Search Results (1,071)

There is currently no effective therapeutic capable of arresting or inducing regression of primary or metastatic brain cancers. This article presents both pre-clinical and clinical studies supportive that a new bioengineered technology could induce regression and/or elimination of primary and metastatic brain cancers through three disease-modifying mechanisms. Transcranial Radiofrequency Wave Treatment (TRFT) is non-thermal, non-invasive and self-administered in-home to safely provide radiofrequency waves to the entire human brain. Since TRFT has already been shown to stop and reverse the cognitive decline of Alzheimer’s Disease in small studies, evidence is provided that three key mechanisms of TRFT action, alone or in synergy, could effectively treat brain cancers: (1) enhancement of brain meningeal lymph flow to increase immune trafficking between the brain cancer and cervical lymph nodes, resulting in a robust immune attack on the brain cancer; (2) rebalancing of the immune system’s cytokines within the brain or brain cancer environment to decrease inflammation therein and thus make for an inhospitable environment for brain cancer growth; (3) direct anti-proliferation/antigrowth affects within the brain tumor microenvironment. Importantly, these mechanisms of TRFT action could be effective against both visualized brain tumors and those that are yet too small to be identified through brain imaging. The existing animal and human clinical evidence presented in this perspective article justifies TRFT to be clinically tested immediately against both primary and metastatic brain cancers as monotherapy or possibly in combination with immune checkpoint inhibitors. Full article

This paper presents a novel multi-modal segment anything model (MSAM) for glioma tumor segmentation using structural MRI images and diffusion tensor imaging data. We designed an effective multimodal feature fusion block to effectively integrate features from different modalities of data, thereby improving the accuracy of brain tumor segmentation. We have designed an effective missing modality training method to address the issue of missing modalities in actual clinical scenarios. To evaluate the effectiveness of MSAM, a series of experiments were conducted comparing its performance with U-Net across various modality combinations. The results demonstrate that MSAM consistently outperforms U-Net in terms of both Dice Similarity Coefficient and 95% Hausdorff Distance, particularly when structural modality data are used alone. Through feature visualization and the use of missing modality training, we show that MSAM can effectively adapt to missing data, providing robust segmentation even when key modalities are absent. Additionally, segmentation accuracy is influenced by tumor region size, with smaller regions presenting more challenges. These findings underscore the potential of MSAM in clinical applications where incomplete data or varying tumor sizes are prevalent. Full article

Background: The accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is pivotal for timely clinical intervention, yet remains challenged by tumor heterogeneity, morphological variability, and imaging artifacts. Methods: This paper presents a novel hybrid approach for improved brain tumor classification and proposes a novel hybrid deep learning framework that amalgamates the hierarchical feature extraction capabilities of VGG-16, a convolutional neural network (CNN), with the global contextual modeling of FTVT-b16, a fine-tuned vision transformer (ViT), to advance the precision of brain tumor classification. To evaluate the recommended method’s efficacy, two widely known MRI datasets were utilized in the experiments. The first dataset consisted of 7.023 MRI scans categorized into four classes gliomas, meningiomas, pituitary tumors, and no tumor. The second dataset was obtained from Kaggle, which consisted of 3000 scans categorized into two classes, consisting of healthy brains and brain tumors. Results: The proposed framework addresses critical limitations of conventional CNNs (local receptive fields) and pure ViTs (data inefficiency), offering a robust, interpretable solution aligned with clinical workflows. These findings underscore the transformative potential of hybrid architectures in neuro-oncology, paving the way for AI-assisted precision diagnostics. The proposed framework was run on these two different datasets and demonstrated outstanding performance, with accuracy of 99.46% and 99.90%, respectively. Conclusions: Future work will focus on multi-institutional validation and computational optimization to ensure scalability in diverse clinical settings. Full article

Background/Objectives: The rapid development of artificial intelligence is transforming the face of medicine. Due to the large number of imaging studies (pre-, intra-, and postoperative) combined with histopathological and molecular findings, its impact may be particularly significant in neurosurgery. We aimed to perform a scoping review of recent applications of deep learning in MRI-based diagnostics of brain tumors relevant to neurosurgical practice. Methods: We conducted a systematic search of scientific articles available in the PubMed database. The search was performed on 22 April 2024, using the following query: ((MRI) AND (brain tumor)) AND (deep learning). We included original studies that applied deep-learning methods to brain tumor diagnostics using MRI, with potential relevance to neuroradiology or neurosurgery. A total of 893 records were retrieved, and after title/abstract screening and full-text assessment by two independent reviewers, 229 studies met the inclusion criteria. The study was not registered and received no external funding. Results: Most included articles were published after 1 January 2022. The studies primarily focused on developing models to differentiate between specific CNS tumors. With improved radiological analysis, deep-learning technologies can support surgical planning through enhanced visualization of cerebral vessels, white matter tracts, and functional brain areas. Over half of the papers (52%) focused on gliomas, particularly their detection, grading, and molecular characterization. Conclusions: Recent advancements in artificial intelligence methods have enabled differentiation between normal and abnormal CNS imaging, identification of various pathological entities, and, in some cases, precise tumor classification and molecular profiling. These tools show promise in supporting both diagnosis and treatment planning in neurosurgery. Full article

Characterizing complex heterogeneous structures in high-dimensional data remains a significant challenge. Traditional approaches often rely on summary statistics such as histograms, skewness, or kurtosis, which—despite their simplicity—are insufficient for capturing nuanced patterns of heterogeneity. Motivated by a brain tumor study, we consider data in the form of point clouds, where each observation consists of a variable number of points. Our goal is to detect differences in the heterogeneity structures across distinct groups of observations. To this end, we employ the Dirichlet Diffusion Tree (DDT) to characterize the latent heterogeneity structure of each observation. We further extend the DDT framework by introducing a regression component that links covariates to the hyperparameters of the latent trees. We develop a Markov chain Monte Carlo algorithm for posterior inference, which alternatively updates the latent tree structures and the regression coefficients. The effectiveness of our proposed method is evaluated by a simulation study and a real-world application in brain tumor imaging. Full article

Background: Glioblastoma, the most common malignant primary brain tumor in adults, continues to present a major therapeutic challenge, with a median survival of only 12–15 months and a 5-year survival rate below 2%. Despite aggressive treatment—including maximal surgical excision, radiation, and temozolomide (TMZ) chemotherapy—recurrent disease is nearly universal due to the tumor’s infiltrative nature. Objectives: To address the critical need for improved diagnostic and therapeutic strategies for glioblastoma multiforme (GBM), we have developed an innovative theranostic molecule, [^99mTc]Tc-AGT-7. Methods: AGT-7 integrates diagnostic and therapeutic modalities comprising [^99mTc]Tc-TF (a nuclear medicine imaging agent) and TMZ. The diagnostic component has been tailored to selectively accumulate in glioma mitochondria. A chelating moiety enables radiolabeling with technetium-99m (^99mTc) for precise Single-Photon Emission Computed Tomography (SPECT) imaging. The therapeutic arm includes the tethering of a TMZ moiety for localized cytotoxicity. Conclusions: In vitro studies illustrated that AGT-7 has potent cytotoxic effects in GBM cell lines (T98 and U87), with greater efficacy than TMZ, and toxicity assays in zebrafish embryos indicated a favorable safety profile. Biodistribution studies in CFW mice demonstrated that [^99mTc]Tc-AGT-7 exhibited a ~10-fold lower heart uptake compared to [^99mTc]Tc-TF, implying reduced off-target cardiac localization. This significantly lowers the risk of cardiotoxicity and enhances AGT-7’s potential as a glioma-targeted theranostic agent. Full article

Magnetic resonance imaging (MRI), with its high resolution and radiation-free characteristics, has become a crucial tool for brain tumor diagnosis. We classified brain tumors into non-tumors, glioma, meningioma, and pituitary tumors by integrating public image datasets with preprocessing and data augmentation techniques and employing four deep learning models, such as a convolutional neural network (CNN), visual geometry group network 19 (VGGNet 9), residual network 101 version 2 (ResNet101V2), and efficient network version 2 b2 (EfficientNetV2B2). VGGNet19 and CNNs excelled in accuracy and stability, while EfficientNetV2B2 was efficient yet required refinement for specific categories, and ResNet101V2 benefited from further optimization. Deep learning significantly enhances diagnostic efficiency and accuracy, assisting clinical decision-making and improving patient survival rates. Full article

Glioblastoma multiforme (GBM) remains one of the most aggressive and treatment-resistant brain tumors, with poor prognosis and limited therapeutic options. Background/Objectives: Integrin α_vβ₃, a cell surface receptor overexpressed in GBM, specifically binds to cyclic arginine-glycine-aspartate-D-phenylalanine-lysine (c(RGDfK)) motif, making it a valuable target for tumor-specific delivery and PET imaging. This study explores a novel radiotheranostic agent, [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK), which combines the imaging and therapeutic capabilities of copper-64 (⁶⁴Cu) and the cytotoxic activity of a terpyridine-platinum (TP) complex, conjugated to c(RGDfK). Methods: A robust protocol was developed for the small-scale preparation of NOTA-TP-c(RGDfK). Comparative cellular studies were conducted using U87 MG glioblastoma (GBM) cells and SVG p12 human astrocytes to evaluate the performance of [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) relative to [⁶⁴Cu]Cu-NOTA-c(RGDfK), [⁶⁴Cu]Cu-NOTA-TP, ^natCu-NOTA-TP-c(RGDfK), cisplatin, and temozolomide. Results: ⁶⁴Cu-radiolabeling of NOTA-TP-c(RGDfK) was achieved with >99% radiochemical purity, and competition assays confirmed high binding affinity to integrin α_vβ₃ (IC₅₀ = 16 ± 8 nM). Cellular uptake, internalization, and retention studies demonstrated significantly higher accumulation of [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) in U87 MG cells compared to control compounds, with 38.8 ± 1.8% uptake and 28.0 ± 1.0% internalization at 24 h. Nuclear localization (6.0 ± 0.5%) and stable intracellular retention further support its therapeutic potential for inducing localized DNA damage. Importantly, [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) exhibited the highest cytotoxicity in U87 MG cells (IC₅₀ = 10 ± 2 nM at 48 h), while maintaining minimal toxicity in normal SVG p12 astrocytes. Conclusions: These results highlight [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) as a promising targeted radiotheranostic agent for GBM, warranting further preclinical development Full article

Background/Objectives: This study aims to evaluate the additional value of [¹⁸F]F-fluorocholine ([¹⁸F]F-FCH) PET/CT over contrast-enhanced magnetic resonance imaging (CE-MRI) in detecting the recurrence of brain metastases (BMs) after stereotactic radiosurgery (SRS) in patients with lung cancer brain metastases (LCBMs). Methods: Thirty-one patients with suspected recurrence of BM in LCBM after SRS were enrolled in this retrospective study. They underwent both [¹⁸F]F-FCH PET/CT and CE-MRI within 2 weeks. The tumor imaging parameters and clinical features were analyzed. The results of histopathology or radiographic follow-up served as the reference standard for the final diagnosis. Results: In these 31 patients, there were 54 lesions, of which 27 lesions were proven to be BM recurrence, while 27 lesions were non-recurrence. [¹⁸F]F-FCH PET/CT showed high radiotracer uptake in recurrent lesions of BM and identified 24 positive lesions (88.89% of sensitivity), while CE-MRI indicated 23 positive lesions (85.19% of sensitivity). [¹⁸F]F-FCH PET/CT indicated higher specificity (81.48%) and accuracy (85.19%) in detecting recurrence of BM than CE-MRI (40.74% and 62.96%, both p < 0.05), particularly in frontal lobes and cerebella. For lesion sizes, the accuracy of [¹⁸F]F-FCH PET/CT in detecting recurrent lesions was higher than that of CE-MRI for lesions over 1.0 cm but below 2.0 cm (p = 0.016). The detective performance of [¹⁸F]F-FCH PET/CT combined with CE-MRI was higher than [¹⁸F]F-FCH PET/CT or CE-MRI alone (all p < 0.05). Interestingly, TLC (≥4.11) was significantly correlated with poor intracranial PFS (iPFS), meaning it was a significant prognostic factor for iPFS. Conclusions: This study identified that compared with CE-MRI, [¹⁸F]F-FCH PET/CT demonstrated higher specificity and accuracy in diagnosing recurrence of BM in LCBM after SRS. Combining [¹⁸F]F-FCH PET/CT with CE-MRI has the potential to improve diagnostic performance for recurrence of BM and management of patient treatment. TLC was an independent risk factor for iPFS. Full article

Glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by rapid progression, profound heterogeneity, and resistance to conventional therapies. This review provides an integrated overview of GBM’s pathophysiology, highlighting key mechanisms such as neuroinflammation, genetic alterations (e.g., EGFR, PDGFRA), the tumor microenvironment, microbiome interactions, and molecular dysregulations involving gangliosides and sphingolipids. Current diagnostic strategies, including imaging, histopathology, immunohistochemistry, and emerging liquid biopsy techniques, are explored for their role in improving early detection and monitoring. Treatment remains challenging, with standard therapies—surgery, radiotherapy, and temozolomide—offering limited survival benefits. Innovative therapies are increasingly being explored and implemented, including immune checkpoint inhibitors, CAR-T cell therapy, dendritic and peptide vaccines, and oncolytic virotherapy. Advances in nanotechnology and personalized medicine, such as individualized multimodal immunotherapy and NanoTherm therapy, are also discussed as strategies to overcome the blood–brain barrier and tumor heterogeneity. Additionally, stem cell-based approaches show promise in targeted drug delivery and immune modulation. Non-conventional strategies such as ketogenic diets and palliative care are also evaluated for their adjunctive potential. While novel therapies hold promise, GBM’s complexity demands continued interdisciplinary research to improve prognosis, treatment response, and patient quality of life. This review underscores the urgent need for personalized, multimodal strategies in combating this devastating malignancy. Full article

Recent advancements in deep learning have significantly enhanced brain tumor segmentation from MRI data, providing valuable support for clinical diagnosis and treatment planning. However, challenges persist in effectively integrating prior medical knowledge, capturing global multimodal features, and accurately delineating tumor boundaries. To address these challenges, the Hybrid Network for Multimodal Brain Tumor Segmentation (HN-MBTS) is proposed, which incorporates prior medical knowledge to refine feature extraction and boundary precision. Key innovations include the Two-Branch, Two-Model Attention (TB-TMA) module for efficient multimodal feature fusion, the Linear Attention Mamba (LAM) module for robust multi-scale feature modeling, and the Residual Attention (RA) module for enhanced boundary refinement. Experimental results demonstrate that this method significantly outperforms existing approaches. On the BraT2020 and BraT2023 datasets, the method achieved average Dice scores of 87.66% and 88.07%, respectively. These results confirm the superior segmentation accuracy and efficiency of the approach, highlighting its potential to provide valuable assistance in clinical settings. Full article

This narrative review explores the integration of theranostics and artificial intelligence (AI) in neuro-oncology, addressing the urgent need for improved diagnostic and treatment strategies for brain tumors, including gliomas, meningiomas, and pediatric central nervous system neoplasms. A comprehensive literature search was conducted through PubMed, Scopus, and Embase for articles published between January 2020 and May 2025, focusing on recent clinical and preclinical advancements in personalized neuro-oncology. The review synthesizes evidence on novel theranostic agents—such as Lu-177-based radiopharmaceuticals, CXCR4-targeted PET tracers, and multifunctional nanoparticles—and highlights the role of AI in enhancing tumor detection, segmentation, and treatment planning through advanced imaging analysis, radiogenomics, and predictive modeling. Key findings include the emergence of nanotheranostics for targeted drug delivery and real-time monitoring, the application of AI-driven algorithms for improved image interpretation and therapy guidance, and the identification of current limitations such as data standardization, regulatory challenges, and limited multicenter validation. The review concludes that the convergence of AI and theranostic technologies holds significant promise for advancing precision medicine in neuro-oncology, but emphasizes the need for collaborative, multidisciplinary research to overcome existing barriers and enable widespread clinical adoption. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

Using neural networks has become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformer architectures, including Swin Transformer and MaxViT, for several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset included different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer achieved the highest accuracy, with an average of 99.0% on single datasets and reaching 99.43% on the combined dataset. This research highlights the adaptability of Transformer-based models to various human organs and image modalities. The main contribution lies in evaluating multiple ViT architectures across multi-organ tumor datasets, demonstrating their generalization to multi-organ classification. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient healthcare solutions. Full article