Search Results (220)

Pediatric diffuse midline gliomas in the brainstem (bDMGs) are malignant primary brain neoplasms with poor prognosis. Conventional dogma cites that biopsy procedures have risks of devastating injury to the eloquent brainstem and have no direct benefit to affected patients. In recent years, the use of augmented reality (AR) adjuncts has demonstrated potential in providing excellent intraoperative three-dimensional (3D) visualization of intracranial structures. Put together, we hypothesize that the application of AR will be useful as a training tool for brainstem biopsy procedures. Anatomical models of bDMG tumors are created and uploaded to an AR application. The processed data is transferred into designated AR head-mounted devices. Briefly, individual 3D-rendered bDMG images are overlaid with an age-matched, life-sized child mannequin in prone position. A virtual stereotactic brain biopsy needle is deployed by the user into the lesion. At the end of the exercise, each user evaluates their trajectory of choice to assess its accuracy. Overall, the participants reported that the AR platform was useful in reviewing technical nuances for brainstem biopsy in a safe environment. This focused, proof-of-concept study adds to the growing body of literature that AR platforms demonstrate feasibility for neurosurgeons in the understanding of challenging operative neuroanatomy. Full article

In recent years, machine learning (ML) has been increasingly used in many fields, including medicine. Magnetic resonance imaging (MRI) is a non-invasive and effective diagnostic technique; however, manual image analysis is time-consuming and prone to human variability. In response, ML models have been developed to support MRI analysis, particularly in segmentation and classification tasks. This work presents an updated narrative review of ML applications in brain MRI, with a focus on tumor classification and segmentation. A literature search was conducted in PubMed and Scopus databases and Mendeley Catalog (MC)—a publicly accessible bibliographic catalog linked to Elsevier’s Scopus indexing system—covering the period from January 2020 to April 2025. The included studies focused on patients with primary or secondary brain neoplasms and applied machine learning techniques to MRI data for classification or segmentation purposes. Only original research articles written in English and reporting model validation were considered. Studies using animal models, non-imaging data, lacking proper validation, or without accessible full texts (e.g., abstract-only records or publications unavailable through institutional access) were excluded. In total, 108 studies met all inclusion criteria and were analyzed qualitatively. In general, models based on convolutional neural networks (CNNs) were found to dominate current research due to their ability to extract spatial features directly from imaging data. Reported classification accuracies ranged from 95% to 99%, while Dice coefficients for segmentation tasks varied between 0.83 and 0.94. Hybrid architectures (e.g., CNN-SVM, CNN-LSTM) achieved strong results in both classification and segmentation tasks, with accuracies above 95% and Dice scores around 0.90. Transformer-based models, such as the Swin Transformer, reached the highest performance, up to 99.9%. Despite high reported accuracy, challenges remain regarding overfitting, generalization to real-world clinical data, and lack of standardized evaluation protocols. Transfer learning and data augmentation were frequently applied to mitigate limited data availability, while radiomics-based models introduced new avenues for personalized diagnostics. ML has demonstrated substantial potential in enhancing brain MRI analysis and supporting clinical decision-making. Nevertheless, further progress requires rigorous clinical validation, methodological standardization, and comparative benchmarking to bridge the gap between research settings and practical deployment. Full article

Strabismus, or squint or deviating eyes, is defined as misalignment of the eyes when fixating on an object and is a common problem in ophthalmology. Palsy of the third, fourth or sixth cranial nerve is one of the leading underlying causes for paralytic strabismus, often requiring surgery. However, uncertainty regarding factors influencing surgical success remains. Background/Objectives: The purpose of this study is to review the outcome and influencing factors of strabismus surgery in patients with cranial nerve palsy. Methods: A retrospective study of 57 patients with third cranial nerve (CN3) palsy, fourth cranial nerve (CN4) palsy, sixth cranial nerve (CN6) palsy or combined nerve palsy who underwent strabismus surgery between October 2009 and December 2023 was conducted. Analyzed data included demographic details, type of surgical intervention, etiology of nerve palsy, pre- and postoperative angle of deviation (AOD), vertical deviation (VD), best-corrected visual acuity (BCVA), and refractive error. Results: Mean age was 41.29 ± 23.14 years with a mean follow-up of 10.8 ± 15.38 months. 30 patients (52.63%) had CN6 palsy, 12 patients (21.05%) had CN3 palsy, eight patients (14.04%) had CN4 palsy and seven patients (12.28%) had combined nerve palsy. Brain neoplasm was the most common cause of nerve palsy (33.33%). Mean preoperative AOD improved from 17.54° ± 10.68 to 7.13° ± 8.93 and from 17.21° ± 9.58 to 7.49° ± 9.75 for near and distance, respectively (p < 0.001). Changes in VD, refractive error, and BCVA were not statistically significant. Conclusions: Age, gender, preoperative AOD, subtype and etiology of nerve palsy had no significant influence on surgical outcomes, which are satisfactory in patients with cranial nerve palsy (80.7%). Full article

Introduction: Renal cell carcinoma (RCC) develops metastatic disease in 30–50% of patients during their disease course, with approximately one quarter presenting with metastases at diagnosis. While the lungs, liver, bones, brain, and adrenal glands are the most frequent metastatic sites, duodenal involvement is exceptionally rare. This uncommon presentation poses diagnostic and therapeutic challenges, particularly regarding the role of surgical resection in the metastatic setting. Objective: We aim to evaluate the clinical presentation, management strategies, and outcomes of patients with duodenal metastasis from RCC, with particular emphasis on the potential role of surgery, through a systematic review of the literature. Methods: A comprehensive electronic search of Medline, Embase, and Scopus was conducted according to PRISMA guidelines. The following MeSH terms were applied: Kidney Neoplasms [MeSH] AND Duodenal Neoplasms/metastasis [MeSH]. Eligible studies included original reports or case series describing RCC with duodenal metastasis. Demographic, clinical, surgical, and survival data were extracted and synthesized. Results: Of 89 records identified, 83 underwent full-text review and 51 met inclusion criteria, representing 55 patients. The median age at diagnosis was 64 years, and 80% of primary tumors arose from the right kidney. Nearly all patients (98%) were symptomatic, most commonly with upper gastrointestinal bleeding, anemia, or obstructive features. Pancreaticoduodenectomy was the predominant surgical approach, performed with curative intent in selected cases. Patients undergoing surgery achieved a 5-year overall survival of 70%, compared with 0% among non-operated patients. Conclusions: Duodenal metastasis from RCC remains an uncommon entity, limiting the strength of available evidence. Nevertheless, our findings suggest that surgical management—when feasible and decided within a multidisciplinary framework—can provide meaningful survival benefit and should be considered as a complement to contemporary systemic therapies for metastatic RCC Full article

Background: Amino acid tracer positron emission tomography–magnetic resonance imaging (PET-MRI) was shown to be superior to MRI alone for evaluating central nervous system (CNS) tumours in adults. This study aimed to investigate the utility of amino acid PET-MRI in children with CNS tumours. Methods: We reviewed the amino acid PET-MRI findings of children with suspected or confirmed CNS neoplasms managed in a territory-wide referral centre in Hong Kong from 2022 to 2025. Maximal standardized uptake values (SUVmax) were captured, and tumour-to-background SUVmax ratios (TBRmax) were measured with reference to adjacent or contralateral normal brain structures. Comparisons were made among patients with clinical high-grade and low-grade/non-neoplastic lesions. Results: Thirty-seven patients were included, with 63 PET-MRIs performed. PET-MRI was performed as part of initial diagnostics in 41% of the cases, for response assessment in 48%, and evaluation of residual/relapsed disease in 11%. High-grade lesions had a significantly higher SUVmax and TBRmax compared to low-grade/non-malignant lesions (median SUVmax 3.7 vs. 1.6, p = 0.00006; median TBRmax 2.06 vs. 0.91, p = 0.00002). Optimal SUVmax and TBRmax cut-offs by ROC analysis were 2.38 and 1.62, respectively. Similar performance was reproduced by focusing on the subset of patients with suspected CNS germ cell tumours (CNS-GCT). The impact of amino acid PET availability is considerable, as clinical management was modified in 65% of patients. Conclusions: Our study demonstrates the performance and clinical utility of amino acid PET-MRI in the management of children with CNS pathologies. Amino acid PET-MRI contributes to the diagnosis, monitoring, and treatment guidance of these patients, providing crucial information for decision-making. Full article

Background: Glioblastomas (GBMs) are the most aggressive and common neoplasms that affect glial cells, presenting rapid growth, invasion, and resistance to treatments. Studies have demonstrated the potentially inhibitory effect of flavonoids on glioblastoma cells’ stemness and viability. However, further research is needed to explore sensitivity and the mechanism of action in chemoresistant cells. Methods: In this study, we characterized the impact of apigenin treatment on the viability and differentiation of human GBM cells in vitro and its effects on tumorigenesis and regulation of the inflammatory response in vivo. Results: The flavonoid apigenin reduced the viability of U-251 cells, patient-derived cells TG-1 and OB-1 stem cells in a dose-dependent manner, associated with the induction of acidic vesicle organelles formation and apoptosis. Treatment with apigenin also inhibited migration and induced neural differentiation in the remaining viable cells, characterized by a decrease in the expression of the precursor marker nestin and an increase in the expression of astrocyte and neuron markers, GFAP and β-III tubulin, respectively. The xenotransplantation of apigenin-pretreated U251 cells into rat brains did not lead to tumor formation, unlike untreated cells. The surrounding area of transplanted untreated U251 cells exhibited reactive microglia and astrocytes, along with increased VEGF expression, which was absent in implant sites of apigenin-pretreated GBM cells. Moreover, in this implant area, we observed a significant decrease in the expression of mRNA for inflammatory factors IL-1β, TNF, and NOS2, and the downregulation of IL-10 and IL-4. Conclusions: These results demonstrate that apigenin inhibits the growth of tumoral cells, affecting the viability of tumor stem cells and impairing tumorigenicity, while altering the regulatory profile of immunomodulatory proteins. Therefore, this flavonoid can be considered for further studies to determine its use as an adjuvant to the treatment of human GBMs. Full article

The search for markers applicable for efficient differential diagnosis and personalized therapy is a priority task of experimental neuro-oncology. Modern molecular methods allow us to analyze human biopsy material; however, further actions with this extracted tumor tissue are limited. Relevant and sophisticated CNS tumor models are required for precise therapy development. Although it is possible to use human biomaterial to create 2D and 3D cultures and implant them into xenograft animals, the data generated from such models is limited. Due to changes in the classification of the CNS tumors in 2021, a representative model should have not only morphological similarity to human tumors but also key genetic aberrations for studying the mechanisms of carcinogenesis and personalized therapy (such as PDGFRa, Olig1/2, Sox2, and Mki67) for different glioma models such as astrocytoma, oligodendroglioma, and glioblastoma. On the basis of a unique scientific facility “The Collection of experimental tumors of the nervous system and neural tumor cell lines” (Avtsyn Research Institute of Human Morphology of “Petrovsky National Research Center of Surgery”), there is a biobank of chemically induced transplantable tumors of laboratory animals. Their properties, mechanisms, and progression closely correlate with malignant CNS neoplasms in humans. These are potentially useful for identifying novel signaling pathways associated with oncogenesis in the nervous system and personalizing therapeutic approaches. In our work, we characterized a tissue-transplantable brain tumor strain of rat glioma101.8 using MRI, IHC, scRNA-seq, and qPCR-RT methods. According to this study, the cellular composition of the tissue-transplantable rat glioma 101.8 strain was determined, as well as the major genetic signature characteristics of each cell population of this tissue-transplantable strain and its microenvironment. Full article

Eikenella corrodens is a facultative anaerobic Gram-negative bacillus, part of the normal oropharyngeal flora, with opportunistic pathogenic potential particularly in immunocompromised hosts. The progression from localized intracranial infections such as cerebritis and subdural empyema to secondary bloodstream infection represents a rare but clinically significant pathway, especially in immuno-compromised patients. Here, we report a case of secondary E. corrodens bacteremia following left temporal cerebritis and ipsilateral subdural empyema in a 50-year-old man with advanced nasopharyngeal carcinoma treated with nivolumab. The patient presented neurological deficits and systemic inflammatory response, suggesting for a bacterial infection. Neuroimaging confirmed the intracranial infectious foci and blood cultures identified E. corrodens via MALDI-TOF MS and 16S rRNA gene sequencing. We discuss how cancer-associated immune dysregulation and immune checkpoint inhibition could modulate host susceptibility and clinical presentation of infection, potentially facilitating microbial dissemination across compromised blood–brain barriers. Additionally, we examine the cases of E. corrodens bacteremia secondary to CNS and head and neck infections. This case underscores the importance of heightened clinical vigilance for secondary bacteremia in oncologic patients with CNS infections and highlights the need for integrated microbiological and radiological assessment to optimize outcomes. Full article

Purpose: This systematic review aims to analyze the literature on the application of AI in predicting patient outcomes and treatment-related toxicity in those undergoing SBRT or SRS across heterogeneous tumor sites. Materials and methods: Our review conformed to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. PubMed, EMBASE and Scopus were systematically searched for English-language human studies evaluating AI for outcome and toxicity prediction in patients undergoing SBRT or SRS for solid tumors. Search terms included (“Stereotactic Body Radiotherapy” OR “SBRT” OR “Stereotactic Radiosurgery” OR “SRS” OR “Stereotactic Ablative Radiotherapy” OR “SABR”) AND (“Artificial Intelligence” OR “AI” OR “Machine Learning” OR “Deep Learning” OR “Radiomics”) AND (“Response Prediction” OR “Response to Treatment” OR “Outcome Prediction”) AND (“Toxicity” OR “Side Effects” OR “Treatment Toxicities” OR “Adverse Events”). Results: The search yielded 29 eligible retrospective studies, published between 2020 and 2025. Eight studies addressed early-stage primary lung cancer, highlighting the potential of AI-based models in predicting radiation-induced pneumonitis, fibrosis and local control. Five studies investigated AI models for predicting hepatobiliary toxicity following SBRT for liver tumors. Sixteen studies involved SRS-treated patients with brain metastases or benign intracranial neoplasms (e.g., arteriovenous malformations, vestibular schwannomas, meningiomas), exploring AI algorithms for predicting treatment response and radiation-induced changes. In the results, AI might have been exploited to both reaffirm already known clinical predictors and to identify novel imaging, dosimetric or biological biomarkers. Examples include predicting radiation pneumonitis in lung cancer, residual liver function in hepatic tumors and local recurrence in brain metastases, thus supporting tailored treatment decisions. Conclusions: Combining AI with SBRT could greatly enhance personalized cancer care by predicting patient-specific outcomes and toxicity. AI models analyze complex datasets, including imaging and clinical data, to identify patterns that traditional methods may miss, thus enabling more accurate risk stratification and reducing variability in treatment planning. With further research and clinical validation, this integration could make radiotherapy safer, more effective and contribute to advancement in precision oncology. Full article

Background/objectives: Treating pediatric brain tumors remains challenging due to the limitations of conventional therapies, which often damage healthy cells. Ketogenic diets (KDs)—high in fat and low in carbohydrates—have emerged as potential adjunct therapies by limiting glucose availability and offering ketones as an alternative energy source proposed to hinder tumor growth. However, due to limited awareness, there is hesitancy to recommend KDs. Methods: This study assessed oncologists’ knowledge and perceptions in Saudi Arabia regarding KD use in pediatric brain cancer patients. A cross-sectional survey was conducted with 94 oncologists from five major Riyadh healthcare centers, examining their knowledge, safety concerns, feasibility, and perceived efficacy of KDs. Results: Results showed that 67% correctly identified the basic composition of KDs, though 43% were neutral about its safety. Concerns about malnutrition and side effects were common and 53% found adherence to the diet challenging in pediatric patients. While 48.9% believed KDs could improve outcomes when combined with standard therapies, many stressed the importance of tailoring recommendations to individual medical conditions, including patient age, treatment stage, and overall nutritional status. Participants’ responses revealed variability in knowledge and perception levels regarding KDs, with consultants and internationally educated oncologists generally expressing more favorable views compared to fellows, who showed greater hesitancy. Conclusions: This study shows a mixed level of knowledge and perceptions among oncologists, reflecting a lack of consensus about KDs’ safety, feasibility, and potential benefits. These findings suggest the need for further education, clearer guidelines, and interdisciplinary collaboration to support informed decision-making, particularly in the local context. Full article

Neoplasms of the central nervous system (CNS) constitute a minor fraction of all malignant tumors. CNS accounts for approximately 4% of newly diagnosed oncological cases. Among primary CNS neoplasms, gliomas predominate, comprising nearly 90% of all malignant brain tumors, with Glioblastoma (GBM) representing the most prevalent and aggressive histological subtype. The earliest documented occurrences of GBM date back to the 19th century. Contemporary therapeutic modalities for GBM primarily involve maximal surgical resection, adjuvant radiotherapy, and systemic chemotherapy. However, the intrinsic heterogeneity of GBM poses a formidable obstacle to treatment efficacy. The immunosuppressive tumor microenvironment, coupled with the restrictive nature of the blood–brain barrier (BBB), significantly limits the intratumorally delivery of chemotherapeutic agents. The emergence of nanotechnology in the biomedical domain has been driven by the urgent need to develop more effective and targeted anticancer interventions. Optimizing therapeutic outcomes necessitates the concurrent application of multimodal strategies. This review emphasizes the Nano-Based Technology in GBM. Full article

Background: Ependymomas are primary CNS neoplasms that arise from the ependymal cells of the brain and spinal cord, accounting for 3–6% of all CNS tumors. Aims: This study provides a comprehensive analysis of ependymoma survival patterns and examines non-cancer causes of death in the US. Methods: This retrospective study used data from SEER 17 registries between 2000 and 2019 to evaluate the incidence of ependymoma, as well as the survival and mortality trends in the US. Results: A total of 3821 patients were included, with 842 (22%) deaths. The highest mortality was observed in younger patients (<18 years) within one year of diagnosis (SMR, 54.77; 95% CI, 38.95–74.88). Brain and other nervous system cancers were the leading causes of death, followed by non-cancer causes, particularly cerebrovascular diseases, pneumonia, influenza, and septicemia. The survival rates observed at one, three, and five years were 94% (95% CI: 0.94–0.95), 88% (95% CI: 0.87–0.89), and 84% (95% CI: 0.82–0.85), respectively. Conditional survival improved over time, with a three-year conditional relative survival rate of 92% after one year of diagnosis and 96% for those who survived five years. Conclusion: The death rate was highest among pediatric patients under 18 years of age. Cerebrovascular disorders were the leading non-cancer cause of death across all time intervals. The probability of surviving for three years increases for patients who have already survived one, three, or five years post-diagnosis. Full article

Background/Objectives: This study aimed to evaluate the diagnostic and prognostic utility of B7-H3 expression in differentiating low-grade gliomas (LGGs) from high-grade gliomas (HGGs) and to examine its association with clinical outcomes. Methods: This retrospective study included 99 patients with histopathologically confirmed gliomas (42 LGGs and 57 HGGs). B7-H3 expression was assessed using immunohistochemistry and scored by immunoreactive score (IRS). Results: B7-H3 expression was significantly higher in HGG compared to LGG (p < 0.001). The total IRS (B7-H3 A × B) demonstrated strong discriminative power (AUC = 0.816). High B7-H3 expression independently predicted disease progression (OR = 4.9, 95% CI: 2.4–10.1; p < 0.001) and was associated with IDH wild-type status and elevated Ki-67 index. Patients with high B7-H3 had significantly shorter overall survival (median 6 months vs. 42 months) and progression-free survival (median 3 months vs. 25 months) (both p < 0.001). Cox regression confirmed high B7-H3 as an independent predictor of mortality (HR = 2.9, 95% CI: 1.7–4.7; p < 0.001) and progression (HR = 2.6, 95% CI: 1.6–4.2; p < 0.001). Conclusions: B7-H3 expression is a reliable biomarker for distinguishing HGG from LGG and is independently associated with worse survival outcomes. Its assessment may aid in glioma classification and prognostication. 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