Multi-modal medical image (MI) fusion assists in generating collaboration images collecting complement features through the distinct images of several conditions. The images help physicians to diagnose disease accurately. Hence, this research proposes a novel multi-modal MI fusion modal named guided filter-based interactive multi-scale and multi-modal transformer (Trans-IMSM) fusion approach to develop high-quality computed tomography-magnetic resonance imaging (CT-MRI) fused images for brain tumor detection. This research utilizes the CT and MRI brain scan dataset to gather the input CT and MRI images. At first, the data preprocessing is carried out to preprocess these input images to improve the image quality and generalization ability for further analysis. Then, these preprocessed CT and MRI are decomposed into detail and base components utilizing the guided filter-based MI decomposition approach. This approach involves two phases: such as acquiring the image guidance and decomposing the images utilizing the guided filter. A canny operator is employed to acquire the image guidance comprising robust edge for CT and MRI images, and the guided filter is applied to decompose the guidance and preprocessed images. Then, by applying the Trans-IMSM model, fuse the detail components, while a weighting approach is used for the base components. The fused detail and base components are subsequently processed through a gated fusion and reconstruction network, and the final fused images for brain tumor detection are generated. Extensive tests are carried out to compute the Trans-IMSM method\'s efficacy. The evaluation results demonstrated the robustness and effectiveness, achieving an accuracy of 98.64% and an SSIM of 0.94.

We describe a case of a young patient with a recurrent pleomorphic xanthoastrocytoma (PXA) showing unusual cell-in-cell (CiC) phenomena. We observed mostly viable but also necrotic neutrophils engulfed within tumor cells. The recurrent tumor was immunopositive for BRAFV600E mutant protein and showed CDKN2 homozygous deletions typical of PXA. Both genetic alterations were also reported in the original primary tumor. Unlike the original tumor that was GFAP and Olig-2 immunopositive, the recurrent neoplasm was largely negative for GFAP and Olig-2 suggesting dedifferentiation. The large malignant cells that contained the neutrophils were negative for histiocytic and lymphohematopoietic markers. Whereas CDKN2 homozygous deletion is common in PXA, its presence is rare in histiocytic neoplasms. Both reactive astrocytes and glial neoplasms very rarely may engulf neutrophils in a process resembling emperipolesis or cellular cannibalism. Future work may clarify which type of CiC pathway is involved.

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN\'s optimal hyperparameters. The CNN network, however, can be considered a \"black box\" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN\'s assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don\'t quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.

Glioblastoma (GBM) conventional treatment is not curative, and it is associated with severe toxicity. Thus, natural compounds with anti-cancer properties and lower systemic toxicity, such as gallic acid (GA), have been explored as alternatives. However, GA\'s therapeutic effects are limited due to its rapid metabolism, low bioavailability, and low permeability across the blood-brain barrier (BBB). This work aimed to develop poly (lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) modified with folic acid (FA), as its receptor is overexpressed in BBB and GBM cells, for GA delivery to enhance its therapeutic efficacy. The preparation of NPs was optimized by a central composite design (CCD). The obtained NPs showed physicochemical features suitable for drug internalization in BBB and tumor cells (sizes below 200 nm, monodispersity, and negative surface charge) and the ability to maintain a slow and sustained release for 40 days. In vitro studies using a human GBM cell line (U215) revealed the NPs\' ability to accumulate in the target cells, further promoting GA antiproliferative activity by inducing the production of intracellular reactive oxygen species (ROS). Furthermore, GA encapsulation in the developed nanosystems conferred higher protection to healthy cells.

The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization\'s (WHO) classification.

Chronic occlusion of the superior sagittal sinus (SSS) by tumors in the midsagittal region causes the collateral venous pathway (CVP). Understanding common patterns of CVP is helpful in reducing surgical complications. This study aimed to investigate the CVP found in patients with SSS-invading tumors, and to provide information on the prevention of operative venous complications. From January 2015 to December 2022, this retrospective study collected patients with tumors that invaded the SSS and underwent digital subtraction angiography of intracranial vessels. Data collected included sex, age, tumor pathology, tumor location along the SSS, tumor side, degree of obstruction of the SSS, types and route patterns of the CVP, and the distance between the tumor and the diploic vein (DV). Twenty patients (6 males, 14 females) were recruited. The prevalence of CVP types was 90% for DV, 35% for end-to-end anastomosis of superficial cortical vein, 15% for meningeal vein, and 20% for other types of CVP. The pteriofrontoparietal and occipitoparietal diploic routes were found on the cerebral hemisphere contralateral to the tumor significantly more than in the cerebral hemisphere ipsilateral to the tumor. Of all patients with presence of collateral DV, 61% had a very close (less than 1 cm) distance between the nearest DV and tumor attachment in the SSS. DV in the cerebral hemisphere contralateral to the tumor was the most common type of CVP found in patients with tumor-induced SSS obstruction. Most of the collateral DV was located very close to the SSS tumor attachment. Neurosurgeons should realize these findings when planning a craniotomy.

UNASSIGNED: The ultrasonic surgical aspirator is widely used in intracranial tumor resection as this instrument is considered safe. The advantage of an ultrasonic surgical aspirator is that it does not damage vessels or nerves close to the tumor. Therefore, limited information exists regarding intraoperative arterial injury by the ultrasonic surgical aspirator.
UNASSIGNED: We report two cases. The first case was a 30-year-old woman who underwent surgery for a recurrent craniopharyngioma, and the second was a 50-year-old man who underwent surgery for a meningioma. A craniopharyngioma encased the basilar artery in the former case, and the superior cerebellar artery was encased by a meningioma in the latter. An ultrasonic surgical aspirator was used to resect the tumors in two cases. During surgery, the arteries involved in the tumors were unintentionally injured using an ultrasonic surgical aspirator. Intraoperative hemostasis was achieved for the bleeding from the injured arteries. However, postoperative digital cerebral angiography revealed pseudoaneurysms in the injured arteries. A subarachnoid hemorrhage occurred in the first case. The pseudoaneurysms were managed using endovascular embolization.
UNASSIGNED: Intraoperative arterial injury can occur with the application of an ultrasonic surgical aspirator. Neurosurgeons should be cautious when using ultrasonic surgical aspirators to avoid damaging the arteries involved with the tumor.

Alterations in miRNA levels have been observed in various types of cancer, impacting numerous cellular processes and increasing their potential usefulness in combination therapies also in brain tumors. Recent advances in understanding the genetics and epigenetics of brain tumours point to new aberrations and associations, making it essential to continually update knowledge and classification. Here we conducted molecular analysis of 123 samples of childhood brain tumors (pilocytic astrocytoma, medulloblastoma, ependymoma), focusing on identification of genes that could potentially be regulated by crucial representatives of OncomiR-1: miR-17-5p and miR-20a-5p. On the basis of microarray gene expression analysis and qRTPCR profiling, we selected six (WEE1, CCND1, VEGFA, PTPRO, TP53INP1, BCL2L11) the most promising target genes for further experiments. The WEE1, CCND1, PTPRO, TP53INP1 genes showed increased expression levels in all tested entities with the lowest increase in the pilocytic astrocytoma compared to the ependymoma and medulloblastoma. The obtained results indicate a correlation between gene expression and the WHO grade and subtype. Furthermore, our analysis showed that the integration between genomic and epigenetic pathways should now point the way to further molecular research.

BACKGROUND: Serum albumin reflects nutritional status and is associated with postoperative complications and mortality. Delta albumin (ΔAlb), defined as the difference between preoperative and lowest postoperative levels, could predict complications and mortality, even with post-op levels above 30 g/L prompting albumin infusions. This study aimed to assess how ΔAlb relates to outcomes in craniotomy patients with brain tumors.
METHODS: This retrospective study screened patients diagnosed with a brain tumor who underwent cerebral surgery from a single Chinese hospital between December 2010 and April 2021. Patients were divided into four groups based on their ΔAlb levels: <5 g/L (normal), 5-9.9 g/L (mild ΔAlb), 10-14.9 g/L (moderate ΔAlb), and ≥15 g/L (severe ΔAlb). The primary outcome was postoperative 30-day mortality.
RESULTS: Among the 9660 patients undergoing craniotomy for brain tumors, the median ΔAlb level after craniotomy was 7.3 g/L. ΔAlb was associated with increased postoperative 30-day mortality; Odds ratios (OR) for mild, moderate, and severe ΔAlb were 1.93(95% CI, 1.17-3.18,P=0.01), 2.21(95% CI, 1.28-3.79,P=0.004), and 7.26(95% CI, 4.19-12.58,P<0.01), respectively. Significantly, ΔAlb >5g/L was found to have a strong association with a higher risk of mortality, even when the nadir Alb remained greater than 30 g/L (OR, 1.84; 95% CI, 1.13- 3.00, P=0.014).
CONCLUSIONS: Among patients undergoing craniotomy for brain tumor resection, a mild degree of ΔAlb was associated with increased 30-day mortality, even if the nadir Alb remained greater than 30 g/L. Moreover, ΔAlb was associated with postoperative complications and longer lengths of stay.

OBJECTIVE: To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors.
METHODS: High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors.
RESULTS: The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 μT and 11.2% vs. 7.8% at 1.5 μT).
CONCLUSIONS: The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.