BackgroundNeuroimaging 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.ObjectiveTo 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 MethodsWe 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.ResultsThe 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.ConclusionWe 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.
Background: Glioblastoma is the most aggressive primary brain malignancy in adults, with a poor prognosis of about 14 months. Recent evidence ascribed to metformin (MET), an antihyperglycemic drug, the potential to reduce cancer incidence and progression, but the molecular mechanisms underlying these effects need to be better investigated. Methods: Here, we tested the efficacy of MET on n = 10 primary glioblastoma endothelial cells (GECs), by viability and proliferation tests, as MTT and Live/Dead assays, apoptosis tests, as annexin V assay and caspase 3/7 activity, functional tests as tube-like structure formation and migration assay and by mRNA and protein expression performed by quantitative real-time PCR analysis (qRT-PCR) and Western Blot, respectively. Results: Data resulting revealed a time- and μ-dependent ability of MET to decrease cell viability and proliferation, increasing pro-apoptotic mechanisms mediated by caspases 3/7. Also, MET impacted GEC functionality with a significant decrease of angiogenesis and invasiveness potential. Mechanistically, MET was able to interfere with sphingolipid metabolism, weakening the oncopromoter signaling promoted by sphingosine-1-phosphate (S1P) and shifting the balance toward the production of the pro-apoptotic ceramide. Conclusions: These observations ascribed to MET the potential to serve as add-on therapy against glioblastoma, suggesting a repurposing of an old, totally safe and tolerable drug for novel oncology therapeutics.
Traditionally, medical care and research in Parkinson’s disease (PD) have been conducted through in-person visit. The recent Coronavirus Disease 2019 (COVID-19) pandemic has profoundly impacted the delivery of in-person clinical care. We conducted an online survey to investigate the impact of COVID-19 on access to telehealth care, interviewing both PD patients and neurologists. Survey responses were collected from 1 March to 31 May 2021 through an anonymous, self-reported questionnaire, on the ‘Qualtrics’ platform. In total, 197 patients and 42 neurologists completed the survey. In our sample, 37.56% of PD patients and 88.10% of neurologists reported having used alternatives to in-person visits, while 13.70% of PD patients and 40.48% of neurologists used telemedicine. Data showed that respondents were generally satisfied with the use of telemedicine during the COVID-19 pandemic. The relational dimension between patient and neurologist seems to be the factor that most positively affected the telemedicine experience, contributing greatly to a more patient-centred care. Current findings suggest the need to improve the access to telehealth services for patients with PD. The technology has the potential to improve the care of frail patients, especially when availability of face-to-face visits is limited.
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