What can we learn from machine learning studies on flow diverter aneurysm embolization? A systematic review.

Background: As the use of flow diverters has expanded in recent years, predicting successful outcomes has become more challenging for certain aneurysms.

Objective: To provide neurointerventionalists with an understanding of the available machine learning algorithms for predicting the success of flow diverters in occluding aneurysms.

Methods: This study followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the four major medical databases (PubMed, Embase, Scopus, Web of Science) were screened.

DSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.

Background And Purpose: Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis.

Impact analysis of primary and secondary research in radiology journals.

Introduction: Primary and secondary studies are considered the two major research categories. In this study, we examined the scientific and social media impact of primary and secondary publication types in papers published radiological journals during 2010-2020.

Materials And Methods: PubMed publication type tags were used to filter original articles and systematic review and meta-analysis (SR/MA) articles.

Prediction of delayed cerebral ischemia followed aneurysmal subarachnoid hemorrhage. A machine-learning based study.

Introduction: Delayed Cerebral Ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH) that can lead to poor outcomes. Machine learning techniques have shown promise in predicting DCI and improving risk stratification.

Methods: In this study, we aimed to develop machine learning models to predict the occurrence of DCI in patients with aSAH.

Journal selection guide for radiology case reports.

Case reports and interesting images are valuable contributions to the radiology literature as they provide unique insights into uncommon conditions and rare presentations. Additionally, they serve as a rapidly expanding live image atlas and, therefore, can help radiologists to improve their diagnostics skills. However, due to high rejection rates and an increasing number of predatory publishers, publishing radiology case reports remains a daunting task for junior researchers.

Common design and data elements reported in active mechanical thrombectomy trials focusing on distal medium vessel occlusions and minor strokes: a systematic review.

Background: Distal medium vessel occlusions (DMVOs) and minor strokes represent emerging frontiers in mechanical thrombectomy (MT). Although several randomized clinical trials (RCTs) are underway, the design characteristics of these trials and the specific questions they aim to address have not been extensively explored. This current study sought to investigate the design and data elements reported in active prospective DMVO and minor stroke studies.

Region-specific interobserver agreement of the Alberta Stroke Program Early Computed Tomography Score: A meta-analysis.

Background And Purpose: The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used scoring system for evaluating ischemic stroke to determine therapeutic strategy. However, there is variation in the interobserver agreement of ASPECTS. This systematic review and meta-analysis aimed to investigate the interobserver agreement of total and regional ASPECTS.

Disability-free outcomes after mechanical thrombectomy: A systematic review and meta-analysis of the randomized controlled trials.

Background: The modified Rankin Scale (mRS) score of ≤2 (functional independence) has been the most common primary endpoint of modern mechanical thrombectomy (MT) trials. However, unlike mRS 0-1, mRS score of 2 indicates disability. An important proportion of the mRS 2 patients are home dependent and report a significant decrease in their quality of life.

Middle meningeal artery embolization for chronic subdural hematoma: A single-center experience and predictive modeling of outcomes.

Background: Remarkable interest is rising around middle meningeal artery embolization (MMAE) as an emerging alternative therapy for chronic subdural hematoma (cSDH). The study aims to highlight a large center experience and the variables associated with treatment failure and build experimental machine learning (ML) models for outcome prediction.

Material And Methods: A 2-year experience in MMAE for managing patients with chronic subdural hematoma was analyzed.

Embolization of arteriovenous malformations of head and neck: A systematic review.

Background: Cervicofacial arteriovenous malformations (AVMs) are a significant source of morbidity. Endovascular embolization has emerged as a promising treatment technique for these lesions. However, current literature on cervicofacial AVM embolization mostly consists of single-agent oriented case series, and to date, no comprehensive study has compared the outcomes of available embolic agents.

Quality Assessment of Radiomics Studies on Functional Outcomes After Acute Ischemic Stroke-A Systematic Review.

Objective: Radiomics is a machine-learning method that extracts features from medical images. The objective of the present systematic review was to assess the quality of existing studies that use radiomics methods to predict functional outcomes in patients after acute ischemic stroke.

Methods: Studies using radiomics-extracted features to predict functional outcomes among patients with acute ischemic stroke using the modified Rankin Scale were included.

Machine learning prediction of hospital discharge disposition for inpatients with acute ischemic stroke following mechanical thrombectomy in the United States.

Background And Purpose: Predicting patient recovery and discharge disposition following mechanical thrombectomy remains a challenge in patients with ischemic stroke. Machine learning offers a promising prognostication approach assisting in personalized post-thrombectomy care plans and resource allocation. As a large national database, National Inpatient Sample (NIS), contain valuable insights amenable to data-mining.

Quantitative image signature and machine learning-based prediction of outcomes in cerebral cavernous malformations.

Purpose: There is increasing interest in novel prognostic tools and predictive biomarkers to help identify, with more certainty, cerebral cavernous malformations (CCM) susceptible of bleeding if left untreated. We developed explainable quantitative-based machine learning models from magnetic resonance imaging (MRI) in a large CCM cohort to demonstrate the value of artificial intelligence and radiomics in complementing natural history studies for hemorrhage and functional outcome prediction.

Materials And Methods: One-hundred-eighty-one patients from a prospectively registered cohort of 366 adults with CCM were included.

The Effect of the COVID-19 Pandemic on Academic Research Gender Disparities in Radiology.

Rationale And Objectives: Gender disparities have long existed in radiology. The COVID-19 pandemic disrupted research activities worldwide and have impacted gender disparities across medical specialties. This study investigates the effect of the COVID-19 pandemic on gender disparities in radiology academic authorship.

Influential Radiology Figures and Organizations in Social Media.

Purpose: With social media becoming a vibrant hub for the radiology community, highlighting expert leaders and trustful conduits of information in the virtual field is proving crucial. The aim of this study was to identify and describe the most prominent and influential figures and organizational accounts to follow in radiology.

Methods: Influence scores for the topic "radiology" on Twitter (now known as X) were computed using the Right Relevance machine learning service.

Machine learning can accurately predict risk factors for all-cause reoperation after ACLR: creating a clinical tool to improve patient counseling and outcomes.

Purpose: Identifying predictive factors for all-cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all-cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression.

Methods: A longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury.

National Institutes of Health grant funding for cerebrovascular diseases.

Background: Federal research funding is highly sought after but may be challenging to attain. A clear understanding of funding for specific diseases, such as cerebrovascular disorders, might help researchers regarding which National Institutes of Health (NIH) institutes fund research into specific disorders and grant types.

Objective: To examine the current scope of NIH grant funding for cerebrovascular conditions.

Automated CT angiography collateral scoring in anterior large vessel occlusion stroke: A multireader study.

Background: Computed tomography (CT) angiography collateral score (CTA-CS) is an important clinical outcome predictor following mechanical thrombectomy for ischemic stroke with large vessel occlusion (LVO). The present multireader study aimed to evaluate the performance of e-CTA software for automated assistance in CTA-CS scoring.

Materials And Methods: Brain CTA images of 56 patients with anterior LVO were retrospectively processed.

Common Data Elements Analysis of Mechanical Thrombectomy Clinical Trials for Acute Ischemic Stroke with Large Core Infarct.

Background: Clinical trials addressing large core acute ischemic stroke (AIS) are ongoing across multiple international groups. Future development of clinical guidelines depends on meta-analyses of these trials calling for a degree of homogeneity of elements across the studies. This common data element study aims to provide an overview of key features of pertinent large core infarct trials.

Operator assessment versus core laboratory adjudication of recanalization following endovascular treatment of acute ischemic stroke: a systematic review and meta-analysis.

Background: Successful recanalization after endovascular thrombectomy serves as the primary endpoint in clinical trials and is a crucial predictor of long-term outcomes. Radiographic outcomes for various interventions have been shown to vary based on the type of interpreter, including the site interventionalist compared with an independent reader.

Objective: To compare angiographic outcomes in stroke thrombectomy procedures based on the type of reader.

Long-term outcomes of flow diversion for unruptured intracranial aneurysms: a systematic review and meta-analysis.

Background: Flow diverters have been widely used in clinical practice for more than a decade. However, most outcome data are limited to 1 year timepoints. This study aims to offer meta-analysis data on long-term (>1 year) safety and effectiveness results for patients with aneurysms treated with flow diverters.

The Prophylactic Use of Glycoprotein 2b/3a Inhibitors in the Endovascular Treatment of Intracranial Aneurysms: A Systematic Review and Meta-Analysis.

Background: There has been a growing interest in the use of Glycoprotein 2b/3a (GP2B3A) inhibitors in neuroendovascular procedures. However, clinical evidence for their prophylactic use is still sparse. In this review, we aimed to assess the safety and efficacy of prophylactic GP2B3A inhibitor use and to compare the performance of GP2B3A inhibitors with oral dual antiplatelet (DAP) treatment in intracranial aneurysm patients treated with stent-assisted coil embolization or flow diversion.

Bibliometric Trends in Open Surgical and Endovascular Cerebrovascular Research.

The last decade has witnessed a major expansion in endovascular interventions concurrent with a contraction of open neurovascular surgeries. Whether research efforts have also shifted from open to endovascular neurosurgery is an effect that has not been explored extensively. Understanding the bibliometric trend is important for researchers, funding agencies, and publishing journals.

Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction.

Background And Purpose: Mechanical thrombectomy greatly improves stroke outcomes. Nonetheless, some patients fall short of full recovery despite good reperfusion. The purpose of this study was to develop machine learning (ML) models for the pre-interventional prediction of functional outcome at 3 months of thrombectomy in acute ischemic stroke (AIS), using clinical and auto-extractable radiological information consistently available upon first emergency evaluation.