Neurosurgery
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Recent advancements in neuroimaging and machine learning have significantly improved our ability to diagnose and categorize isocitrate dehydrogenase (IDH)-wildtype glioblastoma, a disease characterized by notable tumoral heterogeneity, which is crucial for effective treatment. Neuroimaging techniques, such as diffusion tensor imaging and magnetic resonance radiomics, provide noninvasive insights into tumor infiltration patterns and metabolic profiles, aiding in accurate diagnosis and prognostication. Machine learning algorithms further enhance glioblastoma characterization by identifying distinct imaging patterns and features, facilitating precise diagnoses and treatment planning. ⋯ Future directions should focus on refining machine learning models, integrating emerging imaging techniques, and elucidating the complex interplay between imaging features and underlying molecular processes. This review highlights the pivotal role of neuroimaging and machine learning in glioblastoma research, offering invaluable noninvasive tools for diagnosis, prognosis prediction, and treatment planning, ultimately improving patient outcomes. These advances in the field promise to usher in a new era in the understanding and classification of IDH-wildtype glioblastoma.
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Improved imaging modalities have led to an increased detection of intracranial aneurysms, many of which are small. There is uncertainty in the appropriate management of tiny aneurysms. The objective of this study was to use a large, multi-institutional NeuroVascular Quality Initiative-Quality Outcomes Database (NVQI-QOD) to assess the frequency, safety, and efficacy of treatment of tiny, unruptured middle cerebral artery (MCA) aneurysms. ⋯ Treatment of tiny, unruptured MCA aneurysms is efficacious but may have a high rate of complications. Physicians should be cautious when deciding to treat tiny, unruptured MCA aneurysms.
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Post-traumatic brain injury (TBI) lesions, which combine brain atrophy and white matter injuries, can lead to progressive post-traumatic encephalopathy. However, the specific involvement of the cerebellum, which participates in cognitive, executive, and sensory functions, has been little studied. The aim of this work was to explore the long-term cerebellar consequences of severe TBI. ⋯ This work shows that even if direct cerebellar damage is rare, long-term post-TBI cerebellar lesions can be observed. Therefore, clinical correlates of cerebellar lesions should be considered more systematically.
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Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and management. Artificial intelligence (AI) techniques have been used in other fields of medicine to identify different phenotypic clusters that guide clinical care. In this study, we use a novel, combined data-driven and clinician input feature selection process and AI clustering to differentiate presenting phenotypes of CM1 + SM. ⋯ This is the first study that uses an AI clustering algorithm combining a data-driven feature selection process with clinical expertise to identify different presenting phenotypes of CM1 + SM.
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The optimal management strategy for pediatric patients with symptomatic moyamoya disease (MMD) is not well established. This systematic review and meta-analysis compares surgical vs conservative management and direct/combined bypass (DB/CB) vs indirect bypass (IB) for pediatric patients with symptomatic MMD. ⋯ Surgical revascularization yielded more favorable clinical outcomes than conservative management in this meta-analysis. Clinical outcomes were similar between DB/CB vs IB techniques. Surgical flow augmentation, either by DB/CB or IB, seems to benefit pediatric patients with symptomatic MMD.