Glioblastoma (GBM) remains one of the most aggressive and treatment-resistant brain tumors, necessitating novel therapeutic approaches. Oncolytic treatments, particularly oncolytic viruses (OVs), have emerged as promising candidates by selectively infecting and lysing tumor cells while stimulating anti-tumor immunity. Various virus-based therapies are under investigation, including genetically engineered herpes simplex virus (HSV), adenovirus, poliovirus, reovirus, vaccinia virus, measles virus, and Newcastle disease virus, each exploiting unique tumor-selective mechanisms. While some, such as HSV-based therapies including G207 and Delytact^TM, have demonstrated clinical progress, significant challenges persist, including immune evasion, heterogeneity in patient response, and delivery barriers due to the blood–brain barrier. Moreover, combination strategies integrating OVs with immune checkpoint inhibitors, chemotherapy, and radiation are promising but require further clinical validation. Non-viral oncolytic approaches, such as tumor-targeting bacteria and synthetic peptides, remain underexplored. This review highlights current advancements while addressing critical gaps in the literature, including the need for optimized delivery methods, better biomarker-based patient stratification, and a deeper understanding of GBM’s immunosuppressive microenvironment. Future research should focus on enhancing OV specificity, engineering viruses to deliver therapeutic genes, and integrating OVs with precision medicine strategies. By identifying these gaps, this review provides a framework for advancing oncolytic therapies in GBM treatment.

Background/Objectives: Artificial intelligence (AI) has rapidly expanded across medical imaging with proposed applications in diagnosis, prognostication, and surgical planning. Concerns remain regarding methodological robustness and clinical readiness for many published models. This systematic review aimed to conduct a methodological audit of AI imaging studies relevant to contemporary neurosurgical practice—including intracranial, cerebrovascular, spinal, and connectomics-based applications—published in 2025. Methods: Following PRISMA guidelines and PROSPERO registration (CRD420261284068), PubMed was searched for studies published in 2025 evaluating machine learning or deep learning applications in MRI- or CT-based imaging. Three reviewers independently extracted data on validation strategy, data leakage risk, human comparator use, calibration reporting, and CLAIM/TRIPOD-AI adherence. Risk of bias was assessed using PROBAST+AI. Results: Of 1776 screened records, 91 studies met the inclusion criteria. China led contributions (54.9%), oncology was the most common domain (37.4%), and MRI was the predominant modality (67.0%). External validation was reported in 75.8% of studies, and 66.0% used multicenter cohorts. Data leakage risk was low in 93.4%. However, only 18.7% included human comparators, calibration was reported in 30.8%, and none achieved full CLAIM/TRIPOD-AI compliance. Conclusions: AI imaging studies published in 2025 demonstrate encouraging progress in multicenter design and external validation. However, persistent gaps in human benchmarking, calibration, and reporting suggest further methodological development is needed.

Intracranial epidermoid tumors (ETs) are rare, benign lesions that often arise within the cerebellopontine angle and are typically slow-growing with favorable outcomes. Malignant transformation is exceedingly rare and poorly understood, with limited data on imaging findings, medical and operative management, and overall survival. We conducted a PRISMA-guided systematic review and pooled analysis of 68 patients from 58 studies with pathologically confirmed malignant transformation of intracranial ETs. Clinical presentation, tumor location, radiologic evolution, management, histopathology, molecular findings, and survival were analyzed. The Kaplan-Meier method was used for survival analysis. Mean age at diagnosis was 49 years with a slight male predominance. Most tumors arose in the cerebellopontine angle (97%). Malignant transformation was associated with contrast enhancement (98%) and rapid growth (57%). Longitudinal imaging revealed heterogeneous T2 signal changes (56%) and loss of diffusion restriction on diffusion-weighted imaging (43%), termed "Loss of Fluidity" and "Flip Sign." Gross total resection was achieved in 24%; subtotal resection in 66%, often supplemented with radiotherapy (46%) and chemotherapy (21%). Median overall survival was 23 months, with 58% mortality within six months and recurrence in 63%. Immunohistochemistry confirmed squamous differentiation with high Ki-67 (mean 41%). Malignant transformation of intracranial ETs is rare but highly aggressive. Early recognition of clinical deterioration and identifying characteristic imaging changes may enable prompt detection. Maximal safe resection remains the cornerstone of management, with multimodal approaches potentially improving outcomes. Long-term surveillance and molecular studies are essential to guide prognosis and targeted treatment.