Pedro David Delgado-López

Recurrent malignant gliomas, particularly glioblastoma, remain among the most challenging entities in neuro-oncology, with nearly universal relapse and limited survival despite aggressive multimodal upfront treatment. Management at recurrence is heterogeneous and lacks a universally accepted standard of care, reflecting modest efficacy of available therapies and marked biological divergence between primary and recurrent tumors. This review provides a comprehensive and critical synthesis of current evidence supporting active salvage strategies, including repeat surgery, reirradiation, systemic therapies, and tumor-treating fields, highlighting indications, outcomes, and limitations. Special emphasis is placed on patient selection, extent of resection, modern radiotherapy techniques, and the role of molecularly refined approaches, which benefit only small, well-defined subgroups. Given the limited survival gains achievable with oncologic treatments, the review further addresses the central role of early integrated palliative care in recurrent malignant glioma, encompassing symptom control, home-based support, advance care planning, and end-of-life decision-making. Particular attention is paid to palliative (terminal) sedation and the ethical–legal framework surrounding euthanasia and medical aid in dying, with specific reference to the Spanish context. Overall, this article aims to contextualize therapeutic decision-making within an integrated, patient-centered model that balances potential oncologic benefit with quality of life, functional preservation, and respect for patient values.

Access to radiotherapy in Spain remains uneven, with rural and sparsely populated areas facing significant geographic, social, and economic barriers. Satellite radiotherapy units, conceived as extensions of central radiation oncology departments, have emerged as a practical solution to improve equity of access while maintaining safety and technical standards. This review examines the rationale, models, and implementation strategies of such units, with particular emphasis on the recent deployment in Soria (Castilla y León, Spain). We describe international experiences and Spanish case studies, analyzing their organizational, technological, and regulatory dimensions. The Soria project highlights both the potential benefits and the inherent challenges of decentralization: while improved treatment adherence, reduced patient burden, and decongestion of hub centers were evident, persistent obstacles include limited patient volume, difficulties in recruiting and retaining specialized staff, interoperability gaps between information systems, and ambiguities in governance structures. Lessons learned from this experience underline the critical importance of robust integration with the reference center, standardized clinical protocols, reliable digital connectivity, and a clear definition of responsibilities between institutions. Despite these barriers, satellite units represent a feasible and scalable strategy to address regional disparities in oncology care. Their long-term success depends on sustained institutional support, strategic workforce planning, and alignment with national policies aimed at ensuring equitable access to advanced cancer treatment.

We read with great interest the article by Giotta Lucifero and colleagues

Cerebrospinal fluid (CSF) leakage is a significant complication following planned or incidental durotomies during spinal surgery. While primary suturing remains the gold standard for dural closure, it is common to necessitate adjunctive reinforcement techniques. This study aims to evaluate the efficacy of a synthetic collagen-based sealant patch (TachoSil^®) in preventing CSF leaks in a large cohort of patients undergoing spinal surgery. A retrospective analysis was conducted in whom TachoSil^® was used as a dural sealant, either following planned (n = 12) or incidental (n = 42) durotomies, or prophylactically (n = 31) after decompression for critical lumbar canal stenosis. The primary endpoint was the prevention and resolution rate of CSF leaks. Secondary endpoints included reoperation rates and postoperative complications. A total of 85 patients (6.8%) met the inclusion criteria (mean age, 64.7 ± 15.4 years). TachoSil® was used as monotherapy in 39 patients and in combination with primary suturing or other sealants in 46 patients. Overall, 66 patients (77.6%) did not require surgical reintervention. TachoSil® monotherapy was associated with a significantly higher success rate (89.7%) compared to combined techniques (68.2% for TachoSil® plus suturing and 60.0% for TachoSil® plus other sealants; p = 0.041). Excluding its prophylactic use (either planned or incidental durotomy, n = 54), its efficacy in avoiding reintervention was 86.7% for TachoSil® alone, 77.3% for TachoSil® plus suture, and 64.7% for TachoSil® plus other combinations of sealants (p = 0.67). Postoperative complications occurred in 22 patients (25.9%), primarily including persistent CSF fistula, pseudomeningocele formation, and surgical site infections. TachoSil® demonstrated high efficacy as a standalone dural reinforcement method and when combined with suture or other sealant techniques in preventing CSF leaks following spinal surgeries involving dural compromise.

Background The rising life expectancy has led to an increased incidence of meningiomas among the elderly. In octogenarians, surgical decision-making remains particularly challenging due to frailty, comorbidities, and the risk of postoperative decline. This study investigates whether preoperative frailty indices and radiological features predict surgical outcomes in this high-risk population. Methods A multicenter retrospective cohort study was conducted across ten Spanish tertiary care centers, including 189 patients aged ≥ 80 years who underwent intracranial meningioma resection between 2010 and 2023. Preoperative variables included the 5-item Frailty Index (5-FI), Charlson Comorbidity Index (CCI), American Society of Anesthesiologists (ASA) classification, and tumor-specific imaging characteristics (tumor diameter, peritumoral edema, and venous sinus involvement). The primary endpoint was the occurrence of Unfavorable Outcome (any postoperative neurological deficit, 30-day mortality, or major complication). Secondary endpoints included variation of KPS score at 1 year, discharge disposition, and 1-year mortality. Both univariate and multivariate logistic regression models were applied. Results Mean patient age was 83.0 ± 2.6 years, with 58.2% females. In univariate analysis, preoperative KPS < 70, higher WHO grade, and larger tumor diameter were associated with an unfavorable outcome. However, multivariate analysis identified only preoperative KPS < 70 as an independent predictor of worse outcome (OR 3.10, 95% CI 1.44–6.68, p = 0.004). At 12 months, functional status declined significantly (mean KPS from 73.5 to 63.5; p < 0.001), although 27.1% of patients remained functionally independent. New neurological deficits occurred in 23.8%, postoperative complications in 42.8%, and 30-day mortality was 4.2%. One-year mortality reached 15.8%. Predictors of 12-month KPS < 70 included higher age, preoperative KPS < 70, higher 5-FI, higher ASA grade, higher WHO grade, and the presence of major postoperative complications or new neurological deficits. In multivariate analysis, preoperative KPS < 70 (OR 14.45, 95% CI 5.64–37.03, p < 0.001) and the occurrence of new neurological deficits (OR 4.79, 95% CI 1.59–14.38, p = 0.005) were independent predictors of 12-month KPS < 70. Conclusions In octogenarians undergoing meningioma surgery, frailty indices—especially low preoperative KPS—and tumor-related characteristics are stronger predictors of surgical outcomes than age alone. Incorporating individualized assessments of physiological reserve and tumor burden may improve surgical planning and preoperative counseling in this growing patient population.

Artificial Intelligence (AI) is transforming neuro-oncology by enhancing diagnosis, treatment planning, and prognosis prediction. AI-driven approaches-such as CNNs and deep learning-have improved the detection and classification of brain tumors through advanced imaging techniques and genomic analysis. Explainable AI methods mitigate the "black box" problem, promoting model transparency and clinical trust. Mechanistic models complement AI by integrating biological principles, enabling precise tumor growth predictions and treatment response assessments. AI applications also include the creation of digital twins for personalized therapy optimization, virtual clinical trials, and predictive modeling for estimation of tumor resection and pattern of recurrence. However, challenges such as data bias, ethical concerns, and regulatory compliance persist. The European Artificial Intelligence Act and the Health Data Space Regulation impose strict data protection and transparency requirements. This review explores AI's methodological foundations, clinical applications, and ethical challenges in neuro-oncology, emphasizing the need for interdisciplinary collaboration and regulatory adaptation.