNA expression of transcripts without exon 4 was higher than transcripts with exon 4 in BT samples (p-value<001). In GGNBP2, exon 2 in the 5'UTR region and exon 6 in the coding sequence were spliced. The expression analysis results showed that the relative mRNA expression of transcript variants without exon 2 was higher than other transcript variants with exon 2 in BT samples (p-value<001).

Conclusion

The decreased expression levels of transcripts with longer 5'UTR in BT samples than in testicular or low-grade brain tumor samples may decrease their translation efficiency. Therefore, decreased amounts of TSGA10 and GGNBP2 as potential tumor suppressor proteins, especially in high-grade brain tumors, may cause cancer development by angiogenesis and metastasis.

Cell-Membrane-Coated Nanoparticles for Targeted Drug Delivery to the Brain for the Treatment of Neurological Diseases

Neurological diseases (NDs) are a significant cause of disability and death in the global population. However, effective treatments still need to be improved for most NDs. In recent years, cell-membrane-coated nanoparticles (CMCNPs) as drug-targeting delivery systems have become a research hotspot. Such a membrane-derived, nano drug-delivery system not only contributes to avoiding immune clearance but also endows nanoparticles (NPs) with various cellular and functional mimicries. This review article first provides an overview of the function and mechanism of single/hybrid cell-membrane-derived NPs. Then, we highlight the application and safety of CMCNPs in NDs. Finally, we discuss the challenges and opportunities in the field.

Investigating the Impact of Two Major Programming Environments on the Accuracy of Deep Learning-Based Glioma Detection from MRI Images

Brain tumors have been the subject of research for many years. Brain tumors are typically classified into two main groups: benign and malignant tumors. The most common tumor type among malignant brain tumors is known as glioma. In the diagnosis of glioma, different imaging technologies could be used. Among these techniques, MRI is the most preferred imaging technology due to its high-resolution image data. However, the detection of gliomas from a huge set of MRI data could be challenging for the practitioners. In order to solve this concern, many Deep Learning (DL) models based on Convolutional Neural Networks (CNNs) have been proposed to be used in detecting glioma. However, understanding which CNN architecture would work efficiently under various conditions including development environment or programming aspects as well as performance analysis has not been studied so far. In this research work, therefore, the purpose is to investigate the impact of two major programming environments (namely, MATLAB and Python) on the accuracy of CNN-based glioma detection from Magnetic Resonance Imaging (MRI) images. To this end, experiments on the Brain Tumor Segmentation (BraTS) dataset (2016 and 2017) consisting of multiparametric magnetic MRI images are performed by implementing two popular CNN architectures, the three-dimensional (3D) U-Net and the V-Net in the programming environments. From the results, it is concluded that the use of Python with Google Colaboratory (Colab) might be highly useful in the implementation of CNN-based models for glioma detection. Moreover, the 3D U-Net model is found to perform better, attaining a high accuracy on the dataset. The authors believe that the results achieved from this study would provide useful information to the research community in their appropriate implementation of DL approaches for brain tumor detection.

Olea europaea Leaf Phenolics Oleuropein, Hydroxytyrosol, Tyrosol, and Rutin Induce Apoptosis and Additionally Affect Temozolomide against Glioblastoma: In Particular, Oleuropein Inhibits Spheroid Growth by Attenuating Stem-like Cell Phenotype

The effects of Olea europaea leaf extract (OLE) phenolics, including oleuropein (OL), hydroxytyrosol (HT), tyrosol (TYR), and rutin against glioblastoma (GB), independently and in combination with temozolomide (TMZ), were investigated in T98G and A172 cells. Cell growth was assessed by WST-1, real-time cell analysis, colony formation, and cell cycle distribution assays. A dual acridine orange propidium iodide (AO/PI) staining and annexin V assay determined cell viability. A sphere-forming assay, an intracellular oxidative stress assay, and the RNA expression of CD133 and OCT4 investigated the GB stem-like cell (GSC) phenotype. A scratch wound-healing assay evaluated migration capacity. OL was as effective as OLE in terms of apoptosis promotion (p < 0.001) and GSC inhibition (p < 0.001). HT inhibited cell viability, GSC phenotype, and migration rate (p < 0.001), but its anti-GB effect was less than the total effect of OLE alone. Rutin decreased reactive oxygen species production and inhibited colony formation and cell migration (p < 0.001). TYR demonstrated the least effect. The additive effects of OL, HT, TYR and rutin with TMZ were significant (p < 0.001). Our data suggest that OL may represent a novel therapeutic approach against GB cells, while HT and rutin show promise in increasing the efficacy of TMZ therapy.

Navigating disability insurance in the American healthcare system for the low-grade glioma patient

In the United States, diagnosis of grade 3 or 4 glioma qualifies patients for Social Security disability benefits. Low-grade gliomas (LGGs) can be similarly debilitating, with at least 31% of patients presenting with cognitive deficits and 80% with tumor-related epilepsy. A diagnosis of LGG does not in and of itself qualify patients for disability benefits; the burden of proof is substantially higher. We outline the American healthcare system process of medical documentation to support disability benefits, Social Security Disability Insurance (SSDI) and Supplemental Security Income (SSI). We provide a template to assist providers in facilitating the application process for patients with LGG. The provider's role is not to simply "declare" a patient disabled, but to provide comprehensive documentation regarding diagnosis, treatment, disease status, symptoms, and functional status in the medical record. As cognitive symptoms and seizures are 2 key sources of disability in LGG patients, selective referrals to neuropsychology and epileptology may improve patient care and bolster documentation of the patient's symptoms in these domains. Likewise, connecting patients with social workers and disability claims representatives can assist them in navigating the complicated application process. We provide an extensive review for patient eligibility in the United States to receive disability. We map a comprehensive care process that may have relevance to multiple regions outside the United States. Providers are better able to help their patients navigate the disability application process when they understand how to address physical and cognitive changes for thorough care of their patient.