Impact of intensity-modulated proton therapy in reducing radiation-induced lymphopenia in glioma patients.

Background: Current standard management in adult grades 2-4 gliomas includes maximal safe resection followed by adjuvant radiotherapy (RT) and chemotherapy. Radiation-induced lymphopenia (RIL) has been shown to possibly affect treatment outcomes adversely. Proton beam therapy (PBT) may reduce the volume of the normal brain receiving moderate radiation doses, and consequently RIL. Our aim was to evaluate the incidence and severity of RIL during proton beam therapy (PBT). Methods: We identified patients with grades 2-4 glioma treated with PBT at our center between January 2019 and December 2021. We evaluated the incidence and severity of RIL from weekly complete blood count (CBC) data collected during PBT and compared it to the patients who were treated with photon-based RT (XRT) at our center during the same time. Results: The incidence of any degree of lymphopenia (48% in PBT, vs. 81.2% in XRT, P value = .001) and severe lymphopenia (8% in PBT, vs. 24.6% in XRT, P value = .093) were both significantly lesser in patients who received PBT. Severe RIL in patients receiving PBT was seen in only CNS WHO Gr-4 tumors. Mean whole brain V20GyE and V25GyE inversely correlated to nadir ALC and were both significantly lower with PBT. Patients with lymphopenia during PBT showed a trend toward poorer progression-free survival (P = .053) compared to those with maintained lymphocyte counts. Conclusions: Proton therapy seems to have a superior sparing of normal brain to moderate dose radiation than photon-based RT and reduces the incidence of lymphopenia. Glioma patients with lymphopenia possibly have worse outcomes than the ones with maintained lymphocyte counts.

Thalamic H3K27M altered diffuse midline gliomas: Clinicopathological and outcome analysis.

INTRODUCTION: Diffuse midline glioma (DMG) is a relatively new entity which was introduced in the fourth edition of the WHO classification of CNS tumours in 2016 and later underwent revision in 2021. It is an infiltrative glioma arising from midline structures, viz., thalamus, spine, and brainstem. Current literature on DMG is based majorly on brainstem lesions, and DMGs arising elsewhere remain unexplored. In our study, we have discussed our experience with thalamic DMGs. METHODOLOGY: This is a retrospective observational study of all patients with histopathologically proven DMG H3K27M altered, arising in the thalamus from 2018 to 2022. Clinical, neuroimaging, and pathology were re-reviewed, and prognostic factors for 3 months, 6 months, and overall survival (OS) were analyzed for all patients. RESULTS: There were 89 patients- 64 adults and 25 pediatric patients with thalamic DMG. The median age at presentation was 24 years. Raised ICP followed by limb weakness were the most common presenting complaints. Stereotactic biopsy was performed in 64 (71.9 %) patients and surgical decompression in 25 (28.1 %) patients. CSF diversion was required in 53 (59.6 %) patients. Median survival was 8 months in adults and 7 months in pediatric (p-value: 0.51). Raised ICP and TP53 mutation were prognostic factors in pediatric population. Radiotherapy with or without chemotherapy improved survival (p-value- <0.01). CONCLUSION: Thalamic DMGs have a poor prognosis which is comparable to brainstem DMGs. Radiotherapy improves survival in these patients. However, the disease remains an enigma and further work delving into its molecular characterization should be encouraged.

The role of lactate-induced protein lactylation in gliomas: implications for preclinical research and the development of new treatments.

The most prevalent primary brain tumors in adults are gliomas. In addition to insufficient therapeutic alternatives, gliomas are fatal mostly due to the rapid proliferation and continuous infiltration of tumor cells into the surrounding healthy brain tissue. According to a growing body of research, aerobic glycolysis, or the Warburg effect, promotes glioma development because gliomas are heterogeneous cancers that undergo metabolic reprogramming. Therefore, addressing the Warburg effect might be a useful therapeutic strategy for treating cancer. Lactate plays a critical role in reprogramming energy metabolism, allowing cells to rapidly access large amounts of energy. Lactate, a byproduct of glycolysis, is therefore present in rapidly proliferating cells and tumors. In addition to the protumorigenesis pathways of lactate synthesis, circulation, and consumption, lactate-induced lactylation has been identified in recent investigations. Lactate plays crucial roles in modulating immune processes, maintaining homeostasis, and promoting metabolic reprogramming in tumors, which are processes regulated by the lactate-induced lactylation of the lysine residues of histones. In this paper, we discuss the discovery and effects of lactylation, review the published studies on how protein lactylation influences cancer growth and further explore novel treatment approaches to achieve improved antitumor effects by targeting lactylation. These findings could lead to a new approach and guidance for improving the prognosis of patients with gliomas.

Single cell spatial biology over developmental time can decipher pediatric brain pathologies.

Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.

Global post­marketing safety surveillance of Tumor Treating Fields (TTFields) therapy in over 25,000 patients with CNS malignancies treated between 2011-2022.

BACKGROUND: Tumor Treating Fields (TTFields) are alternating electric fields that disrupt cancer cell processes. TTFields therapy is approved for recurrent glioblastoma (rGBM), and newly-diagnosed (nd) GBM (with concomitant temozolomide for ndGBM; US), and for grade IV glioma (EU). We present an updated global, post-marketing surveillance safety analysis of patients with CNS malignancies treated with TTFields therapy. METHODS: Safety data were collected from routine post-marketing activities for patients in North America, Europe, Israel, and Japan (October 2011-October 2022). Adverse events (AEs) were stratified by age, sex, and diagnosis. RESULTS: Overall, 25,898 patients were included (diagnoses: ndGBM [68%], rGBM [26%], anaplastic astrocytoma/oligodendroglioma [4%], other CNS malignancies [2%]). Median (range) age was 59 (3-103) years; 66% patients were male. Most (69%) patients were 18-65 years; 0.4% were < 18 years; 30% were > 65 years. All-cause and TTFields-related AEs occurred in 18,798 (73%) and 14,599 (56%) patients, respectively. Most common treatment-related AEs were beneath-array skin reactions (43%), electric sensation (tingling; 14%), and heat sensation (warmth; 12%). Treatment-related skin reactions were comparable in pediatric (39%), adult (42%), and elderly (45%) groups, and in males (41%) and females (46%); and similar across diagnostic subgroups (ndGBM, 46%; rGBM, 34%; anaplastic astrocytoma/oligodendroglioma, 42%; other, 40%). No TTFields-related systemic AEs were reported. CONCLUSIONS: This long-term, real-world analysis of > 25,000 patients demonstrated good tolerability of TTFields in patients with CNS malignancies. Most therapy-related AEs were manageable localized, non-serious skin events. The TTFields therapy safety profile remained consistent across subgroups (age, sex, and diagnosis), indicative of its broad applicability.

Surgery for pediatric low-grade gliomas within the vermis.

Pediatric low-grade gliomas (pLGGs) in the cerebellar vermis present unique challenges due to their intricate anatomical location and potential impact on critical neurological functions. Surgical intervention remains a cornerstone in the management of these tumors, aiming to achieve maximal tumor resection while preserving neurological function. In this review, the authors will discuss anatomical consideration and will explore current surgical techniques and strategies employed in the treatment of cerebellar vermis pLGGs such as the midline and lateral suboccipital approaches, as well as endoscopic-assisted technique. Additionally, we will emphasize the importance of intraoperative neurophysiological monitoring (IONM) in ensuring safe and effective tumor resection. Overall, this review provides insights into the neurosurgical approach of pLGGs in the cerebellar vermis.

Controversies in neuro-oncology: Focal proton versus photon radiation therapy for adult brain tumors.

Radiation therapy (RT) plays a fundamental role in the treatment of malignant and benign brain tumors. Current state-of-the-art photon- and proton-based RT combines more conformal dose distribution of target volumes and accurate dose delivery while limiting the adverse radiation effects. PubMed was systematically searched from from 2000 to October 2023 to identify studies reporting outcomes related to treatment of central nervous system (CNS)/skull base tumors with PT in adults. Several studies have demonstrated that proton therapy (PT) provides a reduced dose to healthy brain parenchyma compared with photon-based (xRT) radiation techniques. However, whether dosimetric advantages translate into superior clinical outcomes for different adult brain tumors remains an open question. This review aims at critically reviewing the recent studies on PT in adult patients with brain tumors, including glioma, meningiomas, and chordomas, to explore its potential benefits compared with xRT.

2-Methoxyestradiol, an Endogenous 17ß-Estradiol Metabolite, Induces Antimitogenic and Apoptotic Actions in Oligodendroglial Precursor Cells and Triggers Endoreduplication via the p53 Pathway.

The abnormal growth of oligodendrocyte precursor cells (OPCs) significantly contributes to the progression of glioblastoma tumors. Hence, molecules that block OPC growth may be of therapeutic importance in treating gliomas. 2-Methoxyestradiol (2ME), an endogenous tubulin-interacting metabolite of estradiol, is effective against multiple proliferative disorders. Based on its anti-carcinogenic and anti-angiogenic actions, it is undergoing phase II clinical trials. We hypothesize that 2ME may prevent glioma growth by targeting OPC growth. Here, we tested this hypothesis by assessing the impact of 2ME on the growth of an OPC line, "Oli-neu", and dissected the underlying mechanism(s). Treatment with 2ME inhibited OPC growth in a concentration-dependent manner, accompanied by significant upregulation in the expression of p21 and p27, which are negative cell-cycle regulators. Moreover, treatment with 2ME altered OPC morphology from multi-arm processes to rounded cells. At concentrations of 1uM and greater, 2ME induced apoptosis, with increased expressions of caspase 3, PARP, and caspase-7 fragments, externalized phosphatidylserine staining/APOPercentage, and increased mitochondrial activity. Flow cytometry and microscopic analysis demonstrated that 2ME triggers endoreduplication in a concentration-dependent fashion. Importantly, 2ME induced cyclin E, JNK1/2, and p53 expression, as well as OPC fusion, which are key mechanisms driving endoreduplication and whole-genome duplication. Importantly, the inhibition of p53 with pifithrin-α rescued 2ME-induced endoreduplication. The pro-apoptotic and endoreduplication actions of 2ME were accompanied by the upregulation of survivin, cyclin A, Cyclin B, Cyclin D2, and ppRB. Similar growth inhibitory, apoptotic, and endoreduplication effects of 2ME were observed in CG4 cells. Taken together, our findings provide evidence that 2ME not only inhibits OPC growth and triggers apoptosis, but also activates OPCs into survival (fight or flight) mode, leading to endoreduplication. This inherent survival characteristic of OPCs may, in part, be responsible for drug resistance in gliomas, as observed for many tubulin-interacting drugs. Importantly, the fate of OPCs after 2ME treatment may depend on the cell-cycle status of individual cells. Combining tubulin-interfering molecules with drugs such as pifithrin-α that inhibit endoreduplication may help inhibit OPC/glioma growth and limit drug resistance.

Oligodendroglioma: a neurological perspective in Sub-saharan Africa.

Gliomas are a kind of brain cancer that develops from glial cells. Glial cells provide nourishment and energy to nerve cells, and they also preserve the blood-brain barrier. A primary cancer of the central nervous system (CNS) is oligodendroglioma. This suggests that it originates in the brain or spinal cord. While oligodendrogliomas can strike anyone at any age, the age range of 35 to 44 is when they most commonly occur. Oligodendrogliomas are rare in young people and more common in men than women. Based on anecdotal data, patients with oligodendroglioma may present management challenges in Africa. There are delays in diagnosis and referrals due to the scarcity of neuroimaging facilities. A wide range of strategies have been put forth to improve pathology services in low- and middle-income nations. Adequate mentorship, short-term visitor programs, overcoming supply chain constraints, establishing training standards, and establishing the role of pathologists in cancer screening and early diagnosis have all been proposed as solutions to this problem. To sum up, oligodendroglioma is one of the low-grade gliomas this study looked at. Brain cancer is a serious public health concern in Africa. Improved options for screening and therapy are required to better address this problem.

Microsurgical anatomy and approaches to thalamic gliomas. Part 2: Maximal safe resection of thalamic gliomas improves outcomes. A single-center experience.

OBJECTIVE: As presented in Part 1 of this series, thalamic gliomas (TGs) are deep-seated, difficult-to-access tumors surrounded by vital neurovascular structures. Given their high operative morbidity, TGs have historically been considered inoperable lesions. Although maximal safe resection (MSR) has become the treatment standard for lobar and even deep-seated mediobasal temporal and insular gliomas, the eloquent location of TGs has precluded this management strategy, with biopsy and adjuvant treatment being the mainstay. The authors hypothesized that MSR can be achieved with low morbidity and mortality for TGs, thus resulting in improved outcomes. METHODS: A retrospective single-center study was performed on all TG patients from 2006 to 2020. Clinical, imaging, and pathology reports were obtained. Univariate and multivariate analyses were performed to determine prognostic variables. Case examples illustrate various approaches and the rationale for staging resections of more complex TGs. RESULTS: A total of 42 patients (26 males, 16 females), among them 12 pediatric (29%) cases, were included. Their mean age was 36.0 ± 21.4 (median 30, range 3-73) years. The median maximal tumor diameter was 45 (range 19-70) mm. Eighteen patients (43%) had a prior stereotactic needle tumor biopsy, with the ultimate diagnosis changed for 7 patients (39%) following microsurgical resection. The most common surgical approaches were transtemporal (29%), anterior interhemispheric transcallosal (29%), and superior parietal lobule (25%). Overall, the combined subtotal and gross-total resection rate was 95% (n = 40). Low-grade gliomas (LGGs; grades I and II) comprised one-third of the group, whereas half of the patients had glioblastoma multiforme. There were no operative mortalities. Although temporary postoperative motor deficits were observed in 12 patients (28.6%), all improved during the early postoperative period except 1 (2.4%), who had mild residual hemiparesis. Two patients required CSF diversion for hydrocephalus. The 2-year overall survival rate was 90% for LGG patients and 15% for high-grade glioma (HGG) patients. Multivariate analysis revealed that histological grade, age, and extent of resection were independent prognostic factors associated with survival. CONCLUSIONS: Management of TGs is challenging, with resection avoided by many, if not most, neurosurgeons, especially for HGGs. The results reported here demonstrate improved outcomes with resection, particularly in younger LGG patients. The authors therefore advocate for MSR for a select cohort of TG patients using carefully planned surgical approaches, contemporary intraoperative adjuncts, and meticulous microsurgical techniques.

Microsurgical anatomy and approaches to thalamic gliomas. Part 1: A cartography guide for navigating to the thalamus. Integrating 3D model rendering with anatomical dissections.

OBJECTIVE: The selection of appropriate microsurgical approaches to treat thalamic pathologies is currently largely subjective. The objective of this study was to provide a structured cartography map for surgical navigation to treat gliomas involving different surfaces of the thalamus. METHODS: Fifteen formalin-fixed, silicone-injected cadavers (30 sides) were dissected, and 10 adult brain specimens (20 sides) were used to illustrate thalamic microsurgical anatomy using the Klingler fiber dissection technique. Exposures and trajectories for the six most common microsurgical approaches were depicted using MR data from healthy subjects converted into surface-rendered 3D virtual brain models. Additionally, thalamic surfaces exposed with all six approaches were color mapped on the virtual 3D model and compared side-by-side in 360° views with previously reported microsurgical approaches. These 3D models were then used in conjunction with topographic data to guide cadaveric dissection steps. RESULTS: There are two general surgical routes to thalamic lesions: the subarachnoid transcisternal and transcortical routes. The transcisternal route consists of the following three approaches: 1) anterior interhemispheric transcallosal approach, which exposes the anterior and superior thalamus; 2) posterior interhemispheric transcallosal approach, which exposes the posterosuperior thalamus; and 3) supracerebellar infratentorial approach, which exposes the posteromedial cisternal thalamus and can be extended laterally to approach the posterolateral thalamus by cutting the tentorium. The three transcortical approaches are the 1) superior parietal lobule approach, which exposes the posterosuperior thalamus and is particularly advantageous in the setting of hydrocephalus; 2) transtemporal gyrus approach, which exposes the inferolateral thalamus; and 3) transsylvian transinsular approach, which exposes the lateral thalamus (slightly more superiorly and posteriorly) and is advantageous for pathologies extending laterally into the peduncle, lenticular nucleus, or insula. CONCLUSIONS: Microsurgical approaches to thalamic gliomas continue to be challenging. Nonetheless, safe and effective cisternal, ventricular, and cortical corridors can be developed with thoughtful planning, anatomical understanding, and knowledge of the advantages, risks, and limitations of each approach. In some cases, it is wise to combine these approaches with staged procedures, as the authors demonstrate in Part 2. In Part 1 of this two-part series, they discuss thalamic microsurgical anatomy and illustrate the trajectory and exposures of all six approaches to guide decision-making. Part 2 discusses their thalamic glioma microsurgical case series, which utilizes these microsurgical approaches.

Noninvasive prediction of CCL2 expression level in high-grade glioma patients.

BACKGROUND: Gliomas are recognized as the most frequent type of malignancies in the central nervous system, and efficacious prognostic indicators are essential to treat patients with gliomas and improve their clinical outcomes. The chemokine (C-C motif) ligand 2 (CCL2) is a promising predictor for glioma malignancy and progression. However, at present, the methods to evaluate CCL2 expression level are invasive and operator-dependent. OBJECTIVE: It was expected to noninvasively predict CCL2 expression levels in malignant glioma tissues by magnetic resonance imaging (MRI)-based radiomics and assess the association between the developed radiomics model and prognostic indicators and related genes. METHODS: MRI-based radiomics was used to predict CCL2 expression level using data obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) databases. A support vector machine (SVM)-based radiomics model and a logistic regression (LR)-based radiomics model were used to predict the radiomics score, and its correlation with CCL2 expression level was analyzed. RESULTS: The results revealed that there was an association between CCL2 expression level and the overall survival of cases with gliomas, and bioinformatics correlation analysis showed that CCL2 expression level was highly correlated with disease-related pathways, such as mTOR signaling pathway, cGMP-PKG signaling pathway, and MAPK signaling pathway. Both SVM- and LR-based radiomics data robustly predicted CCL2 expression level, and radiomics scores could also be used to predict the overall survival of patients. Moreover, the high/low radiomics scores were highly correlated with the known glioma-related genes, including CD70, CD27, and PDCD1. CONCLUSION: An MRI-based radiomics model was successfully developed, and its clinical benefits were confirmed, including the prediction of CCL2 expression level and patients' prognosis.

MGMT promoter methylation is a strong prognostic factor for survival after progression in high-grade gliomas.

BACKGROUND: High-grade gliomas (HGGs) have a rapid relapse and short survival. Studies have identified many clinical characteristics and biomarkers associated with progression-free survival (PFS) and over-survival (OS). However, there has not yet a comprehensive study on survival after the first progression (SAP). METHODS: From CGGA and TCGA, 319 and 308 HGGs were confirmed as the first progression. The data on clinical characteristics and biomarkers were analyzed in accordance with OS, PFS, and SAP. RESULTS: Analysis of 319 patients from CGGA, significant predictors of improved OS/PFS/SAP were WHO grade, MGMT promoter methylation, and Ki-67 expression in univariate analysis. Further multivariate analysis showed MGMT promoter methylation and Ki-67 expression were independent predictors. However, an analysis of 308 patients from TCGA found MGMT promoter methylation is the only prognostic marker. A longer SAP was observed in patients with methylated MGMT promoter after standard chemoradiotherapy. In our data, HGGs could be divided into low, intermediate, and high-risk groups for SAP by MGMT methylation and Ki-67 expression. CONCLUSIONS: Patients with MGMT promoter methylation have a prolonger SAP after standard chemoradiotherapy. HGGs could be divided into low, intermediate, and high-risk groups for SAP according to MGMT status and Ki-67 expression.

Trans-temporal trans-choroidal resection of thalamic and thalamopeduncular tumors: how I do it.

BACKGROUND:  Surgical resection is the cornerstone of treatment for low-grade tumors, albeit total excision is beneficial. As the thalamus is surrounded by vital neurovascular system, lesions here present a surgical challenge. METHOD: This article aims to demonstrate the trans-temporal, trans-choroidal fissure approach's effective surgical therapy on patients with thalamic lesions. With this approach, we were able to remove the tumor completely in three patients and almost completely in six more. Here we discuss a few technical details and potential hazards of the procedure with an operative video. CONCLUSION: This approach  provides excellent access to the deep areas of brain.

Enhanced Multimodal Brain Tumor Classification in MR Images using 2D ResNet as backbone with Explicit Tumor Size Information.

It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can determine the degree of malignancy to formulate the best treatment plan, which can eliminate the tumor or limit widespread metastasis of the tumor, saving the patient's life and improving their prognosis. To more accurately predict the grading of gliomas, we proposed a novel method of combining the advantages of 2D and 3D Convolutional Neural Networks for tumor grading by multimodality on Magnetic Resonance Imaging. The core of the innovation lies in our combination of tumor 3D information extracted from multimodal data with those obtained from a 2D ResNet50 architecture. It solves both the lack of temporal-spatial information provided by 3D imaging in 2D convolutional neural networks and avoids more noise from too much information in 3D convolutional neural networks, which causes serious overfitting problems. Incorporating explicit tumor 3D information, such as tumor volume and surface area, enhances the grading model's performance and addresses the limitations of both approaches. By fusing information from multiple modalities, the model achieves a more precise and accurate characterization of tumors. The model I s trained and evaluated using two publicly available brain glioma datasets, achieving an AUC of 0.9684 on the validation set. The model's interpretability is enhanced through heatmaps, which highlight the tumor region. The proposed method holds promise for clinical application in tumor grading and contributes to the field of medical diagnostics for prediction.

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas.

AIM: This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas. METHODS: Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. In vitro and in vivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated. RESULTS: Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance. CONCLUSION: This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As an alternative, computer-assisted methods, particularly deep convolutional neural networks (DCNNs), have gained traction. This research paper explores the recent advancements in DCNNs for glioma grading using brain magnetic resonance images (MRIs) from 2015 to 2023. The study evaluated various DCNN architectures and their performance, revealing remarkable results with models such as hybrid and ensemble based DCNNs achieving accuracy levels of up to 98.91%. However, challenges persisted in the form of limited datasets, lack of external validation, and variations in grading formulations across diverse literature sources. Addressing these challenges through expanding datasets, conducting external validation, and standardizing grading formulations can enhance the performance and reliability of DCNNs in glioma grading, thereby advancing brain tumor classification and extending its applications to other neurological disorders.