Antonio Delacy Martini Vial

Background: Glioblastoma (GBM) represents the most aggressive and lethal primary malignancy of the central nervous system in adults. Although clinical trials evaluating immunotherapy strategies have increased substantially in recent years, the available evidence remains highly fragmented. This scoping review aimed to synthesize the available evidence from clinical trials investigating immunotherapy for newly diagnosed GBM. Methods: PubMed, Scopus, Embase, and Web of Science were searched from inception to July 2026 for clinical trials of first-line immunotherapy in adults with newly diagnosed GBM; recurrent, mixed-setting, preclinical, and abstract-only reports were excluded. Outcomes were overall survival (OS) and progression-free survival (PFS), adverse events, and treatment-related deaths. Results: Six therapeutic classes were represented: immune checkpoint inhibitors (13 reports), peptide and neoantigen vaccines (12), dendritic cell vaccines (10), adoptive cell therapies (5), oncolytic viral and gene therapies (3), and autologous formalin-fixed tumor vaccines (2). The evidence base was predominantly early-phase: 36 of 45 studies enrolled fewer than 100 patients, only 14 included a randomized comparison, and 5 were phase III. Median OS ranged from 11.5 to 38.4 months and median PFS from 1.3 to 60.1 months, the highest estimates arising from small, highly selected single-arm cohorts. No randomized trial improved OS. Important adverse events were rare and almost exclusively confined to checkpoint inhibitor trials. Conclusions: Immunotherapy in newly diagnosed GBM is feasible and tolerable but has not improved survival, with outcomes still determined more by MGMT promoter status and extent of resection than by treatment assignment. Biomarker-enriched selection, lymphocyte-sparing design and rational combinations are required in future trials.

Background: Neurosurgery depends heavily on implanted biomaterials, and the endovascular treatment of intracranial aneurysms (IAs) is the paradigmatic case, since coils, flow diverters and intrasaccular devices achieve durable occlusion only through intrasaccular thrombus organization and endothelial coverage of the neck, both governed by the local flow environment. Computational fluid dynamics (CFD) resolves that environment but is too slow and too solver-dependent for clinical or device design use. Graph and geometric deep learning operates natively on the unstructured meshes in which vascular anatomy and implanted scaffolds are represented and has been proposed as the technology that would close this gap. Methods: Four databases were searched up to 29 July 2026 for original studies developing or validating graph-based or geometric deep learning applied to IA geometry, with a hemodynamic or clinical outcome. Screening and extraction were performed in duplicate; risk of bias with PROBAST and artificial intelligence signaling items, reporting with TRIPOD+AI, and synthesis followed SWiM. Results: Twelve studies (2021 to 2026) were included: seven clinical (1965 aneurysms, plus 81 externally) and five computational (up to 984 geometries). Learned geometric representations discriminated rupture, growth and post-embolization recanalization better than morphological indices and then PHASES, reaching an area under the curve of 0.795 to 0.97 internally. Graph and point cloud surrogates reproduced hemodynamic fields with normalized errors of 2% to 5% in seconds rather than hours, and one transformer-based graph network predicted the extent and timing of intra-aneurysmal thrombus formation over a reactive surface. External validation was reached by two studies, and clinical utility was achieved in one; discrimination fell from 0.85 to 0.71 in one external test, and surrogate error rose from 4.1% to 19.1% on patient-derived anatomy. No model was trained on a device-laden geometry. Additionally, 5/7 clinical studies classified cross-sectional rupture status rather than prospectively predicting future rupture. Conclusions: Graph and geometric deep learning has substantially reduced the computational burden of hemodynamic analysis, but reliable generalization to unseen patient-specific anatomy remains a relevant challenge. Shared patient-specific benchmarks, prespecified external validation, reporting of calibration, and extension of training corpora to implanted anatomy are necessary next steps towards virtual evaluation of neurovascular biomaterials.