A Frailty-Adjusted Stratification Score to Predict Surgical Risk, Post-Operative, Long-Term Functional Outcome, and Quality of Life after Surgery in Intracranial Meningiomas.

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.

A Deep Learning Model for Preoperative Differentiation of Glioblastoma, Brain Metastasis and Primary Central Nervous System Lymphoma: A Pilot Study.

Background: Neuroimaging differentiation of glioblastoma, primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (BM) remains challenging in specific cases showing similar appearances or atypical features. Overall, advanced MRI protocols have high diagnostic reliability, but their limited worldwide availability, coupled with the overlapping of specific neuroimaging features among tumor subgroups, represent significant drawbacks and entail disparities in the planning and management of these oncological patients. Objective: To evaluate the classification performance metrics of a deep learning algorithm trained on T1-weighted gadolinium-enhanced (T1Gd) MRI scans of glioblastomas, atypical PCNSLs and BMs. Materials and Methods: We enrolled 121 patients (glioblastoma: n=47; PCNSL: n=37; BM: n=37) who had undergone preoperative T1Gd-MRI and histopathological confirmation. Each lesion was segmented, and all ROIs were exported in a DICOM dataset. The patient cohort was then split in a training and hold-out test sets following a 70/30 ratio. A Resnet101 model, a deep neural network (DNN), was trained on the training set and validated on the hold-out test set to differentiate glioblastomas, PCNSLs and BMs on T1Gd-MRI scans. Results: The DNN achieved optimal classification performance in distinguishing PCNSLs (AUC: 0.98; 95%CI: 0.95 - 1.00) and glioblastomas (AUC: 0.90; 95%CI: 0.81 - 0.97) and moderate ability in differentiating BMs (AUC: 0.81; 95%CI: 0.70 - 0.95). This performance may allow clinicians to correctly identify patients eligible for lesion biopsy or surgical resection. Conclusion: We trained and internally validated a deep learning model able to reliably differentiate ambiguous cases of PCNSLs, glioblastoma and BMs by means of T1Gd-MRI. The proposed predictive model may provide a low-cost, easily-accessible and high-speed decision-making support for eligibility to diagnostic brain biopsy or maximal tumor resection in atypical cases.

Does COVID-19 Affect Survival and Functional Outcome in Emergency and Urgent Neurosurgical Procedures? A Single-Center Prospective Experience During the Pandemic.

OBJECTIVE: To assess organizational and technical difficulties of neurosurgical procedures during the coronavirus disease 2019 (COVID-19) pandemic and their possible impact on survival and functional outcome and to evaluate virological exposure risk of medical personnel. METHODS: Data for all urgent surgical procedures performed in the COVID-19 operating room were prospectively collected. Preoperative and postoperative variables included demographics, pathology, Karnofsky performance status (KPS) and neurological status at admission, type and duration of surgical procedures, length of stay, postoperative KPS and functional outcome comparison, and destination at discharge. We defined 5 classes of pathologies (traumatic, oncological, vascular, infection, hydrocephalus) and 4 surgical categories (burr hole, craniotomy, cerebrospinal fluid shunting, spine surgery). Postoperative SARS-CoV-2 infection was checked in all the operators. RESULTS: We identified 11 traumatic cases (44%), 4 infections (16%), 6 vascular events (24%), 2 hydrocephalus conditions (8%), and 2 oncological cases (8%). Surgical procedures included 11 burr holes (44%), 7 craniotomies (28%), 6 cerebrospinal fluid shunts (24%), and 1 spine surgery (4%). Mean patient age was 57.8 years. The most frequent clinical presentation was coma (44 cases). Mean KPS score at admission was 20 ± 10, mean surgery duration was 85 ± 63 minutes, and mean length of stay was 27 ± 12 days. Mean KPS score at discharge was 35 ± 25. Outcome comparison showed improvement in 16 patients. Four patients died. Mean follow-up was 6 ± 3 months. None of the operators developed postoperative SARS-CoV-2 infection. CONCLUSIONS: Standardized protocols are mandatory to guarantee a high standard of care for emergency and urgent surgeries during the COVID-19 pandemic. Personal protective equipment affects maneuverability, dexterity, and duration of interventions without affecting survival and functional outcome.