INTRODUCTION: Reliable preoperative identification of patients at high risk for early postoperative complications occurring within 24 h (EPC) of intracranial tumor surgery can improve patient safety and postoperative management. Statistical analysis using machine learning algorithms may generate models that predict EPC better than conventional statistical methods. OBJECTIVE: To train such a model and to assess its predictive ability. METHODS: This cohort study included patients from an ongoing prospective patient registry at a single tertiary care center with an intracranial tumor that underwent elective neurosurgery between June 2015 and May 2017. EPC were categorized based on the Clavien-Dindo classification score. Conventional statistical methods and different machine learning algorithms were used to predict EPC using preoperatively available patient, clinical, and surgery-related variables. The performance of each model was derived from examining classification performance metrics on an out-of-sample test dataset. RESULTS: EPC occurred in 174 (26%) of 668 patients included in the analysis. Gradient boosting machine learning algorithms provided the model best predicting the probability of an EPC. The model scored an accuracy of 0.70 (confidence interval [CI] 0.59-0.79) with an area under the curve (AUC) of 0.73 and a sensitivity and specificity of 0.80 (CI 0.58-0.91) and 0.67 (CI 0.53-0.77) on the test set. The conventional statistical model showed inferior predictive power (test set: accuracy: 0.59 (CI 0.47-0.71); AUC: 0.64; sensitivity: 0.76 (CI 0.64-0.85); specificity: 0.53 (CI 0.41-0.64)). CONCLUSION: Using gradient boosting machine learning algorithms, it was possible to create a prediction model superior to conventional statistical methods. While conventional statistical methods favor patients’ characteristics, we found the pathology and surgery-related (histology, anatomical localization, surgical access) variables to be better predictors of EPC.

Ischemic stroke can arise from the sudden occlusion of a brain-feeding artery by a clot (embolic), or local thrombosis. Hemodynamic stroke occurs when blood flow does not sufficiently meet the metabolic demand of a brain region at a certain time. This discrepancy between demand and supply can occur with cerebropetal arterial occlusion or high-grade stenosis but also arises with systemic conditions reducing blood pressure. Treatment of hemodynamic stroke is targeted toward increasing blood flow to the affected area by either systemically or locally enhancing perfusion. Thus, blood pressure is often maintained above normal values, and extra-intracranial flow augmentation bypass surgery is increasingly considered. Still, current evidence supporting the superiority of pressure or flow increase over conservative measures is limited. However, methods assessing hemodynamic impairment and identifying patients at risk of hemodynamic stroke are rapidly evolving. Sophisticated models incorporating clinical and imaging factors have been suggested to aid patient selection. In this narrative review, we provide current state-of-the-art knowledge about hemodynamic stroke, tools for assessment, and treatment options.

Background: Comprehensive hemodynamic impairment mapping using blood oxygenation‐level dependent (BOLD) cerebrovascular reactivity (CVR) can be used to identify hemodynamically relevant symptomatic unilateral carotid artery disease. Methods and Results: This prospective cohort study was conducted between February 2015 and July 2020 at the Clinical Neuroscience Center of the University Hospital Zurich, Zurich, Switzerland. One hundred two patients with newly diagnosed symptomatic unilateral internal carotid artery (ICA) occlusion or with 70% to 99% ICA stenosis were included. An age‐matched healthy cohort of 12 subjects underwent an identical BOLD functional magnetic resonance imaging examination. Using BOLD functional magnetic resonance imaging with a standardized CO ₂ stimulus, CVR impairment was evaluated. Moreover, embolic versus hemodynamic ischemic patterns were evaluated on diffusion‐weighted imaging. Sixty‐seven patients had unilateral ICA occlusion and 35 patients unilateral 70% to 99% ICA stenosis. Patients with ICA occlusion exhibited lower whole‐brain and ipsilateral hemisphere mean BOLD‐CVR values as compared with healthy subjects (0.12±0.08 versus 0.19±0.04, P =0.004 and 0.09±0.09 versus 0.18±0.04, P <0.001) and ICA stenosis cohort (0.12±0.08 versus 0.16±0.05, P =0.01 and 0.09±0.09 versus 0.15±0.05, P =0.01); however, only 40 (58%) patients of the cohort showed significant BOLD‐CVR impairment. Conversely, there was no difference in mean BOLD‐CVR values between healthy patients and patients with ICA stenosis, although 5 (14%) patients with ICA stenosis showed a significant BOLD‐CVR impairment. No significant BOLD‐CVR difference was discernible between patients with hemodynamic ischemic infarcts versus those with embolic infarct distribution (0.11±0.08 versus 0.13±0.06, P =0.12). Conclusions: Comprehensive BOLD‐CVR mapping allows for identification of hemodynamically relevant symptomatic unilateral carotid artery stenosis or occlusion.

BACKGROUND: Impaired cerebrovascular reactivity (CVR) has been correlated with recurrent ischemic stroke. However, for clinical purposes, most CVR techniques are rather complex, time-consuming, and lack validation for quantitative measurements. The recent adaptation of a standardized hypercapnic stimulus in combination with a blood-oxygenation-level-dependent (BOLD) magnetic resonance imaging signal as a surrogate for cerebral blood flow offers a potential universally comparable CVR assessment. We investigated the association between impaired BOLD-CVR and risk for recurrent ischemic events. METHODS: We conducted a retrospective analysis of patients with symptomatic cerebrovascular large vessel disease who had undergone a prospective hypercapnic-challenged BOLD-CVR protocol at a single tertiary stroke referral center between June 2014 and April 2020. These patients were followed up for recurrent acute ischemic events for up to 3 years. BOLD-CVR (%BOLD signal change per mm Hg CO ₂ ) was calculated on a voxel-by-voxel basis. Impaired BOLD-CVR of the affected (ipsilateral to the vascular pathology) hemisphere was defined as an average BOLD-CVR, falling 2 SD below the mean BOLD-CVR of the right hemisphere in a healthy age-matched reference cohort (n=20). Using a multivariate Cox proportional hazards model, the association between impaired BOLD-CVR and ischemic stroke recurrence was assessed and Kaplan-Meier survival curves to visualize the acute ischemic stroke event rate. RESULTS: Of 130 eligible patients, 28 experienced recurrent strokes (median, 85 days, interquartile range, 5–166 days). Risk factors associated with an increased recurrent stroke rate included impaired BOLD-CVR, a history of atrial fibrillation, and heart insufficiency. After adjusting for sex, age group, and atrial fibrillation, impaired BOLD-CVR exhibited a hazard ratio of 10.73 (95% CI, 4.14–27.81; P <0.001) for recurrent ischemic stroke. CONCLUSIONS: Among patients with symptomatic cerebrovascular large vessel disease, those exhibiting impaired BOLD-CVR in the affected hemisphere had a 10.7-fold higher risk of recurrent ischemic stroke events compared with individuals with nonimpaired BOLD-CVR.

Gliomas, and glioblastoma in particular, exhibit an extensive intra- and inter-tumoral molecular heterogeneity which represents complex biological features correlating to the efficacy of treatment response and survival. From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes. The development of comprehensive glioma imaging markers has potential for improved glioma characterization that would assist in the clinical work-up of preoperative treatment planning and treatment effect monitoring. In particular, the differentiation of tumor recurrence or true progression from pseudoprogression, pseudoresponse, and radiation-induced necrosis can still not reliably be made through standard neuroimaging only. Given the abundant vascular and hemodynamic alterations present in diffuse glioma, advanced hemodynamic imaging approaches constitute an attractive area of clinical imaging development. In this context, the inclusion of objective measurable glioma imaging features may have the potential to enhance the individualized care of diffuse glioma patients, better informing of standard-of-care treatment efficacy and of novel therapies, such as the immunotherapies that are currently increasingly investigated. In Part B of this two-review series, we assess the available evidence pertaining to hemodynamic imaging for molecular feature prediction, in particular focusing on isocitrate dehydrogenase (IDH) mutation status, MGMT promoter methylation, 1p19q codeletion, and EGFR alterations. The results for the differentiation of tumor progression/recurrence from treatment effects have also been the focus of active research and are presented together with the prognostic correlations identified by advanced hemodynamic imaging studies. Finally, the state-of-the-art concepts and advancements of hemodynamic imaging modalities are reviewed together with the advantages derived from the implementation of radiomics and machine learning analyses pipelines.