Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (< 6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.
Background
Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome.
Methods
We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models.
Results
We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67–82]), than encephalopathy (54% [42–65]). Intensive care use was high (38% [35–41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27–32]. The hazard of death was comparatively lower for patients in the WHO European region.
Interpretation
Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission.
Delayed cerebral ischemia (DCI) is a dreadful complication present in 30% of subarachnoid hemorrhage (SAH) patients. Dci prediction and prevention are burdensome in poor grade SAH patients (WfnS 4-5). Therefore, defining an optimal neuromonitoring strategy might be cumbersome. Cerebral microdialysis (CMD) offers near-real-time regional metabolic data of the surrounding brain. However, unilateral neuromonitoring strategies obviate the diffuse repercussions of SAH. To assess the utility, indications and therapeutic implications of bilateral cMD in poor grade SAH patients. poor grade SAH patients eligible for multimodal neuromonitoring were prospectively collected. Aneurysm location and blood volume were assessed on initial Angio-CT scans. CMD probes were bilaterally implanted and maintained, at least, for 48 hours (h). Ischemic events were defined as a Lactate/Pyruvate ratio >40 and Glucose concentration <0.7 mmol/L. 16 patients were monitored for 1725 h, observing ischemic events during 260 h (15.1%). Simultaneous bilateral ischemic events were rare (5 h, 1.9%). The established threshold of ≥7 ischemic events displayed a specificity and sensitivity for DCI of 96.2% and 83.3%, respectively. Bilateral CMD is a safe and useful strategy to evaluate areas at risk of suffering DCI in SAH patients. Metabolic crises occur bilaterally but rarely simultaneously. Hence, unilateral neuromonitoring strategies underestimate the risk of infarction and the possibility to offset its consequences.
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