Object: To investigate those parameters affecting early and follow-up functional outcomes in patients undergoing resection of meningiomas and to design a dedicated predictive score, the Milan Bio(metric)-Surgical Score (MBSS) is hereby presented. Methods: Patients undergoing transcranial surgery for intracranial meningiomas were included. The most significant parameters in the regression analyses were implemented in a patient stratification score and were validated by testing its classification consistency with a clinical–radiological grading scale (CRGS), Milan complexity scale (MCS), and Charlson Comorbidity Index (CCI) scores. Results: The ASA score, Frailty index, skull base and posterior cranial fossa locations, a diameter of >25 mm, and the absence of a brain–tumour interface were predictive of early post-operative deterioration and were collected in MBSS Part A (AUC: 0.965; 95%C.I. 0.890–1.022), while the frailty index, posterior cranial fossa location, a diameter of >25 mm, a edema/tumour volume index of >2, dural sinus invasion, DWI hyperintensity, and the absence of a brain–tumour interface were predictive of a long-term unfavourable outcome and were collected in MBSS Part B (AUC: 0.877; 95%C.I. 0.811–0.942). The score was consistent with CRGS, MCS, and CCI. Conclusion: Patients’ multi-domain evaluation and the implementation of frailty indexes might help predict the perioperative complexity of cases; the functional, clinical, and neurological early outcomes; survival; and overall QoL after surgery.

Background and Objective: To analyze the effects of several drug for pain prevention in adults undergoing craniotomy for elective brain surgery. Material and Methods: A systematic review and meta-analysis were conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. The inclusion criteria were limited to randomized controlled trials (RCTs) that evaluated the effectiveness of pharmacological treatments for preventing post-operative pain in adults (aged 18 years or older) undergoing craniotomies. The main outcome measures were represented by the mean differences in validated pain intensity scales administered at 6 h, 12 h, 24 h and 48 h post-operatively. The pooled estimates were calculated using random forest models. The risk of bias was evaluated using the RoB2 revised tool, and the certainty of evidence was assessed according to the GRADE guidelines. Results: In total, 3359 records were identified through databases and registers’ searching. After study selection, 29 studies and 2376 patients were included in the meta-analysis. The overall risk of bias was low in 78.5% of the studies included. The pooled estimates of the following drug classes were provided: NSAIDs, acetaminophen, local anesthetics and steroids for scalp infiltration and scalp block, gabapentinoids and agonists of adrenal receptors. Conclusions: High-certainty evidence suggests that NSAIDs and acetaminophen may have a moderate effect on reducing post-craniotomy pain 24 h after surgery compared to control and that ropivacaine scalp block may have a bigger impact on reducing post-craniotomy pain 6 h after surgery compared to control. Moderate-certainty evidence indicates that NSAIDs may have a more remarkable effect on reducing post-craniotomy pain 12 h after surgery compared to control. No moderate-to-high-certainty evidence indicates effective treatments for post-craniotomy pain prevention 48 h after surgery.

(1) Background: Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) represents a diagnostic and therapeutic challenge in neurosurgical practice, expanding the burden of care and exposing patients to additional risks related to further invasive procedures and treatment delays. In addition, atypical cases and overlapping features have not been entirely addressed by modern diagnostic research. The aim of this study was to validate a previously designed and internally validated ResNet101 deep learning model to differentiate glioblastomas, PCNSLs and BMs. (2) Methods: We enrolled 126 patients (glioblastoma: n = 64; PCNSL: n = 27; BM: n = 35) with preoperative T1Gd-MRI scans and histopathological confirmation. Each lesion was segmented, and all regions of interest were exported in a DICOM dataset. A pre-trained ResNet101 deep neural network model implemented in a previous work on 121 patients was externally validated on the current cohort to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans. (3) Results: The model achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.73; 95%CI: 0.62–0.85), glioblastomas (AUC: 0.78; 95%CI: 0.71–0.87) and moderate to low ability in differentiating BMs (AUC: 0.63; 95%CI: 0.52–0.76). The performance of expert neuro-radiologists on conventional plus advanced MR imaging, assessed by retrospectively reviewing the diagnostic reports of the selected cohort of patients, was found superior in accuracy for BMs (89.69%) and not inferior for PCNSL (82.90%) and glioblastomas (84.09%). (4) Conclusions: We investigated whether the previously published deep learning model was generalizable to an external population recruited at a different institution—this validation confirmed the consistency of the model and laid the groundwork for future clinical applications in brain tumour classification. This artificial intelligence-based model might represent a valuable educational resource and, if largely replicated on prospective data, help physicians differentiate glioblastomas, PCNSL and solitary BMs, especially in settings with limited resources.