Neurosurgery
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What evidence is available regarding emerging and investigational treatment options for metastatic brain tumors? ⋯ Level 1: The use of afatinib is not recommended in patients with brain metastasis due to breast cancer.There is insufficient evidence to make recommendations regarding: the use of epidermal growth factor receptor inhibitors erlotinib and gefitinib in patients with brain metastasis due to nonsmall cell lung cancerthe use of BRAF inhibitors dabrafenib and vemurafenib in the treatment of patients with brain metastases due to metastatic melanomathe use of HER2 agents trastuzumab and lapatinib to treat patients with brain metastases due to metastatic breast cancerthe use of vascular endothelial growth factor agents bevacizumab, sunitinib, and sorafenib in the treatment of patients with solid tumor brain metastases.The full guideline can be found at: https://www.cns.org/guidelines/guidelines-treatment-adults-metastatic-brain-tumors/chapter_9.
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Image guidance is a promising technology that could lead to lower rates of premature shunt failure by decreasing the rate of inaccurate proximal catheter placement. ⋯ Electromagnetic neuronavigation results in more accurate placement of catheters, but did not result in an overall reduction in proximal shunt failure at 90 and 180 days after the index surgery. However, subgroup analysis suggests a clinically important benefit in those patients with harder to cannulate ventricles.
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Deformity reconstruction surgery has been shown to improve quality of life (QOL) in cases of adult spinal deformity (ASD) but is associated with significant morbidity. ⋯ The predictive nomogram that we developed using these data can improve preoperative risk counseling and patient selection for deformity correction surgery.
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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML). ⋯ The 7 T-ML method is highly consistent with microelectrode-recordings data. This method provides a reliable and accurate patient-specific prediction for targeting the STN.