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The epidermal growth factor receptor (EGFR) plays a pivotal role in tumor progression and is an essential therapeutic target for treating malignant gliomas. Small interfering RNA (siRNA) has the potential to selectively degrade EGFR mRNA, yet its clinical utilization is impeded by various challenges, such as inefficient targeting and limited escape from lysosomes. Our research introduces polyethylene glycol (PEG) and endoplasmic reticulum membrane-coated siEGFR nanoplexes (PEhCv/siEGFR NPs) as an innovative approach to brain glioma therapy by overcoming several obstacles: 1) Tumor-derived endoplasmic reticulum membrane modifications provide a homing effect, facilitating targeted accumulation and cellular uptake; 2) Endoplasmic reticulum membrane proteins mediate a non-degradable "endosome-Golgi-endoplasmic reticulum" transport pathway, circumventing lysosomal degradation. These nanoplexes demonstrated significantly enhanced siEGFR gene silencing in both in vitro and in vivo U87 glioma models. The findings of this study pave the way for the advanced design and effective application of nucleic acid-based therapeutic nanocarriers.

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BACKGROUND: Precise glioma segmentation from multi-parametric magnetic resonance (MR) images is essential for brain glioma diagnosis. However, due to the indistinct boundaries between tumor sub-regions and the heterogeneous appearances of gliomas in volumetric MR scans, designing a reliable and automated glioma segmentation method is still challenging. Although existing 3D Transformer-based or convolution-based segmentation networks have obtained promising results via multi-modal feature fusion strategies or contextual learning methods, they widely lack the capability of hierarchical interactions between different modalities and cannot effectively learn comprehensive feature representations related to all glioma sub-regions. PURPOSE: To overcome these problems, in this paper, we propose a 3D hierarchical cross-modality interaction network (HCMINet) using Transformers and convolutions for accurate multi-modal glioma segmentation, which leverages an effective hierarchical cross-modality interaction strategy to sufficiently learn modality-specific and modality-shared knowledge correlated to glioma sub-region segmentation from multi-parametric MR images. METHODS: In the HCMINet, we first design a hierarchical cross-modality interaction Transformer (HCMITrans) encoder to hierarchically encode and fuse heterogeneous multi-modal features by Transformer-based intra-modal embeddings and inter-modal interactions in multiple encoding stages, which effectively captures complex cross-modality correlations while modeling global contexts. Then, we collaborate an HCMITrans encoder with a modality-shared convolutional encoder to construct the dual-encoder architecture in the encoding stage, which can learn the abundant contextual information from global and local perspectives. Finally, in the decoding stage, we present a progressive hybrid context fusion (PHCF) decoder to progressively fuse local and global features extracted by the dual-encoder architecture, which utilizes the local-global context fusion (LGCF) module to efficiently alleviate the contextual discrepancy among the decoding features. RESULTS: Extensive experiments are conducted on two public and competitive glioma benchmark datasets, including the BraTS2020 dataset with 494 patients and the BraTS2021 dataset with 1251 patients. Results show that our proposed method outperforms existing Transformer-based and CNN-based methods using other multi-modal fusion strategies in our experiments. Specifically, the proposed HCMINet achieves state-of-the-art mean DSC values of 85.33% and 91.09% on the BraTS2020 online validation dataset and the BraTS2021 local testing dataset, respectively. CONCLUSIONS: Our proposed method can accurately and automatically segment glioma regions from multi-parametric MR images, which is beneficial for the quantitative analysis of brain gliomas and helpful for reducing the annotation burden of neuroradiologists.

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In the field of histopathology, many studies on the classification of whole slide images (WSIs) using artificial intelligence (AI) technology have been reported. We have studied the disease progression assessment of glioma. Adult-type diffuse gliomas, a type of brain tumor, are classified into astrocytoma, oligodendroglioma, and glioblastoma. Astrocytoma and oligodendroglioma are also called low grade glioma (LGG), and glioblastoma is also called glioblastoma multiforme (GBM). LGG patients frequently have isocitrate dehydrogenase (IDH) mutations. Patients with IDH mutations have been reported to have a better prognosis than patients without IDH mutations. Therefore, IDH mutations are an essential indicator for the classification of glioma. That is why we focused on the IDH1 mutation. In this paper, we aimed to classify the presence or absence of the IDH1 mutation using WSIs and clinical data of glioma patients. Ensemble learning between the WSIs model and the clinical data model is used to classify the presence or absence of IDH1 mutation. By using slide level labels, we combined patch-based imaging information from hematoxylin and eosin (H & E) stained WSIs, along with clinical data using deep image feature extraction and machine learning classifier for predicting IDH1 gene mutation prediction versus wild-type across cohort of 546 patients. We experimented with different deep learning (DL) models including attention-based multiple instance learning (ABMIL) models on imaging data along with gradient boosting machine (LightGBM) for the clinical variables. Further, we used hyperparameter optimization to find the best overall model in terms of classification accuracy. We obtained the highest area under the curve (AUC) of 0.823 for WSIs, 0.782 for clinical data, and 0.852 for ensemble results using MaxViT and LightGBM combination, respectively. Our experimental results indicate that the overall accuracy of the AI models can be improved by using both clinical data and images.

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OBJECTIVE: To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram. METHODS: Data from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA). RESULTS: A cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance. CONCLUSION: Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.

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OBJECTIVE: Despite the growing acceptance of neuronavigation in the field of neurosurgery, there is limited comparative research with contradictory results. This study aimed to compare the effectiveness (tumor resection rate and survival) and safety (frequency of neurological complications) of surgery for brain gliomas with or without neuronavigation. METHODS: This retrospective cohort study evaluated data obtained from electronic records of patients who underwent surgery for gliomas at Dr. Alejandro Dávila Bolaños Military Hospital and the Clinic Hospital of Barcelona between July 2016 and September 2022. The preoperative and postoperative clinical and radiologic characteristics were analyzed and compared according to the use of neuronavigation. RESULTS: This study included 110 patients, of whom 79 underwent surgery with neuronavigation. Neuronavigation increased gross total resection by 57% in patients in whom it was used; gross total resection was performed in 56% of patients who underwent surgery with neuronavigation as compared with 35.5% in those who underwent surgery without neuronavigation (risk ratio [RR], 1.57; P=0.056). The incidence of postoperative neurologic deficits (transient and permanent) decreased by 79% with the use of neuronavigation, (12% vs. 33.3%; RR, 0.21; P=0.0003). Neuronavigation improved survival in patients with grade IV gliomas (15 months vs. 13.8 months), but it was not statistically significant (odds ratio (OR), 0.19; P=0.13). CONCLUSIONS: Neuronavigation improved the effectiveness (greater gross total resection of tumors) and safety (fewer neurological deficits) of brain glioma surgery. However, neuronavigation does not significantly influence the survival of patients with grade IV gliomas.

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The effectiveness of photodynamic therapy (PDT) in treating brain gliomas is limited by the solubility of photosensitizers and the production of reactive oxygen species (ROS), both of which are influenced by the concentration of photosensitizers and catalyst active sites. In this study, we developed a controllable surface hydroxyl concentration for the photosensitizer CN11 to address its poor water solubility issue and enhance PDT efficacy in tumor treatment. Compared to pure g-C3N4 (CN), CN11 exhibited 4.6 times higher hydrogen peroxide production under visible light, increased incidence of the n → π* electron transition, and provided more available reaction sites for cytotoxic ROS generation. These findings resulted in a 2.43-fold increase in photodynamic treatment efficacy against brain glioma cells. Furthermore, in vivo experiments conducted on mice demonstrated that CN11 could be excreted through normal cell metabolism with low cytotoxicity and high biosafety, effectively achieving complete eradication of tumor cells.

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This study conducted a retrospective analysis on 107 brain glioma patients treated from January 2018 to February 2020 to assess the impact of sodium fluorescein-guided microsurgery on postoperative cognitive function and short-term outcomes. Patients were divided into two groups: a control group (n=50 patients) undergoing routine surgery and a research group (n=57 patients) receiving sodium fluorescein-guided microsurgery. The study compared postoperative total resection rates, changes in cognitive scores, and neuropeptide levels in cerebrospinal fluid between the groups. The findings revealed that the research group experienced shorter surgical time and hospitalization duration, reduced blood loss, and higher total resection rates compared to the control group. Furthermore, the research group demonstrated improvements in cognitive scores and an increase in neuropeptide levels after surgery. There was no significant difference in the comparison of the incidence of postoperative complications between the two groups. The WHO classification and preoperative performance scores were independent prognostic factors for the evaluation of 3-year survival, highlighting the clinical significance of sodium fluorescein-guided microsurgery in improving quality of life and cognitive functions of patients without compromising their long-term survival outcomes.

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PURPOSE: Diffuse gliomas present a significant challenge for healthcare systems globally. While brain MRI plays a vital role in diagnosis, prognosis, and treatment monitoring, accurately characterizing gliomas using conventional MRI techniques alone is challenging. In this study, we explored the potential of utilizing the amide proton transfer (APT) technique to predict tumor grade and type based on the WHO 2021 Classification of CNS Tumors. METHODS: Forty-two adult patients with histopathologically confirmed brain gliomas were included in the study. They underwent 3T MRI imaging, which involved APT sequence. Multinomial and binary logistic regression models were employed to classify patients into clinically relevant groups based on MRI findings and demographic variables. RESULTS: We found that the best model for tumor grade classification included patient age along with APT values. The highest sensitivity (88%) was observed for Grade 4 tumors, while Grade 3 tumors showed the highest specificity (79%). For tumor type classification, our model incorporated four predictors: APT values, patient's age, necrosis, and the presence of hemorrhage. The glioblastoma group had the highest sensitivity and specificity (87%), whereas balanced accuracy was the lowest for astrocytomas (0.73). CONCLUSION: The APT technique shows great potential for noninvasive evaluation of diffuse gliomas. The changes in the classification of gliomas as per the WHO 2021 version of the CNS Tumor Classification did not affect its usefulness in predicting tumor grade or type.

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BACKGROUND AND OBJECTIVE: Mutations in isocitrate dehydrogenase 1 (IDH1) play a crucial role in the prognosis, diagnosis, and treatment of gliomas. However, current methods for determining its mutation status, such as immunohistochemistry and gene sequencing, are difficult to implement widely in routine clinical diagnosis. Recent studies have shown that using deep learning methods based on pathological images of glioma can predict the mutation status of the IDH1 gene. However, our research focuses on utilizing multi-scale information in pathological images to improve the accuracy of predicting IDH1 gene mutations, thereby providing an accurate and cost-effective prediction method for routine clinical diagnosis. METHODS: In this paper, we propose a multi-scale fusion gene identification network (MultiGeneNet). The network first uses two feature extractors to obtain feature maps at different scale images, and then by employing a bilinear pooling layer based on Hadamard product to realize the fusion of multi-scale features. Through fully exploiting the complementarity among features at different scales, we are able to obtain a more comprehensive and rich representation of multi-scale features. RESULTS: Based on the Hematoxylin and Eosin stained pathological section dataset of 296 patients, our method achieved an accuracy of 83.575 % and an AUC of 0.886, thus significantly outperforming other single-scale methods. CONCLUSIONS: Our method can be deployed in medical aid systems at very low cost, serving as a diagnostic or prognostic tool for glioma patients in medically underserved areas.

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Glioblastoma is a primary brain tumor with high incidence and mortality rates, posing a significant threat to human health. It is crucial to provide necessary diagnostic assistance for its management. Among them, Multi-threshold Image Segmentation (MIS) is considered the most efficient and intuitive method in image processing. In recent years, many scholars have combined different metaheuristic algorithms with MIS to improve the quality of Image Segmentation (IS). Slime Mould Algorithm (SMA) is a metaheuristic approach inspired by the foraging behavior of slime mould populations in nature. In this investigation, we introduce a hybridized variant named BDSMA, aimed at overcoming the inherent limitations of the original algorithm. These limitations encompass inadequate exploitation capacity and a tendency to converge prematurely towards local optima when dealing with complex multidimensional problems. To bolster the algorithm's optimization prowess, we integrate the original algorithm with a robust exploitative operator called Differential Evolution (DE). Additionally, we introduce a strategy for handling solutions that surpass boundaries. The incorporation of an advanced cooperative mixing model accelerates the convergence of BDSMA, refining its precision and preventing it from becoming trapped in local optima. To substantiate the effectiveness of our proposed approach, we conduct a comprehensive series of comparative experiments involving 30 benchmark functions. The results of these experiments demonstrate the superiority of our method in terms of both convergence speed and precision. Moreover, within this study, we propose a MIS technique. This technique is subsequently employed to conduct experiments on IS at both low and high threshold levels. The effectiveness of the BDSMA-based MIS technique is further showcased through its successful application to the medical image of brain glioblastoma. The evaluation of these experimental outcomes, utilizing image quality metrics, conclusively underscores the exceptional efficacy of the algorithm we have put forth.

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Brain glioma is a common gynecological tumor. MicroRNA (miRNA) plays a very important role in the pathogenesis and development of tumors. It was found that glycolysis played important regulatory roles in tumor growth. The present study aims to investigate the expression pattern of miR-21-5p in brain glioma cells. We examined miR-21-5p and PFKFB2 levels in brain glioma cells via qRT-PCR. Then we performed CCK-8 and Transwell migration assays and determined glucose uptake and lactose production to unveil the properties of miR-21-5p in invasion, cell viability, along with glycolysis in brain glioma cells. Luciferase activity assay was implemented to elucidate if PFKFB2 was a miR-21-5p target gene. Western blotting and qRT-PCR were executed to further validate that miR-21-5p targeted PFKFB2. We repeated these functional assays to observe whether miR-21-5p could impede the function of PFKFB2. qRT-PCR signified that miR-21-5p was elevated in brain glioma tissues in contrast to matching adjacent normal tissues. Functional assays disclosed that elevation of miR-21-5p promoted cell viability, invasion, together with glycolysis. Luciferase assay indicated that PFKFB2 was a miR-21-5p target gene. Moreover, miR-21-inhibit could hinder cell viability, invasion, and glycolysis triggered by overexpression of PFKFB2 in brain glioma cells. miR-21-5p level is elevated in brain glioma and can impede brain glioma cell growth via regulating the glycolysis mediated by PFKFB2, thus is a potential target of treating brain glioma.

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Understanding the key factors in the tumor microenvironment (TME) that affect the prognosis of gliomas is crucial. In this study, we sought to uncover the prognostic significance of immune cells and immune-related genes in the TME of gliomas. We incorporated data of 970 glioma patient samples from the Chinese Glioma Genome Atlas (CGGA) database as the training set, and an additional set of 666 samples from The Cancer Genome Atlas (TCGA) database served as the validation set. From our analysis, we identified 21 immune-related differentially expressed genes (DEGs) in the TME, which holds implications for glioma prognosis. Based on these genes, we constructed a prognostic risk model on the 21 genes. The prognostic risk model demonstrated robust performance with an area under the curve (AUC) value of 0.848. Notably, the risk score derived from the model emerged as an independent prognostic factor of gliomas, with high risk scores indicative of an unfavorable prognosis. Furthermore, we observed that high infiltration levels of certain immune cells, namely, activated dendritic cells, M0 macrophages, M2 macrophages, and regulatory T cells (Tregs), correlated with an unfavorable glioma prognosis. In conclusion, our findings suggested that the TME of gliomas harbored a distinct immune-associated signature, comprising 21 immune-related genes and specific immune cells. These elements significantly influence the prognosis and present potential as novel indicators in the clinical assessment of glioma patient outcomes.

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BACKGROUND: Accurate segmentation of brain glioma is a critical prerequisite for clinical diagnosis, surgical planning and treatment evaluation. In current clinical workflow, physicians typically perform delineation of brain tumor subregions slice-by-slice, which is more susceptible to variabilities in raters and also time-consuming. Besides, even though convolutional neural networks (CNNs) are driving progress, the performance of standard models still have some room for further improvement. PURPOSE: To deal with these issues, this paper proposes an attention-guided multi-scale context aggregation network (AMCA-Net) for the accurate segmentation of brain glioma in the magnetic resonance imaging (MRI) images with multi-modalities. METHODS: AMCA-Net extracts the multi-scale features from the MRI images and fuses the extracted discriminative features via a self-attention mechanism for brain glioma segmentation. The extraction is performed via a series of down-sampling, convolution layers, and the global context information guidance (GCIG) modules are developed to fuse the features extracted for contextual features. At the end of the down-sampling, a multi-scale fusion (MSF) module is designed to exploit and combine all the extracted multi-scale features. Each of the GCIG and MSF modules contain a channel attention (CA) module that can adaptively calibrate feature responses and emphasize the most relevant features. Finally, multiple predictions with different resolutions are fused through different weightings given by a multi-resolution adaptation (MRA) module instead of the use of averaging or max-pooling to improve the final segmentation results. RESULTS: Datasets used in this paper are publicly accessible, that is, the Multimodal Brain Tumor Segmentation Challenges 2018 (BraTS2018) and 2019 (BraTS2019). BraTS2018 contains 285 patient cases and BraTS2019 contains 335 cases. Simulations show that the AMCA-Net has better or comparable performance against that of the other state-of-the-art models. In terms of the Dice score and Hausdorff 95 for the BraTS2018 dataset, 90.4% and 10.2 mm for the whole tumor region (WT), 83.9% and 7.4 mm for the tumor core region (TC), 80.2% and 4.3 mm for the enhancing tumor region (ET), whereas the Dice score and Hausdorff 95 for the BraTS2019 dataset, 91.0% and 10.7 mm for the WT, 84.2% and 8.4 mm for the TC, 80.1% and 4.8 mm for the ET. CONCLUSIONS: The proposed AMCA-Net performs comparably well in comparison to several state-of-the-art neural net models in identifying the areas involving the peritumoral edema, enhancing tumor, and necrotic and non-enhancing tumor core of brain glioma, which has great potential for clinical practice. In future research, we will further explore the feasibility of applying AMCA-Net to other similar segmentation tasks.

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Objective:To explore and analyze the influence of amyloid precursor protein (APP) protein high expression in brain glioma tissue on postoperative radiotherapy.Methods:Retrospective analysis was performed on 96 patients with surgically resected glioma admitted to the Department of Oncology, Affiliated Hospital of Jining Medical College from Feb. 2020 to Feb. 2022. Clinicopathologic data such as gender, age, tumor location, number of lesions, surgical resection, overall tumor burden reduction rate, radiotherapy dose, maximum lesion diameter of preoperative KPS score and Ki-67 proliferation index were collected, and 96 brain glioma tissue samples were collected.According to the effect of postoperative radiotherapy, the patients were divided into a good radiotherapy effect group (54 cases) and a poor radiotherapy effect group (42 cases), and the number, location, size, pathological grade and other clinical data of the two groups were compared. The expression level of APP in low-grade glioma (52 cases) and high-grade glioma tissue (44 cases) was detected by Western blot method. The clinicopathologic data of patients in the group with good radiotherapy effect and the group with poor radiotherapy effect were analyzed by single factor to find out the influencing factors of postoperative radiotherapy for patients with glioma. Multivariate Logistic analysis was used to analyze the relationship between APP protein expression and postoperative radiotherapy for glioma.Results:Among the 96 brain glioma patients, 56 cases had high expression of APP protein, with high expression rate of 58.33%. The high expression rate of APP protein in high grade gliomas was higher than that in low grade gliomas ( χ2=6.924, P < 0.05). Evaluation results of postoperative radiotherapy for 96 glioma patients showed complete remission was in 21 cases (21.88%), partial remission in 33 cases (34.38%), stable in 26 cases (27.08%) and progressive in 16 cases (16.67%), with an objective remission rate of 56.25% (54/96). Univariate analysis showed that the proportion of high grade glioma, maximum lesion diameter ≥6 cm, Ki67 proliferation index ≥10% and high expression of APP protein in the poor radiotherapy effect group was higher than that in the good radiotherapy effect group ( χ2=6.959, 9.423, 24.282, 37.481, P < 0.05). Multivariate Logistic regression analysis showed that the pathological grade of high-grade glioma ( OR=2.838, 95% CI: 1.501-5.366) and high expression of APP protein ( OR=3.089, 95% CI: 1.364-6.996) were independent risk factors for the effect of postoperative radiotherapy in patients with glioma ( P < 0.05) . Conclusions:APP protein is highly expressed in glioma tissues, and its expression increases with the increase of the pathological grade of glioma. APP protein high expression is an independent risk factor affecting the adverse effect of postoperative radiotherapy in glioma patients.

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Liquid biopsy techniques based on deep sequencing of plasma cell-free DNA (cfDNA) could detect the low-frequency somatic mutations and provide an accurate diagnosis for many cancers. However, for brain gliomas, reliable performance of these techniques currently requires obtaining cfDNA from patients' cerebral spinal fluid, which is cumbersome and risky. Here we report a liquid biopsy method based on sequencing of plasma cfDNA fragments carrying 5-hydroxymethylcytosine (5hmC) using selective chemical labeling (hMe-Seal). We first constructed a dataset including 180 glioma patients and 229 non-glioma controls. We found marked concordance between cfDNA hydroxymethylome and the aberrant transcriptome of the underlying gliomas. Functional analysis also revealed overrepresentation of the differentially hydroxymethylated genes (DhmGs) in oncogenic and neural pathways. After splitting our dataset into training and test cohort, we showed that a penalized logistic model constructed with training set DhmGs could distinguish glioma patients from healthy controls in both our test set (AUC = 0.962) and an independent dataset (AUC = 0.930) consisting of 111 gliomas and 111 controls. Additionally, the DhmGs between gliomas with mutant and wild-type isocitrate dehydrogenase (IDH) could be used to train a cfDNA predictor of the IDH mutation status of the underlying tumor (AUC = 0.816), and patients with predicted IDH mutant gliomas had significantly better outcome (P = .01). These results indicate that our plasma cfDNA 5hmC sequencing method could obtain glioma-specific signals, which may be used to noninvasively detect these patients and predict the aggressiveness of their tumors.

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Purpose The aim of this study was to evaluate the role of permeability surface area product in grading brain gliomas using computed tomography (CT) perfusion Materials and Methods CT perfusion was performed on 33 patients with brain glioma diagnosed on magnetic resonance imaging. Of these, 19 had high-grade glioma and 14 had low-grade glioma on histopathological follow-up. CT perfusion values were obtained and first compared between the tumor region and normal brain parenchyma. Then the relative values of perfusion parameters were compared between high- and low-grade gliomas. Cut-off values, sensitivity, specificity, and strength of agreement for each parameter were calculated and compared subsequently. A conjoint factor (permeability surface area product + cerebral blood volume) was also evaluated since permeability surface area product and cerebral blood volume are considered complimentary factors for tumor vascularity. Results All five perfusion parameters namely permeability surface area product, cerebral blood volume, cerebral blood flow, mean transit time, and time to peak were found significantly higher in the tumor region than normal brain parenchyma. Among these perfusion parameters, only relative permeability surface area product and relative cerebral blood volume were found significant in differentiating high- and low-grade glioma. Moreover, relative permeability surface area product was significantly better than all other perfusion parameters with highest sensitivity and specificity (97.74 and 100%, respectively, at a cut-off of 9.0065). Relative permeability surface area product had a very good agreement with the histopathology grade. The conjoint factor did not yield any significant diagnostic advantage over permeability surface area product. Conclusion Relative permeability surface area product and relative cerebral blood volume were helpful in differentiating high- and low-grade glioma; however, relative permeability surface area product was significantly better than all other perfusion parameters. Grading brain gliomas using relative permeability surface area product can add crucial value in their management and prognostication; hence, it should be evaluated in the routine CT perfusion imaging protocol.

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BACKGROUND: The complexity and refractory of brain glioma requires treatment that should involve a multidisciplinary approach to improve quality of care and fulfill patients' needs. AIM: To explore the effects of comprehensive nursing on postoperative complications, psychological state and quality of life in patients with brain glioma. METHODS: A total of 106 patients with confirmed brain gliomas admitted to Nanchong Central Hospital between January 2019 and May 2021 were selected by random sampling. They were categorized into an observation group and a control group using a random number table with 53 patients in each group. Patients in the observation group were given comprehensive nursing in addition to conventional nursing and patients in the control group were given conventional nursing. The overall incidence of postoperative complications including limb dysfunction, high fever and epilepsy was compared between the two groups. The mental status was evaluated in the two groups before and after intervention using self-rating anxiety scale (SAS) and self-rating depression scale (SDS). Quality of life was assessed and compared using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire between the two groups before and after the intervention. RESULTS: After intervention, the overall incidence of postoperative complications was significantly lower in the observation group (7.55%) than that in the control group (20.75%) (P < 0.05). Before intervention, there was no significant difference in SAS and SDS scores between the two groups (P > 0.05). However, after intervention, scores of SAS and SDS decreased in the two groups compared with those before intervention, and the scores of SAS and SDS were lower in the observation group than in the control group (all P < 0.05). There was no significant difference in quality of life between the two groups before the intervention (P > 0.05). In contrast, quality of life increased in the two groups compared with those before intervention, and it was higher in the observation group than in the control group (P < 0.05). CONCLUSION: Comprehensive nursing can reduce the incidence of postoperative complications, improve the psychological state of anxiety and depression and improve quality of life in patients with brain glioma.

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OBJECTIVE: The aim of this study was to investigate the impact of valproic acid (VPA) on survival and prognosis of patients with glioma who underwent postoperative radiotherapy. METHODS: We obtained the case data with brain glioma who underwent postoperative radiotherapy from January 2012 to December 2019. This cohort was heterogeneous. We conducted single-factor analysis and multiple-factors analysis of the basic features, pathological classification, therapies of all 185 patients using Kaplan-Meier survival curve, log-rank survival significance test, and Cox regression analysis model. RESULTS: By the end of the last follow-up, 94 patients had died and 96 had recurred in all 185 cases. The median follow-up time of this study was 47 months. The median overall survival (OS) and progression-free survival (PFS) times were 34 and 27 months, respectively. The 1-, 3-, and 5-year OS rates were 86.49%, 48.11%, and 14.60%, respectively. The 1-, 3-, and 5-year PFS rates were 80.00%, 43.78%, and 12.97%, respectively. Univariate analysis revealed that age, pathological grade, and VPA administration were all associated with patients' prognosis (p < 0.05). A Cox multivariate analysis revealed that being 47 years or older, having a high pathological grade (WHO grades III and IV), and not taking VPA were all adverse prognostic factors for OS and PFS in patients with glioma. CONCLUSION: Age, pathological grade, and VPA administration are the influencing factors for the prognosis of glioma patients with postoperative radiotherapy. Patients with glioma who received VPA had a more favorable prognosis and a lower recurrence rate.

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PURPOSE: Artificial intelligence (AI), in particular deep neural networks, has achieved remarkable results for medical image analysis in several applications. Yet the lack of explainability of deep neural models is considered the principal restriction before applying these methods in clinical practice. METHODS: In this study, we propose a NeuroXAI framework for explainable AI of deep learning networks to increase the trust of medical experts. NeuroXAI implements seven state-of-the-art explanation methods providing visualization maps to help make deep learning models transparent. RESULTS: NeuroXAI has been applied to two applications of the most widely investigated problems in brain imaging analysis, i.e., image classification and segmentation using magnetic resonance (MR) modality. Visual attention maps of multiple XAI methods have been generated and compared for both applications. Another experiment demonstrated that NeuroXAI can provide information flow visualization on internal layers of a segmentation CNN. CONCLUSION: Due to its open architecture, ease of implementation, and scalability to new XAI methods, NeuroXAI could be utilized to assist radiologists and medical professionals in the detection and diagnosis of brain tumors in the clinical routine of cancer patients. The code of NeuroXAI is publicly accessible at https://github.com/razeineldin/NeuroXAI .

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Background: The input image of a blurry glioma image segmentation is, usually, very unclear. It is difficult to obtain the accurate contour line of image segmentation. The main challenge facing the researchers is to correctly determine the area where the points on the contour line belong to the glioma image. This article highlights the mechanism of formation of glioma and provides an image segmentation prediction model to assist in the accurate division of glioma contour points. The proposed prediction model of segmentation associated with the process of the formation of glioma is innovative and challenging. Bose-Einstein Condensate (BEC) is a microscopic quantum phenomenon in which atoms condense to the ground state of energy as the temperature approaches absolute zero. In this article, we propose a BEC kernel function and a novel prediction model based on the BEC kernel to detect the relationship between the process of the BEC and the formation of a brain glioma. Furthermore, the theoretical derivation and proof of the prediction model are given from micro to macro through quantum mechanics, wave, oscillation of glioma, and statistical distribution of laws. The prediction model is a distinct segmentation model that is guided by BEC theory for blurry glioma image segmentation. Results: Our approach is based on five tests. The first three tests aimed at confirming the measuring range of T and µ in the BEC kernel. The results are extended from -10 to 10, approximating the standard range to T ≤ 0, and µ from 0 to 6.7. Tests 4 and 5 are comparison tests. The comparison in Test 4 was based on various established cluster methods. The results show that our prediction model in image evaluation parameters of P, R, and F is the best amongst all the existent ten forms except for only one reference with the mean value of F that is between 0.88 and 0.93, while our approach returns between 0.85 and 0.99. Test 5 aimed to further compare our results, especially with CNN (Convolutional Neural Networks) methods, by challenging Brain Tumor Segmentation (BraTS) and clinic patient datasets. Our results were also better than all reference tests. In addition, the proposed prediction model with the BEC kernel is feasible and has a comparative validity in glioma image segmentation. Conclusions: Theoretical derivation and experimental verification show that the prediction model based on the BEC kernel can solve the problem of accurate segmentation of blurry glioma images. It demonstrates that the BEC kernel is a more feasible, valid, and accurate approach than a lot of the recent year segmentation methods. It is also an advanced and innovative model of prediction deducing from micro BEC theory to macro glioma image segmentation.