Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
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Glioma is the most common primary intraparenchymal tumor of the brain and the 5-year survival rate of high-grade glioma is poor. Magnetic resonance imaging (MRI) is essential for detecting, characterizing and monitoring brain tumors but definitive diagnosis still relies on surgical pathology. Machine learning has been applied to the analysis of MRI data in glioma research and has the potential to change clinical practice and improve patient outcomes. ⋯ Machine learning tools and data resources were synthesized and summarized to facilitate future research. Machine learning has been widely applied to the processing of MRI data in glioma research and has demonstrated substantial utility. NLP and transfer learning resources enabled the successful development of a replicable method for automating the systematic review article screening process, which has potential for shortening the time from discovery to clinical application in medicine.
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Complete reperfusion (mTICI 3) in anterior circulation ischemic stroke patients after a single mechanical thrombectomy (MT) pass has been identified as a predictor of favorable outcome (modified Rankin Score 0-2) and defined as true first-pass effect recently. This effect has not yet been demonstrated in posterior circulation ischemic stroke. We hypothesized a true first-pass effect for the subgroup of acute basilar artery occlusions (BAO). ⋯ Single pass complete reperfusion in acute basilar artery occlusion is an independent predictor of favorable outcome. Achieving complete reperfusion after multiple passes might impair favorable patient recovery.