Search Results (33)

Virotherapy is a promising method for treating oncological diseases, including such aggressive and difficult-to-treat brain tumors such as glioblastoma. Recombinant vaccinia virus VV-GMCSF-Lact has previously shown high antitumor potential against tumor cells of varying histogenesis, including gliomas, and completed a Phase I clinical trial, demonstrating safety and good tolerability in patients with recurrent/refractory metastatic breast cancer. Investigating two types of VV-GMCSF-Lact delivery, intravenous and intratumoral, into orthotopically transplanted C6 glioma in rats, it was shown that intratumoral injection significantly increases tumor volumes in comparison with intravenous virus delivery and is accompanied by noticeable toxic effects. Extensive areas of necrotic decay of tumor tissue and its significant mixed-cell infiltration and peritumoral edema, affecting the tumor volume, were detected using H&E staining of C6 tumors after intratumoral injection of VV-GMCSF-Lact. However, only with intratumoral administration was a significant decrease in the level of the tumor cell proliferation marker Ki67 demonstrated by immunohistochemical staining. The observed toxic effects of VV-GMCSF-Lact with intratumoral administration revealed the need for dose selection, which was performed on a mouse GL261 glioma model. Results of the study allowed us to determine the viral dose that does not lead to toxic effects and can potentially increase life expectancy of mice. The data obtained show the need for careful selection of both the route of viral drug dose and administration. Full article

We aimed to investigate the utility of peritumoral edema-derived radiomic features from magnetic resonance imaging (MRI) image weights and fused MRI sequences for enhancing the performance of machine learning-based glioma grading. The present study utilized the Multimodal Brain Tumor Segmentation Challenge 2023 (BraTS 2023) dataset. Laplacian Re-decomposition (LRD) was employed to fuse multimodal MRI sequences. The fused image quality was evaluated using the Entropy, standard deviation (STD), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics. A comprehensive set of radiomic features was subsequently extracted from peritumoral edema regions using PyRadiomics. The Boruta algorithm was applied for feature selection, and an optimized classification pipeline was developed using the Tree-based Pipeline Optimization Tool (TPOT). Model performance for glioma grade classification was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC) parameters. Analysis of fused image quality metrics confirmed that the LRD method produces high-quality fused images. From 851 radiomic features extracted from peritumoral edema regions, the Boruta algorithm selected different sets of informative features in both standard MRI and fused images. Subsequent TPOT automated machine learning optimization analysis identified a fine-tuned Stochastic Gradient Descent (SGD) classifier, trained on features from T₁Gd+FLAIR fused images, as the top-performing model. This model achieved superior performance in glioma grade classification (Accuracy = 0.96, Precision = 1.0, Recall = 0.94, F1-Score = 0.96, AUC = 1.0). Radiomic features derived from peritumoral edema in fused MRI images using the LRD method demonstrated distinct, grade-specific patterns and can be utilized as a non-invasive, accurate, and rapid glioma grade classification method. Full article

Objectives: To investigate the correlation between intraoperative conventional ultrasound, SWE, and SMI ultrasound manifestations of glioma and the expression of immunohistochemical markers. Methods: Patients with single superficial supratentorial glioma scheduled for brain tumor resection in our neurosurgery department from October 2020 to October 2022 were prospectively included. High-grade glioma (HGG) and low-grade glioma (LGG) were classified by pathological histological grading, and the differences in conventional ultrasound, SWE Young’s modulus, and SMI intratumoral and peritumoral blood flow architecture between HGG and LGG were analyzed, and the SWE diagnostic cut-off value was calculated by the Youdon index. Logistic regression models were used to analyze the independent predictive ultrasound signs associated with the diagnosis of HGG. HGG and LGG were classified by pathological histological grading. IDH1 expression was measured by immunohistochemical methods to analyze the correlation between IDH1 expression in glioma and clinical and ultrasound characteristics. Results: Forty-eight patients with glioma admitted to our hospital from October 2020 to October 2022 were included in this study, including 30 (62.5%) with HGG and 18 (37.5%) with LGG. For conventional ultrasound, HGG was often associated with severe peritumoral edema compared with LGG (p = 0.048). The sensitivity of HGG was 88.9%, the specificity was 86.7%, and the AUC was 0.855 (95% confidence interval: 0.741–0.968, p = 0.001) using Young’s mode 13.90 kPa as the threshold. Logistic analysis showed that SWE Young’s modulus values, and peritumoral and intratumoral SMI blood flow structures, were associated with the diagnosis of HGG. Among the 48 gliomas, 22 (45.8%) were IDH1-positive and 26 (54.2%) were IDH1-negative, with no statistical difference in age between the two groups and a statistical difference in histological grading (p < 0.05). There was a statistical difference between IDH1 mutant and wild type in terms of peritumoral edema and SMI intratumoral and peritumoral tissue vascular architecture. Logistic regression models showed that intratumoral and peritumoral tissue SMI vascular architecture was a valid predictor of IDH1 positivity, with a classification accuracy of 81.3%, sensitivity of 90.9%, and specificity of 73.1%. Further group analysis of mutant Young’s modulus values in LGG were higher than wild-type Young’s modulus values (p = 0.031). Conclusions: Peritumoral and intratumoral tissue SMI vascular architecture was a valid predictor of IDH1 positivity. Based on intraoperative ultrasound multimodality images, we can preoperatively determine the expression of molecular markers of lesions, which is of clinical significance for optimizing surgical strategies and predicting prognosis. Full article

Accurate segmentation of key tumor subregions in adult gliomas from Magnetic Resonance Imaging (MRI) is of critical importance for brain tumor diagnosis, treatment planning, and prognosis. However, this task remains poorly investigated and highly challenging due to the considerable variability in shape and appearance of these areas across patients. This study proposes a novel Deep Learning architecture leveraging modality-specific encoding and attention-based refinement for the segmentation of glioma subregions, including peritumoral edema (ED), necrotic core (NCR), and enhancing tissue (ET). The model is trained and validated on the Brain Tumor Segmentation (BraTS) 2023 challenge dataset and benchmarked against a state-of-the-art transformer-based approach. Our architecture achieves promising results, with Dice scores of 0.78, 0.86, and 0.88 for NCR, ED, and ET, respectively, outperforming SegFormer3D while maintaining comparable model complexity. To ensure a comprehensive evaluation, performance was also assessed on standard composite tumor regions, i.e., tumor core (TC) and whole tumor (WT). The statistically significant improvements obtained on all regions highlight the effectiveness of integrating complementary modality-specific information and applying channel-wise feature recalibration in the proposed model. Full article

Background: The arcuate fasciculus (AF) is a critical white matter (WM) tract that connects key cortical language-processing regions, including the so-called Broca’s and Wernicke’s areas. The aim of the present study was to quantitatively assess its radiological–anatomical–morphometric modifications according to different brain tumor histotypes. Methods: A retrospective multicentric Italian study was conducted. AF reconstructions were calculated for both hemispheres for each patient diagnosed with glioblastoma (GBM), low-grade glioma (LGG), brain metastasis, and meningioma using Elements Fibertracking 2.0 software (Brainlab AG, Munich, Germany). A 3D object of each fascicle was evaluated for its volume, average fractional anisotropy (FA), and length. The cerebral healthy hemisphere was compared to the pathological contralateral in different tumor histotypes. Results: In total, 1294 patients were evaluated. A total of 156 met the inclusion criteria. We found a significant difference between healthy hemisphere and the contralateral for AF mean length and volume (p = 0.01 and p < 0.001, respectively). Considering separately the different tumor histotypes, the GBM subgroup (98, 63%) confirmed the results for mean FA and volume (p-value < 0.001); LGG patients (26, 17%) showed no significant difference between healthy and pathological hemisphere for AF mean length, mean FA, and volume (p-value 0.5, p-value 0.3, p-value <0.1, respectively). In patients affected by brain metastasis (18, 12%), Student’s t-test showed a significant difference for FA (p-value 0.003). No differences were found in patients affected by meningiomas (14, 9%) (14). Conclusions: Thorough knowledge of the microscopic anatomy and function of the arcuate fasciculus, as well as the pattern of growth of the different brain tumor histotypes, along with a careful preoperative neuroradiological assessment are mandatory to plan a tailored surgical strategy and perform a safe and effective surgical technique. The AF could be displaced and infiltrated/destructed by the solid component and peritumoral edema, respectively, of GBM. LGG shows a prevalent infiltrative pattern. Metastases account for AF dislocation due to peritumoral edema. Meningiomas do not affect WM anatomy. Full article

Background/Objectives: This study aimed to investigate whether small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) can be distinguished based on radiomics data derived from T2-FLAIR digital subtraction images of the peritumoral edema region in patients with brain metastases. Methods: A total of 136 patients who underwent surgery for brain tumors, including 100 patients in the Pretreat-Metstobrain-MASKS dataset and 36 patients from our institution, were included in our study. Radiomic features were extracted from digitally subtracted T2-FLAIR images in the peritumoral edema area. Patients were divided into NSCLC and SCLC groups. The maximum relevance–minimum redundancy (mRMR) method was then used for dimensionality reduction. The Naive Bayes algorithm was used for model development, and the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). The performance metrics included the area under the curve (AUC), sensitivity (SENS), and specificity (SPEC). Results: The mean age of NSCLC patients was 64.6 ± 10.3 years, and that of SCLC patients was 63.4 ± 11.7 years. In the external validation cohort, the model achieved an AUC of 0.82 (0.68–0.97), a SENS of 0.87 (0.74–0.91), and a SPEC of 0.72 (0.72–0.89). In the train cohort, the model achieved an AUC of 1.000, a SENS of 1.000, and a SPEC of 1.000. The feature providing the best effect was wavelet-HHHglcmJointEnergy, with a SHAP value of approximately 2.5. Conclusions: An artificial intelligence model developed using radiomics data from T2-FLAIR digital subtraction images of the peritumoral edema area can identify the histologic type of lung cancer in patients with associated brain metastases. Full article

Background: Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are critical for guiding treatment strategies and patient counseling. Methods: We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers with structured clinical and demographic data to predict overall survival time. Our dataset includes 148 patients from three institutions, featuring expert-annotated segmentations of enhancing tumors, necrosis, and peritumoral edema. Two convolutional neural network backbones—ResNet-50 and EfficientNet-B0—were fused with fully connected layers processing tabular data. Models were trained using mean squared error loss and evaluated through stratified cross-validation and an independent held-out test set. Results: The hybrid model based on EfficientNet-B0 achieved state-of-the-art performance, attaining an R² score of 0.970 and a mean absolute error of 3.05 days on the test set. Permutation feature importance highlighted edema-to-tumor ratio and enhancing tumor volume as the most informative predictors. Grad-CAM visualizations confirmed the model’s attention to anatomically and clinically relevant regions. Performance consistency across validation folds confirmed the framework’s robustness and generalizability. Conclusions: This study demonstrates that multimodal deep learning can deliver accurate, explainable, and clinically actionable survival predictions in brain metastases. The proposed framework offers a promising foundation for integration into real-world oncology workflows to support personalized prognosis and informed therapeutic decision-making. Full article

Background: Falx and convexity meningiomas can compress surrounding brain structures, frequently resulting in epileptic seizures, which adversely impact the quality of life (QoL) of affected patients. This study aimed to assess postoperative QoL in patients with documented perioperative seizures associated with falx and convexity meningiomas. Methods: The Quality of Life in Epilepsy Inventory-31 (QOLIE-31) was administered to patients who underwent surgery for falx/convexity meningiomas and experienced perioperative seizures. Seizures were defined as those occurring within 30 days pre- or post-surgery. Results: A total of 77 patients responded to the questionnaire, of whom 44 were female and 33 were male. Multivariable analysis showed that falx meningioma was associated with a lower QOLIE-31 score (OR = 4.02, 95% CI: 1.14–14.18, p = 0.03). Furthermore, the presence of peritumoral edema was also associated with lower QOLIE-31 scores (OR = 3.89, 95% CI: 1.04–14.47, p = 0.043). Conclusions: This study demonstrates that perioperative seizures in patients with falx meningiomas significantly impact postoperative quality of life. The presence of peritumoral edema was also associated with poorer QoL outcomes. These findings underscore the importance of targeted management strategies to to improve QoL in meningioma patients experiencing seizures. Full article

Image segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image data. These methods have also shown great results in tumor segmentation and the identification of anomalies, which assist health care professionals in treatment planning. Despite advancements made in the domain of deep learning for medical image segmentation, the precise segmentation of tumors remains challenging because of the complex structures of tumors across patients. Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the strengths of both architectures to improve segmentation accuracy and consumes less computational resources by maintaining efficiency. The proposed model possesses the ability to perform explicitly well on scarce data, and we trained this model on the Brain Tumor Segmentation Challenge 2020 (BraTS) dataset. This architecture is inspired by U-Networks that are based on the encoder and decoder architecture. The Hiera pre-trained model is set as a backbone to this architecture to capture multi-scale features. Adapters are embedded into the encoder to achieve parameter-efficient fine-tuning. The dataset contains four channels of MRI scans of 369 glioma patients as T1, T1ce, T2, and T2-flair and a segmentation mask for each patient consisting of non-tumor (NT), necrotic and non-enhancing tumor (NCR/NET), and peritumoral edema or GD-enhancing tumor (ET) as the ground-truth value. These experiments yielded good segmentation performance and achieved balanced performance based on the metrics discussed next in this paragraph for each tumor region. Our experiments yielded the following results with minimal hardware resources, i.e., 16 GB RAM with 30 epochs: a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an $S_{\alpha }$ score of 0.692, a weighted F-beta score ($F_{\beta }^w$) of 0.267, a F-beta score ($F_{\beta }$) of 0.261, an $E_{\varphi }$ score of 0.857, and a Mean Absolute Error (MAE) of 0.04 on the BraTS 2020 dataset. Full article

Skull base meningiomas present considerable challenges in surgical management due to their proximity to critical neurovascular structures. Anterior skull base meningiomas encompass olfactory groove, supra- and parasellar, anterior sphenoid ridge, cavernous sinus, and spheno-orbital tumors. The success of surgical resection and the likelihood of complications are influenced by several key factors, including the presence of an intact arachnoid plane, tumor size and consistency, peritumoral brain edema, cranial nerve involvement, vascular encasement, and invasion of critical areas such as the optic canal or cavernous sinus. These factors not only affect the feasibility of gross total resection but also play a pivotal role in determining functional outcomes and postoperative recovery. With the vast array of imaging modalities available, selecting the most appropriate investigations to assess these parameters and tailoring surgical strategies accordingly remain complex tasks. This review examines the critical surgical parameters, identifies the most effective imaging modalities for evaluating each, and provides key insights into how this analysis can guide surgical decision-making, mitigate risks, and minimize complications. Full article

Background/Objectives: Tumor interactions with their surrounding environment, particularly in the case of peritumoral edema, play a significant role in tumor behavior and progression. While most studies focus on the radiomic features of the tumor core, this work investigates whether peritumoral edema exhibits distinct radiomic fingerprints specific to glioma (GLI), meningioma (MEN), and metastasis (MET). By analyzing these patterns, we aim to deepen our understanding of the tumor microenvironment’s role in tumor development and progression. Methods: Radiomic features were extracted from peritumoral edema regions in T1-weighted (T1), post-gadolinium T1-weighted (T1-c), T2-weighted (T2), and T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) sequences. Three classification tasks using those features were then conducted: differentiating between Low-Grade Glioma (LGG) and High-Grade Glioma (HGG), distinguishing GLI from MET and MEN, and examining all four tumor types, i.e., LGG, HGG, MET, and MEN, to observe how tumor-specific signatures manifest in peritumoral edema. Model performance was assessed using balanced accuracy derived from 10-fold cross-validation. Results: The radiomic fingerprints specific to tumor types were more distinct in the peritumoral regions of T1-c images compared to other modalities. The best models, utilizing all features extracted from the peritumoral regions of T1-c images, achieved balanced accuracies of 0.86, 0.81, and 0.76 for the LGG-HGG, GLI-MET-MEN, and LGG-HGG-MET-MEN tasks, respectively. Conclusions: This study demonstrates that peritumoral edema, as characterized by radiomic features extracted from MRIs, contains fingerprints specific to tumor type, providing a non-invasive approach to understanding tumor-brain interactions. The results of this study hold the potential for predicting recurrence, distinguishing progression from pseudo-progression, and assessing treatment-induced changes, particularly in gliomas. Full article

Background: This case report highlights a rare presentation of a giant convexity en plaque anaplastic meningioma, located in the left frontoparietal parasagittal region, infiltrating the superior sagittal sinus, and associated with Gerstmann syndrome. This study aims to explore the clinical challenges, surgical management, and potential reversibility of neurological deficits induced by the tumor, including those characteristic of Gerstmann syndrome. Methods: A 76-year-old male patient presented with a history of worsening expressive aphasia and cognitive impairments, culminating in a generalized seizure. Preoperative imaging confirmed a 4 × 6 cm highly vascularized tumor with significant peritumoral edema. The patient underwent near-total resection of the tumor, aiming for a Simpson grade 2 resection, while managing hypervascularity and brain edema. Histological analysis confirmed the diagnosis of anaplastic meningioma (WHO Grade III), showing features such as necrosis, brain invasion, and high mitotic activity. Results: Post-surgical follow-up demonstrated significant improvement in the patient’s neurological deficits, particularly in expressive language and cognitive function, suggesting a potential reversal of Gerstmann syndrome. Postoperative imaging revealed a moderate degree of cerebral collapse and absence of contrast leakage. Two-month follow-up confirmed no recurrence of neurological deficits. Conclusions: This case emphasizes the complexity of managing giant convexity en plaque anaplastic meningiomas, particularly when associated with Gerstmann syndrome. Surgical resection, despite the challenges posed by tumor size, hypervascularity, and peritumoral edema, can lead to significant neurological recovery, highlighting the potential reversibility of tumor-induced Gerstmann syndrome. Full article

Background/Objectives: Obstructive hydrocephalus associated with vestibular schwannoma (VS) is the most common in giant VS. Despite tumor removal, some patients may require ongoing ventriculo-peritoneal (VP) surgery. This investigation explores the factors contributing to the requirement for VP surgery following VS surgery in instances of persistent hydrocephalus (HCP). Methods: Volumetric MRI analyses of pre- and postoperative tumor volumes, cerebellum, cerebrum, ventricle system, fourth ventricle, brainstem, and peritumoral edema were conducted using Brainlab Smartbrush and 3D Slicer. The total brain volume was defined as the sum of the cerebrum, cerebellum, and brainstem. ROC analyses were performed to identify the optimum cut-off values of the volumetric data. Results: Permanent cerebrospinal fluid (CSF) diversion after surgery was indicated in 12 patients (12/71; 16.9%). The ratio of baseline volume fraction of brain ventricles to total brain ventricle volume (VTB ratio) was found to predict postoperative VP shunt dependency. The AUC was 0.71 (95% CI: 0.51–0.91), and the optimum threshold value (</≥0.449) yielded a sensitivity and specificity of 67% and 81%, respectively. Multivariable logistic regression analyses of imaging data (pre- and postoperative VS volume, VTB ratio, and extent of resection (%) (EoR)) and patient-specific factors revealed that an increased VTB ratio (≥0.049, OR: 6.2, 95% CI: 1.0–38.0, p = 0.047) and an EoR < 96.4% (OR: 9.1, 95% CI: 1.2–69.3, p = 0.032) were independently associated with postoperative VP shunt dependency. Conclusions: Primary tumor removal remains the best treatment to reduce the risk of postoperative persistent hydrocephalus. However, patients with an increased preoperative VTB ratio are prone to needing postoperative VP shunt surgery and may benefit from perioperative EVD placement. Full article

Glioblastomas synthesize, bear receptors for, and respond to bradykinin, triggering migration and proliferation. Since centrifugal migration into uninvolved surrounding brain tissue occurs early in the course of glioblastoma, this attribute defeats local treatment attempts and is the primary reason current treatments almost always fail. Stopping bradykinin-triggered migration would be a step closer to control of this disease. The recent approval and marketing of an oral plasma kallikrein inhibitor, berotralstat (Orladeyo™), and pending FDA approval of a similar drug, sebetralstat, now offers a potential method for reducing local bradykinin production at sites of bradykinin-mediated glioblastoma migration. Both drugs are approved for treating hereditary angioedema. They are ideal for repurposing as a treatment adjunct in glioblastoma. Furthermore, it has been established that peritumoral edema, a common problem during the clinical course of glioblastoma, is generated in large part by locally produced bradykinin via kallikrein action. Both brain edema and the consequent use of corticosteroids both shorten survival in glioblastoma. Therefore, by (i) migration inhibition, (ii) growth inhibition, (iii) edema reduction, and (iv) the potential for less use of corticosteroids, berotralstat may be of service in treatment of glioblastoma, slowing disease progression. This paper recounts the details and past research on bradykinin in glioblastoma and the rationale of treating it with berotralstat. Full article

Treating malignant glioma is challenging owing to its highly invasive potential in healthy brain tissue and the formation of intense surrounding edema. Peritumoral edema in gliomas can lead to severe symptoms including neurological dysfunction and brain herniation. For the past 50 years, the standard treatment for peritumoral edema has been steroid therapy. However, the discovery of cerebral lymphatic vessels a decade ago prompted a re-evaluation of the mechanisms involved in brain fluid regulation and the formation of cerebral edema. This review aimed to describe the clinical features of peritumoral edema in gliomas. The mechanisms currently known to cause glioma-related edema are summarized, the limitations in current cerebral edema therapies are discussed, and the prospects for future cerebral edema therapies are presented. Further research concerning edema surrounding gliomas is needed to enhance patient prognosis and improve treatment efficacy. Full article