Amide proton transfer weighted and diffusion weighted imaging based radiomics classification algorithm for predicting 1p/19q co-deletion status in low grade gliomas.

BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

[Analysis of the safety, accuracy, and factors influencing bleeding complications in CT-guided puncture biopsy of brain occupying lesions].

Objective: To investigate the safety and accuracy of CT-guided intracranial puncture biopsy and the possible influencing factors of postoperative bleeding complications. Methods: A case series study. A retrospective analysis was conducted on 101 patients who underwent CT-guided intracranial puncture biopsy at the First Affiliated Hospital of Zhengzhou University from January 2017 to December 2021. The basic data of patients and the safety and accuracy of CT-guided intracranial puncture biopsy were analyzed statistically. Univariate and multivariate logistic regression analysis were used to screen the influencing factors of bleeding complications in CT-guided intracranial puncture biopsy, and the bleeding complications in glioma subgroup were analyzed. Results: Among the 101 patients, 53 were males and 48 were females, aged (53.7±17.2) years. The average diameter of intracranial lesions was (3.5±1.4) cm, while the vertical distance from the lesion to the meninges was (2.4±1.7) cm. The needle's intracranial depth reached (3.2±1.8) cm, with adjustments averaging (3±1) occurrences and an average procedural duration of (40.2±12.9) minutes. Pathological diagnoses included glioma (36 cases), gliosis (3 cases), lymphoma (32 cases), metastatic tumors (7 cases), inflammatory lesions (13 cases), and 10 indeterminate cases. The positive rate of puncture pathology was 90.1% (91/101), and the diagnostic coincidence rate was 94.0% (78/83). The incidence of bleeding complications in CT-guided intracranial puncture biopsy was 26.7% (27/101), of which 23 cases had small intratoma or needle path bleeding, 4 cases had massive bleeding, and 2 cases died. The patients were divided into bleeding group (n=27) and no bleeding group (n=74), according to the presence or absence of bleeding. The results of univariate logistic regression analysis showed that thrombin time≥15 s and the number of needle adjustment were the factors affecting the occurrence of bleeding complications (both P<0.05), and the results of multivariate logistic regression showed that thrombin time≥15 s was the related factor for bleeding. Patients with thrombin time≥15 s had a 3.045 times higher risk of bleeding than those with thrombin time<15 s (OR=3.045,95%CI:1.189-7.799,P=0.020). Among the 101 patients, 36 cases of midbrain glioma were divided into low-grade glioma group (n=11) and high-grade glioma group (n=25) according to the pathological grade. Subgroup analysis showed that the risk of bleeding for high-grade gliomas was 9.231 times higher than that for low-grade gliomas (OR=9.231,95%CI:1.023-83.331,P=0.031). Conclusions: CT-guided intracranial puncture biopsy is safe and feasible with high accuracy. Complication rates are associated with thrombin time≥15 s, especially high-grade glioma, which increases the risk of postoperative bleeding.

Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.

The use of various kinds of magnetic resonance imaging (MRI) techniques for examining brain tissue has increased significantly in recent years, and manual investigation of each of the resulting images can be a time-consuming task. This paper presents an automatic brain-tumor diagnosis system that uses a CNN for detection, classification, and segmentation of glioblastomas; the latter stage seeks to segment tumors inside glioma MRI images. The structure of the developed multi-unit system consists of two stages. The first stage is responsible for tumor detection and classification by categorizing brain MRI images into normal, high-grade glioma (glioblastoma), and low-grade glioma. The uniqueness of the proposed network lies in its use of different levels of features, including local and global paths. The second stage is responsible for tumor segmentation, and skip connections and residual units are used during this step. Using 1800 images extracted from the BraTS 2017 dataset, the detection and classification stage was found to achieve a maximum accuracy of 99%. The segmentation stage was then evaluated using the Dice score, specificity, and sensitivity. The results showed that the suggested deep-learning-based system ranks highest among a variety of different strategies reported in the literature.

Multi-omics technologies and molecular biomarkers in brain tumor-related epilepsy.

BACKGROUND: Brain tumors are one of the leading causes of epilepsy, and brain tumor-related epilepsy (BTRE) is recognized as the major cause of intractable epilepsy, resulting in huge treatment cost and burden to patients, their families, and society. Although optimal treatment regimens are available, the majority of patients with BTRE show poor resolution of symptoms. BTRE has a very complex and multifactorial etiology, which includes several influencing factors such as genetic and molecular biomarkers. Advances in multi-omics technologies have enabled to elucidate the pathophysiological mechanisms and related biomarkers of BTRE. Here, we reviewed multi-omics technology-based research studies on BTRE published in the last few decades and discussed the present status, development, opportunities, challenges, and prospects in treating BTRE. METHODS: First, we provided a general review of epilepsy, BTRE, and multi-omics techniques. Next, we described the specific multi-omics (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics) techniques and related molecular biomarkers for BTRE. We then presented the associated pathogenetic mechanisms of BTRE. Finally, we discussed the development and application of novel omics techniques for diagnosing and treating BTRE. RESULTS: Genomics studies have shown that the BRAF gene plays a role in BTRE development. Furthermore, the BRAF V600E variant was found to induce epileptogenesis in the neuronal cell lineage and tumorigenesis in the glial cell lineage. Several genomics studies have linked IDH variants with glioma-related epilepsy, and the overproduction of D2HG is considered to play a role in neuronal excitation that leads to seizure occurrence. The high expression level of Forkhead Box O4 (FOXO4) was associated with a reduced risk of epilepsy occurrence. In transcriptomics studies, VLGR1 was noted as a biomarker of epileptic onset in patients. Several miRNAs such as miR-128 and miRNA-196b participate in BTRE development. miR-128 might be negatively associated with the possibility of tumor-related epilepsy development. The lncRNA UBE2R2-AS1 inhibits the growth and invasion of glioma cells and promotes apoptosis. Quantitative proteomics has been used to determine dynamic changes of protein acetylation in epileptic and non-epileptic gliomas. In another proteomics study, a high expression of AQP-4 was detected in the brain of GBM patients with seizures. By using quantitative RT-PCR and immunohistochemistry assay, a study revealed that patients with astrocytomas and oligoastrocytomas showed high BCL2A1 expression and poor seizure control. By performing immunohistochemistry, several studies have reported the relationship between D2HG overproduction and seizure occurrence. Ki-67 overexpression in WHO grade II gliomas was found to be associated with poor postoperative seizure control. According to metabolomics research, the PI3K/AKT/mTOR pathway is associated with the development of glioma-related epileptogenesis. Another metabolomics study found that SV2A, P-gb, and CAD65/67 have the potential to function as biomarkers for BTRE. CONCLUSIONS: Based on the synthesized information, this review provided new research perspectives and insights into the early diagnosis, etiological factors, and personalized treatment of BTRE.

PRMT6-mediated transcriptional activation of ythdf2 promotes glioblastoma migration, invasion, and emt via the wnt-ß-catenin pathway.

BACKGROUND: Protein arginine methyltransferase 6 (PRMT6) plays a crucial role in various pathophysiological processes and diseases. Glioblastoma (GBM; WHO Grade 4 glioma) is the most common and lethal primary brain tumor in adults, with a prognosis that is extremely poor, despite being less common than other systemic malignancies. Our current research finds PRMT6 upregulated in GBM, enhancing tumor malignancy. Yet, the specifics of PRMT6's regulatory processes and potential molecular mechanisms in GBM remain largely unexplored. METHODS: PRMT6's expression and prognostic significance in GBM were assessed using glioma public databases, immunohistochemistry (IHC), and immunoblotting. Scratch and Transwell assays examined GBM cell migration and invasion. Immunoblotting evaluated the expression of epithelial-mesenchymal transition (EMT) and Wnt-ß-catenin pathway-related proteins. Dual-luciferase reporter assays and ChIP-qPCR assessed the regulatory relationship between PRMT6 and YTHDF2. An in situ tumor model in nude mice evaluated in vivo conditions. RESULTS: Bioinformatics analysis indicates high expression of PRMT6 and YTHDF2 in GBM, correlating with poor prognosis. Functional experiments show PRMT6 and YTHDF2 promote GBM migration, invasion, and EMT. Mechanistic experiments reveal PRMT6 and CDK9 co-regulate YTHDF2 expression. YTHDF2 binds and promotes the degradation of negative regulators APC and GSK3ß mRNA of the Wnt-ß-catenin pathway, activating it and consequently enhancing GBM malignancy. CONCLUSIONS: Our results demonstrate the PRMT6-YTHDF2-Wnt-ß-Catenin axis promotes GBM migration, invasion, and EMT in vitro and in vivo, potentially serving as a therapeutic target for GBM.

BCOR::CREBBP fusion in malignant neuroepithelial tumor of CNS expands the spectrum of methylation class CNS tumor with BCOR/BCOR(L1)-fusion.

Methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" was recently defined based on methylation profiling and tSNE analysis of a series of 21 neuroepithelial tumors with predominant presence of a BCOR fusion and/or characteristic CNV breakpoints at chromosome 22q12.31 and chromosome Xp11.4. Clear diagnostic criteria are still missing for this tumor type, specially that BCOR/BCOR(L1)-fusion is not a consistent finding in these tumors despite being frequent and that none of the Heidelberger classifier versions is able to clearly identify these cases, in particular tumors with alternative fusions other than those involving BCOR, BCORL1, EP300 and CREBBP. In this study, we introduce a BCOR::CREBBP fusion in an adult patient with a right temporomediobasal tumor, for the first time in association with methylation class "CNS tumor with BCOR/BCOR(L1)-fusion" in addition to 35 cases of CNS neuroepithelial tumors with molecular and histopathological characteristics compatible with "CNS tumor with BCOR/BCOR(L1)-fusion" based on a comprehensive literature review and data mining in the repository of 23 published studies on neuroepithelial brain Tumors including 7207 samples of 6761 patients. Based on our index case and the 35 cases found in the literature, we suggest the archetypical histological and molecular features of "CNS tumor with BCOR/BCOR(L1)-fusion". We also present four adult diffuse glioma cases including GBM, IDH-Wildtype and Astrocytoma, IDH-Mutant with CREBBP fusions and describe the necessity of complementary molecular analysis in "CNS tumor with BCOR/BCOR(L1)-alterations for securing a final diagnosis.

has-miR-134-5p inhibits the proliferation and migration of glioma cells by regulating the BDNF/ERK signaling pathway.

Our research investigated the effects of hsa-miR-134-5p on glioma progression, focusing on its interaction with the BDNF/ERK signaling pathway. U251 and U87 cell lines were analyzed post-transfection with hsa-miR-134-5p mimics and inhibitors, confirming the miRNA's binding to BDNF using dual luciferase assays. Q-PCR was employed to measure expression changes, revealing that hsa-miR-134-5p markedly inhibited glioma cell proliferation, migration, and invasion, as evidenced by CCK8, monoclonal formation, and Transwell assays. Scratch tests and Western blotting demonstrated hsa-miR-134-5p's modulation of the BDNF/ERK pathway and associated decrease in MMP2/9 protein levels. Flow cytometry suggested that hsa-miR-134-5p might also block the G0/S phase transition. In vivo studies using nude mice corroborated the tumor-suppressing effects of hsa-miR-134-5p, which were negated by elevated BDNF levels. Comparative protein analysis across groups confirmed the pathway's significance in tumorigenesis. Our findings identify hsa-miR-134-5p as a key molecule impeding glioma cell growth by curtailing the BDNF/ERK pathway, with the reversal by BDNF upregulation pointing to the potential of therapeutically exploiting the hsa-miR-134-5p/BDNF axis in glioma care.

Prognostic significance of systemic inflammatory parameters in high-grade glial tumor patients: Two center experience.

We aimed to determine the prognostic values of the neutrophil-lymphocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index, body mass index, and prognostic nutritional index scores in patients with high-grade glioma. This was a retrospective observational case series. Between 2015 and 2020, 79 patients with high-grade gliomas 2 oncology centers were included in our study. All patients (n = 79) had high-grade glial tumors and were treated with RT. Sixty-nine (87.3%) patients died, and the median 2 years overall survival was 12.7 months. Recurrence was observed in 25 (31.6%) patients at the end of the treatment. The median recurrence free survival was 24.4 months. There was no significant correlation between systemic inflammation indicators and survival parameters for OS and RFS. Only a marginally significant association between the neutrophil-lymphocyte ratio and RFS was found. Systemic inflammatory parameters and outcomes were not significantly correlated in patients with high-grade gliomas.

A multi-omics analysis-based model to predict the prognosis of low-grade gliomas.

Lower-grade gliomas (LGGs) exhibit highly variable clinical behaviors, while classic histology characteristics cannot accurately reflect the authentic biological behaviors, clinical outcomes, and prognosis of LGGs. In this study, we carried out analyses of whole exome sequencing, RNA sequencing and DNA methylation in primary vs. recurrent LGG samples, and also combined the multi-omics data to construct a prognostic prediction model. TCGA-LGG dataset was searched for LGG samples. 523 samples were used for whole exome sequencing analysis, 532 for transcriptional analysis, and 529 for DNA methylation analysis. LASSO regression was used to screen genes with significant association with LGG survival from the frequently mutated genes, differentially expressed genes, and differentially methylated genes, whereby a prediction model for prognosis of LGG was further constructed and validated. The most frequently mutated diver genes in LGGs were IDH1 (77%), TP53 (48%), ATRX (37%), etc. Top significantly up-regulated genes were C6orf15, DAO, MEOX2, etc., and top significantly down-regulated genes were DMBX1, GPR50, HMX2, etc. 2077 genes were more and 299 were less methylated in recurrent vs. primary LGG samples. Thirty-nine genes from the above analysis were included to establish a prediction model of survival, which showed that the high-score group had a very significantly shorter survival than the low-score group in both training and testing sets. ROC analysis showed that AUC was 0.817 for the training set and 0.819 for the testing set. This study will be beneficial to accurately predict the survival of LGGs to identify patients with poor prognosis to take specific treatment as early, which will help improve the treatment outcomes and prognosis of LGG.

KIAA0040 enhances glioma growth by controlling the JAK2/STAT3 signalling pathway.

The role of KIAA0040 role in glioma development is not yet understood despite its connection to nervous system diseases. In this study, KIAA0040 expression levels were evaluated using qRT-PCR, WB and IHC, and functional assays were conducted to assess its impact on glioma progression, along with animal experiments. Moreover, WB was used to examine the impact of KIAA0040 on the JAK2/STAT3 signalling pathway. Our study found that KIAA0040 was increased in glioma and linked to tumour grade and poor clinical outcomes, serving as an independent prognostic factor. Functional assays showed that KIAA0040 enhances glioma growth, migration and invasion by activating the JAK2/STAT3 pathway. Of course, KIAA0040 enhances glioma growth by preventing tumour cell death and promoting cell cycle advancement. Our findings suggest that targeting KIAA0040 could be an effective treatment for glioma due to its role in promoting aggressive tumour behaviour and poor prognosis.

Efficacy of interstitial photodynamic therapy using talaporfin sodium and a semiconductor laser for a mouse allograft glioma model.

To investigate the therapeutic potential of photodynamic therapy (PDT) for malignant gliomas arising in unresectable sites, we investigated the effect of tumor tissue damage by interstitial PDT (i-PDT) using talaporfin sodium (TPS) in a mouse glioma model in which C6 glioma cells were implanted subcutaneously. A kinetic study of TPS demonstrated that a dose of 10 mg/kg and 90 min after administration was appropriate dose and timing for i-PDT. Performing i-PDT using a small-diameter plastic optical fiber demonstrated that an irradiation energy density of 100 J/cm2 or higher was required to achieve therapeutic effects over the entire tumor tissue. The tissue damage induced apoptosis in the area close to the light source, whereas vascular effects, such as fibrin thrombus formation occurred in the area slightly distant from the light source. Furthermore, when irradiating at the same energy density, irradiation at a lower power density for a longer period of time was more effective than irradiation at a higher power density for a shorter time. When performing i-PDT, it is important to consider the rate of delivery of the irradiation light into the tumor tissue and to set irradiation conditions that achieve an optimal balance between cytotoxic and vascular effects.

Radiotherapy plus temozolomide with or without anlotinib in H3K27M-mutant diffuse midline glioma: A retrospective cohort study.

BACKGROUND: Besides the hallmark of H3K27M mutation, aberrant amplifications of receptor tyrosine kinases (RTKs) are commonly observed in diffuse midline glioma (DMG), a highly malignant brain tumor with dismal prognosis. Here, we intended to evaluate the efficacy and safety of a multitarget RTK inhibitor anlotinib in patients with H3K27M-DMG. METHODS: A total of 40 newly diagnosed H3K27M-DMG patients including 15 with anlotinib and 25 without anlotinib treatment were retrospectively enrolled in this cohort. Progression-free survival (PFS), overall survival (OS), and toxicities were assessed and compared. RESULTS: The median PFS and OS of all patients in this cohort were 8.5 months (95% CI, 6.5-11.3) and 15.5 months (95% CI, 12.6-17.1), respectively. According to the Response Assessment in Neuro-Oncology (RANO) criteria, the disease control rate in the anlotinib group [93.3%, 95% confidence interval (CI), 70.2-98.8] was significantly higher than those without anlotinib (64%, 95% CI: 40.5-79.8, p = 0.039). The median PFS of patients with and without anlotinib was 11.6 months (95% CI, 7.8-14.3) and 6.4 months (95% CI, 4.3-10.3), respectively. Both the median PFS and OS of DMG patients treated with anlotinib were longer than those without anlotinib in the infratentorial patients (PFS: 10.3 vs. 5.4 months, p = 0.006; OS: 16.6 vs. 8.7 months, p = 0.016). Multivariate analysis also indicated anlotinib (HR: 0.243, 95% CI: 0.066-0.896, p = 0.034) was an independent prognosticator for longer OS in the infratentorial subgroup. In addition, the adverse events of anlotinib administration were tolerable in the whole cohort. CONCLUSIONS: This study first reported that anlotinib combined with Stupp regimen is a safe and feasible regimen for newly diagnosed patients with H3K27M-DMG. Further, anlotinib showed significant efficacy for H3K27M-DMG located in the infratentorial region.

[Fully Automatic Glioma Segmentation Algorithm of Magnetic Resonance Imaging Based on 3D-UNet With More Global Contextual Feature Extraction: An Improvement on Insufficient Extraction of Global Features]. / 3DGE-UNetç£å ±æŒ¯æˆåƒçš„è„‘èƒ¶è´¨ç˜¤å ¨è‡ªåŠ¨åˆ†å‰²ç®—æ³•ï¼šå¯¹ä¸å åˆ†å ¨å±€ç‰¹å¾æå–çš„æ”¹è¿›.

Objective: The fully automatic segmentation of glioma and its subregions is fundamental for computer-aided clinical diagnosis of tumors. In the segmentation process of brain magnetic resonance imaging (MRI), convolutional neural networks with small convolutional kernels can only capture local features and are ineffective at integrating global features, which narrows the receptive field and leads to insufficient segmentation accuracy. This study aims to use dilated convolution to address the problem of inadequate global feature extraction in 3D-UNet. Methods: 1) Algorithm construction: A 3D-UNet model with three pathways for more global contextual feature extraction, or 3DGE-UNet, was proposed in the paper. By using publicly available datasets from the Brain Tumor Segmentation Challenge (BraTS) of 2019 (335 patient cases), a global contextual feature extraction (GE) module was designed. This module was integrated at the first, second, and third skip connections of the 3D UNet network. The module was utilized to fully extract global features at different scales from the images. The global features thus extracted were then overlaid with the upsampled feature maps to expand the model's receptive field and achieve deep fusion of features at different scales, thereby facilitating end-to-end automatic segmentation of brain tumors. 2) Algorithm validation: The image data were sourced from the BraTs 2019 dataset, which included the preoperative MRI images of 335 patients across four modalities (T1, T1ce, T2, and FLAIR) and a tumor image with annotations made by physicians. The dataset was divided into the training, the validation, and the testing sets at an 8∶1∶1 ratio. Physician-labelled tumor images were used as the gold standard. Then, the algorithm's segmentation performance on the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) was evaluated in the test set using the Dice coefficient (for overall effectiveness evaluation), sensitivity (detection rate of lesion areas), and 95% Hausdorff distance (segmentation accuracy of tumor boundaries). The performance was tested using both the 3D-UNet model without the GE module and the 3DGE-UNet model with the GE module to internally validate the effectiveness of the GE module setup. Additionally, the performance indicators were evaluated using the 3DGE-UNet model, ResUNet, UNet++, nnUNet, and UNETR, and the convergence of these five algorithm models was compared to externally validate the effectiveness of the 3DGE-UNet model. Results: 1) In internal validation, the enhanced 3DGE-UNet model achieved Dice mean values of 91.47%, 87.14%, and 83.35% for segmenting the WT, TC, and ET regions in the test set, respectively, producing the optimal values for comprehensive evaluation. These scores were superior to the corresponding scores of the traditional 3D-UNet model, which were 89.79%, 85.13%, and 80.90%, indicating a significant improvement in segmentation accuracy across all three regions (P<0.05). Compared with the 3D-UNet model, the 3DGE-UNet model demonstrated higher sensitivity for ET (86.46% vs. 80.77%) (P<0.05) , demonstrating better performance in the detection of all the lesion areas. When dealing with lesion areas, the 3DGE-UNet model tended to correctly identify and capture the positive areas in a more comprehensive way, thereby effectively reducing the likelihood of missed diagnoses. The 3DGE-UNet model also exhibited exceptional performance in segmenting the edges of WT, producing a mean 95% Hausdorff distance superior to that of the 3D-UNet model (8.17 mm vs. 13.61 mm, P<0.05). However, its performance for TC (8.73 mm vs. 7.47 mm) and ET (6.21 mm vs. 5.45 mm) was similar to that of the 3D-UNet model. 2) In the external validation, the other four algorithms outperformed the 3DGE-UNet model only in the mean Dice for TC (87.25%), the mean sensitivity for WT (94.59%), the mean sensitivity for TC (86.98%), and the mean 95% Hausdorff distance for ET (5.37 mm). Nonetheless, these differences were not statistically significant (P>0.05). The 3DGE-UNet model demonstrated rapid convergence during the training phase, outpacing the other external models. Conclusion: The 3DGE-UNet model can effectively extract and fuse feature information on different scales, improving the accuracy of brain tumor segmentation.

Executive Functions in a Patient with Low-Grade Glioma of the Central Nervous System: A Case Report.

Central nervous system tumors produce adverse outcomes in daily life, although low-grade gliomas are rare in adults. In neurological clinics, the state of impairment of executive functions goes unnoticed in the examinations and interviews carried out. For this reason, the objective of this study was to describe the executive function of a 59-year-old adult neurocancer patient. This study is novel in integrating and demonstrating biological effects and outcomes in performance evaluated by a neuropsychological instrument and psychological interviews. For this purpose, pre- and post-evaluations were carried out of neurological and neuropsychological functioning through neuroimaging techniques (iRM, spectroscopy, electroencephalography), hospital medical history, psychological interviews, and the Wisconsin Card Classification Test (WCST). There was evidence of deterioration in executive performance, as evidenced by the increase in perseverative scores, failure to maintain one's attitude, and an inability to learn in relation to clinical samples. This information coincides with the evolution of neuroimaging over time. Our case shows that the presence of the tumor is associated with alterations in executive functions that are not very evident in clinical interviews or are explicit in neuropsychological evaluations. In this study, we quantified the degree of impairment of executive functions in a patient with low-grade glioma in a middle-income country where research is scarce.

Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.

Demographic bias in misdiagnosis by computational pathology models.

Despite increasing numbers of regulatory approvals, deep learning-based computational pathology systems often overlook the impact of demographic factors on performance, potentially leading to biases. This concern is all the more important as computational pathology has leveraged large public datasets that underrepresent certain demographic groups. Using publicly available data from The Cancer Genome Atlas and the EBRAINS brain tumor atlas, as well as internal patient data, we show that whole-slide image classification models display marked performance disparities across different demographic groups when used to subtype breast and lung carcinomas and to predict IDH1 mutations in gliomas. For example, when using common modeling approaches, we observed performance gaps (in area under the receiver operating characteristic curve) between white and Black patients of 3.0% for breast cancer subtyping, 10.9% for lung cancer subtyping and 16.0% for IDH1 mutation prediction in gliomas. We found that richer feature representations obtained from self-supervised vision foundation models reduce performance variations between groups. These representations provide improvements upon weaker models even when those weaker models are combined with state-of-the-art bias mitigation strategies and modeling choices. Nevertheless, self-supervised vision foundation models do not fully eliminate these discrepancies, highlighting the continuing need for bias mitigation efforts in computational pathology. Finally, we demonstrate that our results extend to other demographic factors beyond patient race. Given these findings, we encourage regulatory and policy agencies to integrate demographic-stratified evaluation into their assessment guidelines.

PSMA2 promotes glioma proliferation and migration via EMT.

BACKGROUND: Gliomas advance rapidly and are associated with a poor prognosis. Epithelial-mesenchymal transition (EMT) accelerates the progression of gliomas, exerting a pivotal role in glioma development. Proteasome subunit alpha type-2 (PSMA2) exhibits high expression levels in gliomas. however, its specific involvement in glioma progression and its correlation with EMT remain elusive. This study aims to elucidate the role of PSMA2 in glioma progression and its potential association with EMT. METHODS: Online tools were employed to analyze the expression patterns and survival curves of PSMA2 in gliomas. The relationship between PSMA2 and various characteristics of glioma patients was investigated using data from the TCGA and CGGA databases. In vitro, cell proliferation and migration were assessed through CCK-8, colony formation, and transwell assays. Furthermore, a tumor xenograft model in nude mice was established to evaluate in vivo tumorigenesis. Protein binding to PSMA2 was scrutinized using co-immunoprecipitation MS (co-IP MS). The potential biological functions and molecular pathways associated with PSMA2 were explored through GO analysis and KEGG analysis, and the correlation between PSMA2 and EMT was validated through correlation analysis and Western blot experiments. RESULTS: Bioinformatics analysis revealed a significant upregulation of PSMA2 across various cancers, with particularly heightened expression in gliomas. Moreover, elevated PSMA2 levels were correlated with advanced tumor stages and diminished survival rates among glioma patients. Inhibition of PSMA2 demonstrated a pronounced suppressive effect on glioma cell proliferation, both in vitro and in vivo. Knockdown of PSMA2 also impeded the migratory capacity of glioma cells. GO and KEGG enrichment analyses indicated that PSMA2-binding proteins (identified through Co-IP-MS) were associated with cell adhesion molecule binding and cadherin binding. Western blot results further confirmed the role of PSMA2 in promoting epithelial-mesenchymal transition (EMT) in glioma cells. CONCLUSION: Our study provides evidence supporting the role of PSMA2 as a regulatory factor in EMT and suggests its potential as a prognostic biomarker for glioma progression.

Functional and oncological outcomes after right hemisphere glioma resection in awake versus asleep patients: a systematic review and meta-analysis.

The right hemisphere has been underestimated by being considered as the non-dominant hemisphere. However, it is involved in many functions, including movement, language, cognition, and emotion. Therefore, because lesions on this side are usually not resected under awake mapping, there is a risk of unfavorable neurological outcomes. The goal of this study is to compare the functional and oncological outcomes of awake surgery (AwS) versus surgery under general anesthesia (GA) in supratentorial right-sided gliomas. A systematic review of the literature according to PRISMA guidelines was performed up to March 2023. Four databases were screened. Primary outcome to assess was return to work (RTW). Secondary outcomes included the rate of postoperative neurological deficit, postoperative Karnofsky Performance Status (KPS) score and the extent of resection (EOR). A total of 32 articles were included with 543 patients who underwent right hemisphere tumor resection under awake surgery and 294 under general anesthesia. There were no significant differences between groups regarding age, gender, handedness, perioperative KPS, tumor location or preoperative seizures. Preoperative and long-term postoperative neurological deficits were statistically lower after AwS (p = 0.03 and p < 0.01, respectively), even though no difference was found regarding early postoperative course (p = 0.32). A subsequent analysis regarding type of postoperative impairment was performed. Severe postoperative language deficits were not different (p = 0.74), but there were fewer long-term mild motor and high-order cognitive deficits (p < 0.05) in AwS group. A higher rate of RTW (p < 0.05) was documented after AwS. The EOR was similar in both groups. Glioma resection of the right hemisphere under awake mapping is a safer procedure with a better preservation of high-order cognitive functions and a higher rate of RTW than resection under general anesthesia, despite similar EOR.

Laser interstitial thermal therapy for recurrent glioblastomas: a systematic review and meta-analysis.

We aim to investigate the efficacy and safety of laser interstitial thermal therapy (LITT) in treating recurrent glioblastomas (rGBMs). A comprehensive search was conducted in four databases to identify studies published between January 2001 and June 2022 that reported prognosis information of rGBM patients treated with LITT as the primary therapy. The primary outcomes of interest were progression-free survival (PFS) and overall survival (OS) at 6 and 12 months after LITT intervention. Adverse events and complications were also evaluated. Eight eligible non-comparative studies comprising 128 patients were included in the analysis. Seven studies involving 120 patients provided data for the analysis of PFS. The pooled PFS rate at 6 months after LITT was 25% (95% CI 15-37%, I2 = 53%), and at 12 months, it was 9% (95% CI 4-15%, I2 = 24%). OS analysis was performed on 54 patients from six studies, with an OS rate of 92% (95% CI 84-100%, I2 = 0%) at 6 months and 42% (95% CI 13-73%, I2 = 67%) at 12 months after LITT. LITT demonstrates a favorable safety profile with low complication rates and promising tumor control and overall survival rates in patients with rGBMs. Tumor volume and performance status are important factors that may influence the effectiveness of LITT in selected patients. Additionally, the combination of LITT with immune-based therapy holds promise. Further well-designed clinical trials are needed to expand the application of LITT in glioma treatment.

Haploinsufficiency of phosphodiesterase 10A activates PI3K/AKT signaling independent of PTEN to induce an aggressive glioma phenotype.

Glioblastoma is universally fatal and characterized by frequent chromosomal copy number alterations harboring oncogenes and tumor suppressors. In this study, we analyzed exome-wide human glioblastoma copy number data and found that cytoband 6q27 is an independent poor prognostic marker in multiple data sets. We then combined CRISPR-Cas9 data, human spatial transcriptomic data, and human and mouse RNA sequencing data to nominate PDE10A as a potential haploinsufficient tumor suppressor in the 6q27 region. Mouse glioblastoma modeling using the RCAS/tv-a system confirmed that Pde10a suppression induced an aggressive glioma phenotype in vivo and resistance to temozolomide and radiation therapy in vitro. Cell culture analysis showed that decreased Pde10a expression led to increased PI3K/AKT signaling in a Pten-independent manner, a response blocked by selective PI3K inhibitors. Single-nucleus RNA sequencing from our mouse gliomas in vivo, in combination with cell culture validation, further showed that Pde10a suppression was associated with a proneural-to-mesenchymal transition that exhibited increased cell adhesion and decreased cell migration. Our results indicate that glioblastoma patients harboring PDE10A loss have worse outcomes and potentially increased sensitivity to PI3K inhibition.