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Targeted therapies for driver gene fusions in cancers have yielded substantial improvements in care. Here, the authors outline a case series of 6 patients with FGFR3-TACC3 fusion in primary brain tumors ranging from polymorphous low-grade neuroepithelial tumor of the young to papillary glioneuronal tumors and glioblastoma (GBM). Previous studies indicated the FGFR3-TACC3 fusion provides survival benefit to GBM patients. Consistent with this, 2 patients with GBM had unexpectedly good outcomes and survived for 5 and 7 years, respectively. In contrast, 2 patients with initially lower graded tumors survived only 3 years and 1 year, respectively. One patient received erdafitinib, a targeted FGFR inhibitor, for 3 months at late disease recurrence and no response was seen. There were varied histomorphological features, including many cases that lacked the characteristic FGFR3-TACC3 pathology. The findings of this cohort suggest that molecular testing is justified, even for glioma cases lacking classic histopathological signatures. Currently, FGFR3-TACC3 fusion gliomas are often classified on the basis of histopathological features. However, further research is needed to examine whether IDH1/2-wild-type tumors with FGFR3-TACC3 fusion should be classified as a subtype on the basis of this molecular fusion. Because patients with IDH1/2-wild-type GBM with FGFR3-TACC3 fusion have improved survival, routine molecular testing for this mutation in patients enrolled in clinical trials and subsequent stratification may be warranted.

Objective: Vestibular schwannomas (VSs) are the most common neoplasm of the cerebellopontine angle in adults. Though these lesions are generally slow growing, their growth patterns and associated symptoms can be unpredictable, which may complicate the decision to pursue conservative management versus active intervention. Additionally, surgical decision-making can be controversial because of limited high-quality evidence and multiple quality-of-life considerations. Machine learning (ML) is a powerful tool that utilizes data sets to essentialize multidimensional clinical processes. In this study, the authors trained multiple tree-based ML algorithms to predict the decision for active treatment versus MRI surveillance of VS in a single institutional cohort. In doing so, they sought to assess which preoperative variables carried the most weight in driving the decision for intervention and could be used to guide future surgical decision-making through an evidence-based approach., Methods: The authors reviewed the records of patients who had undergone evaluation by neurosurgery and otolaryngology with subsequent active treatment (resection or radiation) for unilateral VS in the period from 2009 to 2021, as well as those of patients who had been evaluated for VS and were managed conservatively throughout 2021. Clinical presentation, radiographic data, and management plans were abstracted from each patient record from the time of first evaluation until the last follow-up or surgery. Each encounter with the patient was treated as an instance involving a management decision that depended on demographics, symptoms, and tumor profile. Decision tree and random forest classifiers were trained and tested to predict the decision for treatment versus imaging surveillance on the basis of unseen data using an 80/20 pseudorandom split. Predictor variables were tuned to maximize performance based on lowest Gini impurity indices. Model performance was optimized using fivefold cross-validation., Results: One hundred twenty-four patients with 198 rendered decisions concerning management were included in the study. In the decision tree analysis, only a maximum tumor dimension threshold of 1.6 cm and progressive symptoms were required to predict the decision for treatment with 85% accuracy. Optimizing maximum dimension thresholds and including age at presentation boosted accuracy to 88%. Random forest analysis (n = 500 trees) predicted the decision for treatment with 80% accuracy. Factors with the highest variable importance based on multiple measures of importance, including mean minimal conditional depth and largest Gini impurity reduction, were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms at presentation., Conclusions: Tree-based ML was used to predict which factors drive the decision for active treatment of VS with 80%-88% accuracy. The most important factors were maximum tumor dimension, age at presentation, Koos grade, and progressive symptoms. These results can assist in surgical decision-making and patient counseling. They also demonstrate the power of ML algorithms in extracting useful insights from limited data sets.

Objective: Telemedicine has rapidly expanded in the recent years as technologies have afforded healthcare practitioners the ability to diagnose and treat patients remotely. Due to the COVID-19 pandemic, nonessential clinical visits were greatly limited, and much of the outpatient neurosurgical practice at the authors' institution was shifted quickly to telehealth. Although there are prior data suggesting that the use of telemedicine is satisfactory in other surgical fields, data in neurosurgery are limited. This study aimed to investigate both patient and provider satisfaction with telemedicine and its strengths and limitations in outpatient neurosurgery visits., Methods: This quality improvement study was designed to analyze provider and patient satisfaction with telemedicine consultations in an outpatient neurosurgery clinic setting at a tertiary care, large-volume, academic center. The authors designed an 11-question survey for neurosurgical providers and a 13-question survey for patients using both closed 5-point Likert scale responses and multiple choice responses. The questionnaires were administered to patients and providers during the period when the clinic restricted in-person visits. At the conclusion of the study, the overall data were analyzed qualitatively and quantitatively., Results: During the study period, 607 surveys were sent out to patients seen by telehealth at the authors' academic center, and 122 responses were received. For the provider survey, 85 surveys were sent out to providers at the authors' center and other academic centers, and 40 surveys were received. Ninety-two percent of patients agreed or strongly agreed that they were satisfied with that particular telehealth visit. Eighty-eight percent of patients agreed that their telehealth visit was more convenient for them than an in-person visit, but only 36% of patients stated they would like their future visits to be telehealth. Sixty-three percent of providers agreed that telehealth visits were more convenient for them than in-person visits, and 85% of responding providers stated that they wished to incorporate telehealth into their future practice., Conclusions: Although the authors' transition to telehealth was both rapid and unexpected, most providers and patients reported positive experiences with their telemedicine visits and found telemedicine to be an effective form of ambulatory neurosurgical care. Not all patients preferred telemedicine visits over in-person visits, but the high satisfaction with telemedicine by both providers and patients is promising to the future expansion of telehealth in ambulatory neurosurgery.

Objective: Neurosurgical education in the US has changed significantly as a consequence of the novel coronavirus (COVID-19) pandemic. Institutional social distancing requirements have resulted in many neurosurgical programs utilizing video conferencing for educational activities. However, it is unclear how or if these practices should continue after the pandemic. The objective of this study was to characterize virtual education in neurosurgery and understand how it should be utilized after COVID-19., Methods: A 24-question, 3-part online survey was administered anonymously to all 117 US neurosurgical residency programs from May 15, 2020, to June 15, 2020. Questions pertained to the current use of virtual conferencing, preferences over traditional conferences, and future inclinations. The Likert scale (1 = strongly disagree, 3 = neutral, 5 = strongly agree) was used. Comparisons were calculated using the Mann-Whitney U-test. Statistical significance was set at 0.05., Results: One-hundred eight responses were recorded. Overall, 38 respondents (35.2%) were attendings and 70 (64.8%) were trainees. Forty-one respondents (38.0%) indicated attending 5-6 conferences per week and 70 (64.8%) attend national virtual conferences. When considering different conference types, there was no overall preference (scores < 3) for virtual conferences over traditional conferences. In regard to future use, respondents strongly agreed that they would continue the practice at some capacity after the pandemic (median score 5). Overall, respondents agreed that virtual conferences would partially replace traditional conferences (median score 4), whereas they strongly disagreed with the complete replacement of traditional conferences (median score 1). The most common choices for the partial replacement of tradition conferences were case conferences (59/108, 55%) and board preparation (64/108, 59%). Lastly, there was a significant difference in scores for continued use of virtual conferencing in those who attend nationally sponsored conferences (median score 5, n = 70) and those who do not (median score 4, n = 38; U = 1762.50, z = 2.97, r = 0.29, p = 0.003)., Conclusions: Virtual conferences will likely remain an integral part of neurosurgical education after the COVID-19 pandemic has abated. Across the country, residents and faculty report a preference for continued use of virtual conferencing, especially virtual case conferences and board preparation. Some traditional conferences may even be replaced with virtual conferences, in particular those that are more didactic. Furthermore, nationally sponsored virtual conferences have a positive effect on the preferences for continued use of virtual conferences.