Glioblastoma (GBM) is the most aggressive glial tumor of the central nervous system. Despite intense scientific efforts, patients diagnosed with GBM and treated with the current standard of care have a median survival of only 15 months. Patients are initially treated by a neurosurgeon with the goal of maximal safe resection of the tumor. Obtaining tissue samples during surgery is indispensable for the diagnosis of GBM. Technological improvements, such as navigation systems and intraoperative monitoring, significantly advanced the possibility of safe gross tumor resection. Usually within six weeks after the surgery, concomitant radiotherapy and chemotherapy with temozolomide are initiated. However, current radiotherapy regimens are based on population-level studies and could also be improved. Implementing artificial intelligence in radiotherapy planning might be used to individualize treatment plans. Furthermore, detailed genetic and molecular markers of the tumor could provide patient-tailored immunochemotherapy. In this article, we review current standard of care and possibilities of personalizing these treatments. Additionally, we discuss novel individualized therapeutic options with encouraging results. Due to inherent heterogeneity of GBM, applying patient-tailored treatment could significantly prolong survival of these patients.

Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research.

Background: Cranioplasty is a routine neurosurgery treatment used to correct cranial vault abnormalities. Utilization of 3D printing technology in the field of cranioplasty involving the reconstruction of cranial defects emerged as an advanced possibility of anatomical reshaping. The transformative impact of patient-specific 3D printed implants, focuses on their remarkable accuracy, customization capabilities, and enhanced biocompatibility. Methods: The precise adaptation of implants to patient-specific anatomies, even in complex cases we presented, result in improved aesthetic outcomes and reduced surgical complications. The ability to create highly customized implants addresses the functional aspects of cranial defects and considers the psychological impact on patients. Results: By combining technological innovation with personalized patient care, 3D printed cranioplasty emerges as a transformative avenue in cranial reconstruction, ultimately redefining the standards of success in neurosurgery. Conclusion: 3D printing allows an excellent cranioplasty cosmesis achieved at a reasonable price without sacrificing patient outcomes. Wider implementation of this strategy can lead to significant healthcare cost savings.

Glioblastoma (GBM) is the most aggressive brain tumor, characterized by high recurrence rates and poor patient outcomes. Treatment failure is driven by multiple factors, including complex tumor heterogeneity, the presence of cancer stem cells, the immunosuppressive tumor microenvironment (TME), and many others. GBM’s heterogeneity underlines its ability to resist therapies and adapt to the TME. The TME, which is highly immunosuppressive and shaped by hypoxia, impairs anti-tumor immunity and limits the efficacy of immunotherapy. The blood–brain barrier (BBB) remains a major obstacle to delivering sufficient drug concentrations to the tumor by restricting the penetration of therapeutic agents. Another problem is the lack of reliable biomarkers to perform better patient stratification or even guide personalized treatments, resulting in generalized therapeutic approaches that do not adequately address GBM complexities. This review highlights the multifactorial nature of GBM treatment failure and highlights the need for a paradigm shift and innovative, personalized strategies. A deeper understanding of tumor biology and advances in translational research will be crucial to developing effective therapies and improving patient outcomes in this devastating disease.

Glioblastoma (GBM) is the most aggressive and common primary brain malignancy in adults, characterized by poor prognosis and treatment resistance. Despite advancements in treatment options, the median survival is roughly 15 months, underlining the need for novel and effective treatments. Artificial intelligence (AI) has emerged as a transformative technology in healthcare, offering outstanding capabilities in data analysis, pattern recognition, and helping in decision-making. This review explores the current and potential role of AI in GBM care, focusing on its applications in diagnosis, treatment planning, prognostication, and drug discovery. AI-based algorithms have demonstrated promising potential in enhancing diagnostics through imaging analysis, radiomics, and tumor segmentation. These technologies could enable non-invasive molecular profiling and early detection of GBM. In treatment planning, AI could improve approaches by optimizing surgical resection, radiotherapy regimen, and chemotherapy protocols. Furthermore, machine learning models can integrate multimodal data to develop personalized treatments. They can also enhance prognostication by predicting survival, recurrence, and treatment responses, helping clinicians to make more informed decisions. AI is also revolutionizing pharmacotherapy by identifying novel molecular targets and optimizing combination therapies. Despite notable progress, challenges persist. Limited data quality and quantity, algorithm interpretability, integration problems, and ethical considerations, remain significant challenges to clinical implementation. This review emphasizes the need for continued research and interdisciplinary collaboration to overcome many barriers and realize the transformative potential of AI in GBM care.

Gabriele-de Vries syndrome is a rare autosomal dominant genetic disease caused by de novo pathogenic variants in the Yin Yang 1 (YY1) gene. Individuals with this syndrome present with multiple congenital anomalies, as well as a delay in development and intellectual disability. Herein, we report the case of a newborn male patient with a novel de novo pathogenic variant in the Guanine Nucleotide-Binding Protein, Alpha Stimulating (GNAS) gene, which was identified by whole-exome sequencing. Our patient suffered from a large open spinal dysraphism which was treated surgically immediately after birth. During the follow-up, facial dysmorphism, bladder and bowel incontinence, and mildly delayed motor and speech development were observed. Congenital central nervous system disorders were also confirmed radiologically. In this case report, we present our diagnostic and treatment approaches to this patient. To our knowledge, this is the first reported case of Gabriele-de Vries syndrome presenting with spinal dysraphism. Extensive genetic evaluation is the cornerstone in treatment of patients with suspected Gabriele-de Vries syndrome. However, in cases with potentially life-threatening conditions, surgery should be strongly considered.