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Profound immune dysregulation and impaired response to the SARS-CoV-2 vaccine put patients with chronic lymphocytic leukemia (CLL) at risk of severe COVID-19. We compared humoral memory and T-cell responses after booster dose vaccination or breakthrough infection. (Green) Quantitative determination of anti-Spike specific antibodies. Booster doses increased seroconversion rate and antibody titers in all patient categories, ultimately generating humoral responses similar to those observed in the postinfection cohort. In detail, humoral response with overscale median antibody titers arose in >80% of patients in watch and wait, off-therapy in remission, or under treatment with venetoclax single-agent. Anti-CD20 antibodies and active treatment with BTK inhibitors (BTKi) represent limiting factors of humoral response, still memory mounted in ~40% of cases following booster doses or infection. (Blue) Evaluation of SARS-CoV-2-specific T-cell responses. Number of T-cell functional activation markers documented in each patient. The vast majority of patients, including those seronegative, developed T-cell responses, qualitatively similar between treatment groups or between vaccination alone and infection cases. These data highlight the efficacy of booster doses in eliciting T-cell immunity independently of treatment status and support the use of additional vaccination boosters to stimulate humoral immunity in patients on active CLL-directed treatments., The project was supported in part by Fondazione Veronesi (ID 1852164), Janssen (IBR-I-20-EMEA-014-V01/54179060CLL4024; NOPRODCLL4001), and MH CZ—DRO (FNBr, 65269705).

Background: Dementia affects more than 55 million people worldwide. Several technologies have been developed to slow cognitive decline: deep brain stimulation (DBS) of network targets in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) have been recently investigated. Objective: This study aimed to review the characteristics of the populations, protocols, and outcomes of patients with dementia enrolled in clinical trials investigating the feasibility and efficacy of DBS. Materials and methods: A systematic search of all registered RCTs was performed on Clinicaltrials.gov and EudraCT, while a systematic literature review was conducted on PubMed, Scopus, Cochrane, and APA PsycInfo to identify published trials. Results: The literature search yielded 2122 records, and the clinical trial search 15 records. Overall, 17 studies were included. Two of 17 studies were open-label studies reporting no NCT/EUCT code and were analysed separately. Of 12 studies investigating the role of DBS in AD, we included 5 published RCTs, 2 unregistered open-label (OL) studies, 3 recruiting studies, and 2 unpublished trials with no evidence of completion. The overall risk of bias was assessed as moderate-high. Our review showed significant heterogeneity in the recruited populations regarding age, disease severity, informed consent availability, inclusion, and exclusion criteria. Notably, the standard mean of overall severe adverse events was moderately high (SAEs: 9.10 ± 7.10%). Conclusion: The population investigated is small and heterogeneous, published results from clinical trials are under-represented, severe adverse events not negligible, and cognitive outcomes uncertain. Overall, the validity of these studies requires confirmation based on forthcoming higher-quality clinical trials.

Glioblastoma is the most common primitive tumor in adult central nervous system (CNS), classified as grade IV according to WHO 2016 classification. Glioblastoma shows a poor prognosis with an average survival of approximately 15 months, representing an extreme therapeutic challenge. One of its distinctive and aggressive features is aberrant angiogenesis, which drives tumor neovas-cularization, representing a promising candidate for molecular target therapy. Although several pre-clinical studies and clinical trials have shown promising results, anti-angiogenic drugs have not led to a significant improvement in overall survival (OS), suggesting the necessity of identifying novel therapeutic strategies. Metformin, an anti-hyperglycemic drug of the Biguanides family, used as first line treatment in Type 2 Diabetes Mellitus (T2DM), has demonstrated in vitro and in vivo antitumoral efficacy in many different tumors, including glioblastoma. From this evidence, a process of repurposing of the drug has begun, leading to the demonstration of inhibition of various onco-promoter mechanisms and, consequently, to the identification of the molecular pathways involved. Here, we review and discuss metformin’s potential antitumoral effects on glioblastoma, inspecting if it could properly act as an anti-angiogenic compound to be considered as a safely add-on therapy in the treatment and management of glioblastoma patients.

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.

Diffusion tensor imaging (DTI) allows visualization of the main white matter tracts while intraoperative neurophysiological monitoring (IONM) represents the gold standard for surgical resection of gliomas. In recent years, the use of small craniotomies has gained popularity thanks to neuronavigation and to the low morbidity rates associated with shorter surgical procedures. The aim of this study was to review a series of patients operated for glioma using DTI, IONM, and tumor-targeted craniotomies. The retrospective analysis included patients with supratentorial glioma who met the following inclusion criteria: preoperative DTI, intraoperative IONM, tumor-targeted craniotomy, pre- and postoperative MRI, and complete clinical charts. The DTI was performed on a 3T scanner. The IONM included electroencephalography (EEG), transcranial (TC) and/or cortical motor-evoked potentials (MEP), electrocorticography (ECoG), and direct electrical stimulation (DES). Outcomes included postoperative neurological deficits, volumetric extent of resection (EOR), and overall survival (OS). One hundred and three patients (61 men, 42 women; mean age 54 ± 14 years) were included and presented the following WHO histologies: 65 grade IV, 19 grade III, and 19 grade II gliomas. After 3 months, only three patients had new neurological deficits. The median postoperative volume was 0cc (IQR 3). The median OS for grade IV gliomas was 15 months, while for low-grade gliomas it was not reached. In our experience, a small craniotomy and a tumor resection supported by IONM and DTI permitted to achieve satisfactory results in terms of neurological outcomes, EOR, and OS for glioma patients.

Objective: To characterize patients with acute ischemic stroke related to SARS-CoV-2 infection and assess the classification performance of clinical and laboratory parameters in predicting in-hospital outcome of these patients. Methods: In the setting of the STROKOVID study including patients with acute ischemic stroke consecutively admitted to the ten hub hospitals in Lombardy, Italy, between March 8 and April 30, 2020, we compared clinical features of patients with confirmed infection and non-infected patients by logistic regression models and survival analysis. Then, we trained and tested a random forest (RF) binary classifier for the prediction of in-hospital death among patients with COVID-19. Results: Among 1013 patients, 160 (15.8%) had SARS-CoV-2 infection. Male sex (OR 1.53; 95% CI 1.06–2.27) and atrial fibrillation (OR 1.60; 95% CI 1.05–2.43) were independently associated with COVID-19 status. Patients with COVID-19 had increased stroke severity at admission [median NIHSS score, 9 (25th to75th percentile, 13) vs 6 (25th to75th percentile, 9)] and increased risk of in-hospital death (38.1% deaths vs 7.2%; HR 3.30; 95% CI 2.17–5.02). The RF model based on six clinical and laboratory parameters exhibited high cross-validated classification accuracy (0.86) and precision (0.87), good recall (0.72) and F1-score (0.79) in predicting in-hospital death. Conclusions: Ischemic strokes in COVID-19 patients have distinctive risk factor profile and etiology, increased clinical severity and higher in-hospital mortality rate compared to non-COVID-19 patients. A simple model based on clinical and routine laboratory parameters may be useful in identifying ischemic stroke patients with SARS-CoV-2 infection who are unlikely to survive the acute phase.

Introduction: Posterior fossa syndrome (PFS) is a set of debilitating complications that can occur after surgery for posterior fossa tumors. This study aimed to assess the preoperative radiological and surgical risk factors for the onset of PFS in a histologically homogeneous population of children with medulloblastoma and compare it to a similar population of young adults. Methods: Included patients underwent posterior fossa surgery for medulloblastoma at 11 Italian neurosurgical wards (2003–2019) and were referred to Fondazione IRCCS Istituto Nazionale dei Tumori in Milan (INT) for postoperative treatments. We collected patients’ pre- and post-operative clinical, surgical and radiological data from the INT charts. To compare the distribution of variables, we used the Mann–Whitney and Fisher tests for continuous and categorical variables, respectively. Results: 136 patients (109 children and 27 young adults) were included in the study. Among children, 29 (27%) developed PFS, and all of them had tumors at midline site with invasion of the fourth ventricle. Radiological evidence of involvement of the right superior (39% versus 12%; p = 0.011) or middle cerebellar peduncles (52% versus 18%; p = 0.002) seemed more common in children who developed PFS. Young adults showed an expected lower incidence of PFS (4 out of 27; 15%), that may be due to anatomical, physiological and oncological elements. Conclusions: This study confirmed some factors known to be associated with PFS onset and shed light on other debated issues. Our findings enhance an already hypothesized role of cerebellar language lateralization. The analysis of a population of young adults may shed more light on the often-neglected existence of PFS in non-pediatric patients.