Glioblastoma (GB) is the most common and fatal primary central nervous system malignancy in adults. Various immunotherapies, including vaccination, are under investigation for their potential to extend survival in GB patients. Vaccination therapy has shown variable but promising outcomes across studies. This meta-analysis aims to evaluate the efficacy of available vaccines for newly diagnosed and recurrent GB. We conducted a systematic search in PubMed, Scopus, and Web of Science to identify randomized or non-randomized double-arm studies involving adult GB patients treated with vaccines. Overall survival (OS) and progression-free survival (PFS) were the primary outcomes, with effect sizes represented as hazard ratios (HR) and calculated using a random-effects model. Funnel plots, Egger’s and Begg-Mazumdar tests assessed publication bias, and the chi-square (Q) statistic, I2 statistic, and tau-squared (T2) parameter addressed heterogeneity. A total of 2,792 patients from 23 clinical studies were included. Our findings showed significant improvements in PFS (HR, 0.64; p < 0.001) and OS (HR, 1.09; p < 0.00001) with minimal publication bias but notable heterogeneity. Meta-regression identified vaccine type and publication year as influential factors. Subgroup analysis demonstrated survival benefits with dendritic cell and viral vector vaccines, with a trend towards lower 6-methylguanine-DNA methyltransferase (MGMT) methylation rates. Sensitivity analysis confirmed the robustness of our results. Vaccination therapy showed potential survival benefits for GB patients; however, further phase III studies are needed to validate these results, elucidate biological mechanisms, and strive for improved trial designs and patient stratification.

Background: Surgical site infections (SSIs) remain a serious problem following abdominal surgery due to gynecologic malignancies leading to increased hospitalization, high costs, and delays in adjuvant treatments; thus, SSIs affect overall survival. The aim of the present meta-analysis was to investigate the impact of closed incision–negative pressure wound treatment (ci-NPWT) systems on postoperative surgical site occurrences (SSOs) after gynecologic oncology surgery. Methods: The present meta-analysis was designed using the PRISMA guidelines. A search in several databases was conducted from inception until March 2025. Results: Overall, five studies were included; these studies enrolled 1174 patients in total, where 412 were treated with ci-NPWT systems and 762 were treated with conventional gauze. Patients treated with ci-NPWT systems presented with lower SSI rates (OR 0.40, 95% CI 0.15–1.10, p = 0.08), lower fascial dehiscence rates (OR 0.72, 95% CI 0.21–2.42, p = 0.59), and lower seroma formation rates (OR 0.70, 95% CI 0.25–1.93, p = 0.49), although statistical significance was not reached in all comparisons. On the other hand, patients treated with ci-NPWT systems also presented with higher postoperative hematoma formation rates (OR 1.38, 95% CI 0.32–5.99, p = 0.66), although statistical significance was not reached. Preoperative patient characteristics, operative parameters, and cancer characteristics were similar among the two study groups. Conclusions: The prophylactic use of ci-NPWT systems showed promising results in reducing postoperative SSOs after gynecologic cancer surgery. Nevertheless, prospectively designed studies are needed in the future to reach robust evidence that would enable the wide implementation of such devices in routine clinical practice.

Brain tumor management is one of the most challenging domains in neurosurgery, where timely diagnosis and intervention are critical, yet differentiating between diagnoses often presents difficulties. Despite advances, treatment options, especially for malignant tumors, have largely remained unchanged. Artificial Intelligence (AI) is increasingly being explored in neurosurgery, with its automation, data analysis capabilities, pattern recognition, and ability to detect previously imperceptible features offering potential for enhanced clinical management. This study provides significant insights for biomedical research by identifying promising algorithms for standardization in model healthcare settings and underscoring the interdisciplinary collaboration needed for their successful integration. A comprehensive search in PubMed, Scopus, and Web of Science yielded 4,362 results, of which 58 studies met the inclusion criteria for qualitative analysis. Studies were categorized by five clinical management domains: (1) Tumor Diagnosis, Imaging, Grading, (2) Surgical Planning and Intra-Operative Decision Making (3) Decision-Making AI in Prognostication and Follow-Up, and (4) AI in Histopathological Classification. Data extraction included study characteristics, metrics of model’s efficacy, and study limitations. The most studied domain was Prognostication and Follow-Up, with 20 studies. Most algorithms utilized institutional or publicly available datasets which were retrospectively evaluated. Various models yielded promising results in achieving their designated task. However, standardized validation methods and large, balanced databases are needed to ensure reliability and generalizability. Literature limitations and ethical considerations should also be addressed.

Background: Artificial intelligence (AI) and large language models (LLMs) may make surgical information easier to access and understand. However, these tools cannot replace the clinician’s role in shared decision-making or the requirements for valid informed consent. Methods: This narrative review follows the surgical patient pathway from diagnosis and explanation to option comparison, shared decision-making, and informed consent. It examines conventional digital tools, LLM-assisted communication, personalized prediction, direct consent studies, patient and professional perspectives and legal, ethical, and governance issues. A formal meta-analysis and study-level risk-of-bias assessment were not performed. Results: AI may support several stages of surgical care. Diagnostic and predictive systems can assist clinicians. LLMs can help explain approved information, prepare patients for consultation, and improve consent materials. Conventional interactive tools have the strongest evidence for better comprehension. Evidence for LLM-assisted consent is still based mainly on simulations and document analyses. Patient-facing use requires source control, privacy protection, and visible clinical oversight. AI cannot establish capacity, voluntariness, or final authorization. We propose a five-level framework in which safeguards increase with clinical influence. Conclusions: AI may support the patient’s path from diagnosis to an informed surgical choice when it uses approved information, serves a defined task, and remains under clinical oversight. Current evidence does not support autonomous AI-mediated consent or AI-directed treatment choice. The aim should be a better-informed, preference-sensitive decision, not an automated signature.