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Guozhen Luo, Brent D. Cameron, Li Wang, Hong Yu, Joseph S. Neimat, Peter Hedera, Fenna Phibbs, Elise B. Bradley, Anthony J. Cmelak, and Austin N. Kirschner

OBJECTIVE

Stereotactic radiosurgery (SRS) treats severe, medically refractory essential tremor and tremor-dominant Parkinson disease. However, the optimal target for SRS treatment within the thalamic ventral intermediate nucleus (VIM) is not clearly defined. This work evaluates the precision of the physician-selected VIM target, and determines the optimal SRS target within the VIM by correlation between early responders and nonresponders.

METHODS

Early responders and nonresponders were assessed retrospectively by Elements Basal Ganglia Atlas autocontouring of the VIM on the pre–SRS-treatment 1-mm slice thickness T1-weighted MRI and correlating the center of the post–SRS-treatment lesion. Using pre- and posttreatment diffusion tensor imaging, the fiber tracking package in the Elements software generated tremor-related tracts from autosegmented motor cortex, thalamus, red nucleus, and dentate nucleus. Autocontouring of the VIM was successful for all patients.

RESULTS

Among 23 patients, physician-directed SRS targets had a medial–lateral target range from +2.5 mm to −2.0 mm from the VIM center. Relative to the VIM center, the SRS isocenter target was 0.7–0.9 mm lateral for 6 early responders and 0.9–1.1 mm medial for 4 nonresponders (p = 0.019), and without differences in the other dimensions: 0.2 mm posterior and 0.6 mm superior. Dose–volume histogram analyses for the VIM had no significant differences between responders and nonresponders between 20 Gy and 140 Gy, mean or maximum dose, and dose to small volumes. Tractography data was obtained for 4 patients.

CONCLUSIONS

For tremor control in early responders, the Elements Basal Ganglia Atlas autocontour for the VIM provides the optimal SRS target location that is 0.7–0.9 mm lateral to the VIM center.

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Qing Xie, Xinzhong Shao, Xiaoliang Song, Fengyu Wang, Xu Zhang, Li Wang, Zhemin Zhang, and Li Lyu

OBJECTIVE

The objective of this prospective randomized study was to compare ulnar nerve decompression and anterior subfascial transposition with versus without supercharged end-to-side anterior interosseous nerve–to–ulnar motor nerve transfer for advanced cubital tunnel syndrome, to describe performing the nerve transfer through a small incision, and to investigate predictive factors for poor recovery following the procedure.

METHODS

Between January 2013 and October 2016, 93 patients were randomly allocated to a study group (n = 45) and a control group (n = 48). Patients in the study group were treated with supercharged motor nerve transfer via a 5-cm incision following decompression and anterior subfascial transposition. Patients in the control group were treated with decompression and anterior subfascial transposition alone. Postoperative pinch strength and compound muscle action potential amplitude (CMAPa) were assessed. Function of the limb was assessed based on the Gabel/Amadio scale. Between-group data were compared, and significance was set at p < 0.05. Potential risk factors were collected from demographic data and disease severity indicators.

RESULTS

At the final follow-up at 2 years, the results of the study group were superior to those of the control group with regard to postoperative pinch strength (75.13% ± 7.65% vs 62.11% ± 6.97%, p < 0.05); CMAPa of the first dorsal interossei (17.17 ± 5.84 mV vs 12.20 ± 4.09 mV, p < 0.01); CMAPa of abductor digiti minimi (11.57 ± 4.04 mV vs 8.43 ± 6.11 mV, p < 0.01); and excellent to good results (0.67 for the study group vs 0.35 for the control group, p < 0.05). Multivariate analysis showed that the advanced age (OR 2.98, 95% CI 2.25–4.10; p = 0.003) in the study group was related to unsatisfactory outcome in the patients.

CONCLUSIONS

In the treatment of advanced cubital tunnel syndrome, additional supercharged end-to-side anterior interosseous nerve–to–ulnar motor nerve transfer may produce a better function of the hand. The authors also found that cases in the elderly were related to unsatisfactory postoperative results for these patients and that they could be informed of the possibility of worsening surgery results.

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Andrew T. Hale, David P. Stonko, Li Wang, Megan K. Strother, and Lola B. Chambless

OBJECTIVE

Prognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. The goal of this study was to compare the performance of ML algorithms to standard statistical methods when predicting meningioma grade.

METHODS

The cohort included patients aged 18–65 years with WHO grade I (n = 94) and II (n = 34) meningioma in whom preoperative MRI was obtained between 1998 and 2010. A board-certified neuroradiologist, blinded to histological grade, interpreted all MR images for tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, presence of a draining vein, and patient sex. The authors trained and validated several binary classifiers: k-nearest neighbors models, support vector machines, naïve Bayes classifiers, and artificial neural networks as well as logistic regression models to predict tumor grade. The area under the curve–receiver operating characteristic curve was used for comparison across and within model classes. All analyses were performed in MATLAB using a MacBook Pro.

RESULTS

The authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895).

CONCLUSIONS

ML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.

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Li Wang, Wei Chen, Fujun Liu, Li F. Zhang, and Jing Chen

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Heather M. Kistka, Arash Nayeri, Li Wang, Jamie Dow, Rameela Chandrasekhar, and Lola B. Chambless

OBJECT

Misrepresentation of scholarly achievements is a recognized phenomenon, well documented in numerous fields, yet the accuracy of reporting remains dependent on the honor principle. Therefore, honest self-reporting is of paramount importance to maintain scientific integrity in neurosurgery. The authors had observed a trend toward increasing numbers of publications among applicants for neurosurgery residency at Vanderbilt University and undertook this study to determine whether this change was a result of increased academic productivity, inflated reporting, or both. They also aimed to identify application variables associated with inaccurate citations.

METHODS

The authors retrospectively reviewed the residency applications submitted to their neurosurgery department in 2006 (n = 148) and 2012 (n = 194). The applications from 2006 were made via SF Match and those from 2012 were made using the Electronic Residency Application Service. Publications reported as “accepted” or “in press” were verified via online search of Google Scholar, PubMed, journal websites, and direct journal contact. Works were considered misrepresented if they did not exist, incorrectly listed the applicant as first author, or were incorrectly listed as peer reviewed or published in a printed journal rather than an online only or non-peer-reviewed publication. Demographic data were collected, including applicant sex, medical school ranking and country, advanced degrees, Alpha Omega Alpha membership, and USMLE Step 1 score. Zero-inflated negative binomial regression was used to identify predictors of misrepresentation.

RESULTS

Using univariate analysis, between 2006 and 2012 the percentage of applicants reporting published works increased significantly (47% vs 97%, p < 0.001). However, the percentage of applicants with misrepresentations (33% vs 45%) also increased. In 2012, applicants with a greater total of reported works (p < 0.001) and applicants from unranked US medical schools (those not ranked by US News & World Report) were more likely to have erroneous citations (p = 0.038).

CONCLUSIONS

The incidence of legitimate and misrepresented scholarly works reported by applicants to the authors’ neurosurgery residency program increased during the past 6 years. Misrepresentation is more common in applicants from unranked US medical schools and those with a greater number of reported works on their application. This trend is concerning in a profession where trustworthiness is vital. To preserve integrity in the field, programs should consider verifying citations prior to submitting their rank lists.