Citation analysis of the most influential ependymoma research articles illustrates improved knowledge of the molecular biology of ependymoma.

The history of academic research on ependymoma is expansive. This review summarizes its history with a bibliometric analysis of the 100 most cited articles on ependymoma. In March 2020, we queried the Web of Science database to identify the most cited articles on ependymoma using the terms "ependymoma" or "ependymal tumors," yielding 3145 publications. Results were arranged by the number of times each article was cited in descending order. The top 100 articles spanned across nearly a century; the oldest article was published in 1924, while the most recent was in 2017. These articles were published in 35 unique journals, including a mix of basic science and clinical journals. The three institutions with the most papers in the top 100 were St. Jude Children's Research Hospital (16%), the University of Texas MD Anderson Cancer Center (6%), and the German Cancer Research Center (5%). We analyzed the publications that may be considered the most influential in the understanding and treatment management of ependymoma. Studies focused on the molecular classification of ependymomas were well-represented among the most cited articles, reflecting the field's current area of focus and its future directions. Additionally, this article also offers a reference for further studies in the ependymoma field.

MR-guided delivery of AAV2-BDNF into the entorhinal cortex of non-human primates.

Brain-derived neurotrophic factor (BDNF) gene delivery to the entorhinal cortex is a candidate for treatment of Alzheimer's disease (AD) to reduce neurodegeneration that is associated with memory loss. Accurate targeting of the entorhinal cortex in AD is complex due to the deep and atrophic state of this brain region. Using MRI-guided methods with convection-enhanced delivery, we were able to accurately and consistently target AAV2-BDNF delivery to the entorhinal cortex of non-human primates; 86 ± 3% of transduced cells in the targeted regions co-localized with the neuronal marker NeuN. The volume of AAV2-BDNF (3 × 108 vg/µl) infusion linearly correlated with the number of BDNF labeled cells and the volume (mm3) of BDNF immunoreactivity in the entorhinal cortex. BDNF is normally trafficked to the hippocampus from the entorhinal cortex; in these experiments, we also found that BDNF immunoreactivity was elevated in the hippocampus following therapeutic BDNF vector delivery to the entorhinal cortex, achieving growth factor distribution through key memory circuits. These findings indicate that MRI-guided infusion of AAV2-BDNF to the entorhinal cortex of the non-human primate results in safe and accurate targeting and distribution of BDNF to both the entorhinal cortex and the hippocampus. These methods are adaptable to human clinical trials.

Surgical Management of Spinal Cysticercosis: 2-Dimensional Operative Video.

Neurocysticercosis is a notorious-albeit relatively rare-parasitic infection of the nervous system caused by ingestion of embryonated eggs or gravid proglottids of the pork tapeworm Taenia solium.1 Most cases appear intracranially and can present with seizures and/or headache; far fewer affect the spinal canal alone.1-6 We describe a case of a 66-year-old Hispanic man with isolated spinal cysticercosis presenting to the emergency department initially with axial back pain. After lumbar x-rays and a computed tomography scan failed to find any pathology, he was discharged home without neurosurgical consultation. A week later he would re-present to the same emergency department with worsening back pain and new neurologic deficits, prompting an MRI revealing pathology within the lumbar spinal canal. After consultation, the neurosurgical team discussed management strategies and eventually opted for surgical exploration of the patient's lumbar spine. This exploration eventually culminated in an intradural exploration, revealing an impressive string of interconnected larval cysts of the T. solium tapeworm within the cauda equina. The patient's neurologic condition improved immediately after surgery, and final pathologic examination supported a diagnosis of spinal cysticercosis, prompting the appropriate workup and medical treatment. He would ultimately make a full recovery and continues to follow-up with infectious disease at our institution. The patient consented to the surgical intervention described herein after a discussion of its risks, benefits, and alternatives. Institutional review board and ethics committee approval was not sought because the intervention did not constitute experimental surgery, but rather represented standard of care given the patient's condition on presentation.

"Doctor, what would you do if you were me?" - A survey of physician perspectives toward glioblastoma resection.

OBJECTIVE: How maximal safe resection (MSR) of glioblastoma is implemented in the clinical setting remains understudied. Here, we utilized a survey-based approach to understand physician perspectives on this matter. METHODS: Scenarios involving glioblastomas were presented to physicians who were asked to select from planned sub-total resection (STR), gross total resection (GTR), medical therapy only, or palliative care. Demographic, experience, and Likert scales of value assessment were collected. RESULTS: In the scenario involving a corpus callosum glioblastoma, 2.33% opted for GTR. For a right frontal glioblastoma, 91.7% opted for GTR. In contrast, only 30.8% chose GTR of a right motor strip glioblastoma (p< 0.001). When presented with a left motor strip glioblastoma, fewer respondents (12.7%,p < 0.001) opted for GTR. Physicians who placed a high value on preserving physical independence were more likely to forgo GTR for right motor glioblastomas (HR=0.068,95% CI:0.47-0.97,p=0.035), and physicians who placed a high value on their faith were more likely to opt for surgical treatments that differ from the general consensus, for instance opting for GTR of the corpus callosum glioblastoma (HR=4.18,95%CI:1.63-10.74,p=0.003). No other associations were found between the choice for GTR and other variables collected. INTERPRETATION: Our results suggest that while maximal safe resection remains a guiding principle for glioblastoma resection, physician preference in terms of the extent of resection varies significantly as a function of tumor location and personal values.

Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients.

Background: Cerebral vasospasm (CV) is a feared complication occurring in 20-40% of patients following subarachnoid hemorrhage (SAH) and is known to contribute to delayed cerebral ischemia. It is standard practice to admit SAH patients to intensive care for an extended period of vigilant, resource-intensive, clinical monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date. Methods: SAH patients admitted to UCLA from 2013-2022 and a validation cohort from VUMC from 2018-2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or ICU downgrade. At each institution, a light gradient boosting machine (LightGBM) was trained using five- fold cross validation to predict the primary endpoint at various timepoints during hospital admission. Receiver-operator curves (ROC) and precision-recall (PR) curves were generated. Results: A total of 1,750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 an average of over one week in advance, and successfully ruled out 8% of non-verapamil patients with zero false negatives. Minimum leukocyte count, maximum platelet count, and maximum intracranial pressure were the variables with highest predictive accuracy. Our models predicted "no CVRV" vs "CVRV within three days" vs "CVRV after three days" with AUCs=0.88, 0.83, and 0.88, respectively. For external validation at VUMC, 1,654 patients were included, 75 receiving verapamil. Predictive models at VUMC performed very similarly to those at UCLA, averaging 0.01 AUC points lower. Conclusions: We present an accurate (AUC=0.88) and early (>1 week prior) predictor of CVRV using machine learning over two large cohorts of subarachnoid hemorrhage patients at separate institutions. This represents a significant step towards optimized clinical management and improved resource allocation in the intensive care setting of subarachnoid hemorrhage patients.

Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.

BACKGROUND: Cerebral vasospasm (CV) is a feared complication which occurs after 20-40% of subarachnoid haemorrhage (SAH). It is standard practice to admit patients with SAH to intensive care for an extended period of resource-intensive monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date. METHODS: Patients with SAH admitted to UCLA from 2013 to 2022 and a validation cohort from VUMC from 2018 to 2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or no verapamil. At each institution, a light gradient boosting machine (LightGBM) was trained using five-fold cross validation to predict the primary endpoint at various hospitalization timepoints. FINDINGS: A total of 1750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 > 1 week in advance and ruled out 8% of non-verapamil patients with zero false negatives. Our models predicted "no CVRV" vs "CVRV within three days" vs "CVRV after three days" with AUCs = 0.88, 0.83, and 0.88, respectively. From VUMC, 1654 patients were included, 75 receiving verapamil. VUMC predictions averaged within 0.01 AUC points of UCLA predictions. INTERPRETATION: We present an accurate and early predictor of CVRV using machine learning with multi-center validation. This represents a significant step towards optimized clinical management and resource allocation in patients with SAH. FUNDING: Robert E. Freundlich is supported by National Center for Advancing Translational Sciences federal grant UL1TR002243 and National Heart, Lung, and Blood Institute federal grant K23HL148640; these funders did not play any role in this study. The National Institutes of Health supports Vanderbilt University Medical Center which indirectly supported these research efforts. Neither this study nor any other authors personally received financial support for the research presented in this manuscript. No support from pharmaceutical companies was received.

Early Postoperative Opioid Requirement Is Associated With Later Pain Control Needs After Supratentorial Craniotomies.

BACKGROUND: Despite a renewed focus in recent years on pain management in the inpatient hospital setting, postoperative pain after elective craniotomy remains under investigated. This study aims to identify which perioperative factors associate most strongly with postoperative pain and opioid medication requirements after inpatient craniotomy. MATERIALS AND METHODS: Using an existing dataset, we selected a restricted cohort of patients who underwent elective craniotomy surgery requiring an inpatient postoperative stay during a 7-year period at our institution (n=1832). We examined pain scores and opioid medication usage and analyzed the relative contribution of specific perioperative risk factors to postoperative pain and opioid medication intake (morphine milligram equivalents). RESULTS: Postoperative pain was found to be highest on postoperative day 1 and decreased thereafter (up to day 5). Factors associated with greater postoperative opioid medication requirement were preoperative opioid medication use, duration of anesthesia, degree of pain in the preoperative setting, and patient age. Notably, the most significant factor associated with a higher postoperative pain score and Morphine milligram equivalents requirement was the time elapsed between the end of general anesthesia and a patient's first intravenous opioid medication. CONCLUSION: Postcraniotomy patients are at higher risk for requiring opioid pain medications if they have a history of preoperative opioid use, are of younger age, or undergo a longer surgery. Moreover, early requirement of intravenous opioid medications in the postoperative period should alert treating physicians that a patient's pain may require additional or alternative methods of pain control than routinely administered, to avoid over-reliance on opioid medications.

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

BACKGROUND: Narrowing of the lumbar spinal canal, or lumbar stenosis (LS), may cause debilitating radicular pain or muscle weakness. It is the most frequent indication for spinal surgery in the elderly population. Modern diagnosis relies on magnetic resonance imaging and its inherently subjective interpretation. Diagnostic rigor, accuracy, and speed may be improved by automation. In this work, we aimed to determine whether a deep-U-Net ensemble trained to segment spinal canals on a heterogeneous mix of clinical data is comparable to radiologists' segmentation of these canals in patients with LS. METHODS: The deep U-nets were trained on spinal canals segmented by physicians on 100 axial T2 lumbar magnetic resonance imaging selected randomly from our institutional database. Test data included a total of 279 elderly patients with LS that were separate from the training set. RESULTS: Machine-generated segmentations (MA) were qualitatively similar to expert-generated segmentations (ME1, ME2). Machine- and expert-generated segmentations were quantitatively similar, as evidenced by Dice scores (MA vs. ME1: 0.88 ± 0.04, MA vs. ME2: 0.89 ± 0.04), the Hausdorff distance (MA vs. ME1: 11.7 mm ± 13.8, MA vs. ME2: 13.1 mm ± 16.3), and average surface distance (MAvs. ME1: 0.18 mm ± 0.13, MA vs. ME2 0.18 mm ± 0.16) metrics. These metrics are comparable to inter-rater variation (ME1 vs. ME2 Dice scores: 0.94 ± 0.02, the Hausdorff distances: 9.3 mm ± 15.6, average surface distances: 0.08 mm ± 0.09). CONCLUSION: We conclude that machine learning algorithms can segment lumbar spinal canals in LS patients, and automatic delineations are both qualitatively and quantitatively comparable to expert-generated segmentations.

The 100 Most Highly Cited Publications on Hearing Preservation for Vestibular Schwannomas.

OBJECTIVE: Vestibular schwannomas are benign, slow-growing tumors that often reduce patient quality of life by compressing nearby nerves. Neurological function preservation is one of the indicators of treatment success, with hearing preservation being the most difficult to obtain. This paper provides a bibliometric analysis of hearing preservation in treating acoustic neuromas and a greater understanding of the most highly cited articles, which have enhanced our understanding of this topic. METHODS: Key terms of "acoustic neuroma," "vestibular schwannoma," and "hearing preservation" were queried through Web of Science. Articles were sorted by citation frequency, and the top 100 articles were recorded for title, name of first author, journal title, year of publication, total number of citations (and associated rank), average number of citations per year, country of the first author's associated institution, and type of study. RESULTS: The top 100 cited articles were published from 1980 to 2014. The United States had the highest involvement as a country (55%), the University of Pittsburgh as an institution (13%), and The Journal of Neurosurgery as a publishing source (27%). Fourteen were reviews, and 86 were clinical papers. Of the 86, 73 were retrospective studies. CONCLUSION: Bibliometric analyses summarize and assess potential areas of strength and knowledge gaps within the literature. Studies on hearing preservation in vestibular schwannomas mostly consist of retrospective reviews that assess postoperative outcomes of microsurgery and radiosurgery. Prospective studies and novel treatment options for hearing preservation in vestibular schwannomas are needed to increase current literature diversity.

Evaluation of Superior Semicircular Canal Dehiscence Anatomical Location and Clinical Outcomes: A Single Institution's Experience.

BACKGROUND: Superior semicircular canal dehiscence (SSCD) is becoming increasingly recognized as a pathology underlying various auditory and vestibular complaints. To date, our understanding of the pathology has yet to attribute specific symptoms to the anatomic location of dehiscence in patients with SSCD. This study aims to address this issue by evaluating the relationship between symptomatology and anatomic location of dehiscence. METHODS: A single-institution retrospective review of SSCD patients was performed. Information was collected on patient demographics, symptomatology, and anatomic location of dehiscence. High-resolution computed tomography scans of the temporal bones were used to categorize the anatomic SSCD location into 1 of 3 groups: anterior limb, apex, and posterior limb. Lastly, we performed statistical analysis to determine the degree of association between each of the various perioperative factors and anatomic SSCD location. RESULTS: We studied 54 patients in total (32 women, 22 men). Mean age at diagnosis was 53 years (range: 20-82 years) and mean follow-up length was 5.5 months (range: 0.03-27.0 months). The most common anatomical location of superior semicircular canal dehiscence was the apex, which was seen in 68.5% of cases. While preoperative symptomatology was similar among the 3 cohorts, those with apical dehiscences had a significantly higher rate of postoperative improvement of autophony (P = 0.03), aural fullness (P = 0.03), and tinnitus (P = 0.05) as compared to their counterparts. CONCLUSIONS: Although our results do not support an association between preoperative characteristics-including symptomatology-and anatomic SSCD location, our findings do suggest that apical dehiscences are associated with greater postoperative symptomatic resolution.

Use of Tranexamic Acid for Elective Resection of Intracranial Neoplasms: A Systematic Review.

BACKGROUND: As an established antifibrinolytic agent, tranexamic acid (TXA) has garnered widespread use during surgery to limit intraoperative blood loss. In the field of neurosurgery, TXA is often introduced in cases of traumatic brain injury or elective spine surgeries; however, its role during elective cranial surgeries is not well established. We report a systematic review of the use of TXA in elective surgical resection of intracranial neoplasms. METHODS: We performed this systematic review following PRISMA guidelines to identify studies investigating the use of TXA in elective neurosurgical resection of intracranial neoplasms. Variables extracted included patient demographics, surgical indications, type of surgery performed, TXA dose and route of administration, operative duration, blood loss, transfusion rate, postoperative hemoglobin level, and complications. RESULTS: After careful screening, 4 articles (consisting of 682 patients) met our inclusion/exclusion criteria. The studies included 2 prospective cohort studies, 1 retrospective cohort study, and 1 case series. A χ2 test of pooled data demonstrated that patients administered TXA had a significantly decreased need for blood transfusions during surgery (odds ratio, 0.6273; 95% confidence interval, 0.4254-0.9251; P = 0.018). Mean total blood loss was 821.9 mL in the TXA group and 1099.0 mL in the control group across the studies. There was no significant difference in postoperative hemoglobin levels, with a mean of 11.4 g/dL in both the TXA and control groups. CONCLUSIONS: These results support the use of intraoperative TXA in tumor resection. However, its role in tumor resection has been less well investigated compared with its use in other areas of neurosurgery.

Structural Changes in the Cervicomedullary Junction in Adult Chiari Patients.

OBJECTIVE: To assess volumetric changes in the spinal cord at the cervicomedullary junction, diameter of the cervicomedullary cord, and width of the brainstem following posterior fossa decompression (PFD). METHODS: A retrospective analysis of adult patients with Chiari malformation who underwent PFD was performed. Segmentations were done on clinical quality T2-weighted cervical magnetic resonance images obtained before and after decompression using ITK-SNAP. Volumes of neural tissue within the cervicomedullary junction were evaluated from 10 mm cranial to the medullary beak to the cervical spinal cord at the level of the caudal endplate of the second cervical vertebra. The diameter of the cervicomedullary cord was calculated perpendicular to the spinal cord. The width of the brainstem was measured perpendicular to the clivus at the level of the basion. RESULTS: Twenty adult patients, a mean age of 49.55 years, were included. The cervical cord increased in volume by 13 mm3 to 338 mm3, with an average increase of 155 mm3 (P-value of 0.00002). The diameter of the cervicomedullary cord increased 10.30% 7 mm superior to the beak (P-value of 0.00074), 11.49% at the apex of the beak (P-value of 0.00082), 8.29% 7 mm inferior to the beak (P-value of 0.00075), and the brainstem increased 14.46% perpendicular to the clivus (P-value of 0.00109). The spinal cord at the inferior aspect of the C3 vertebra changed insignificantly (P-value of 0.10580). CONCLUSION: The volume of the cervical cord at the cervical-medullary junction, width of the cervicomedullary cord, and diameter of the brainstem increase following PFD.

The Role of Vancomycin Powder During Spinal Cord Stimulator Implantation: A Case Series and Review of the Literature.

OBJECTIVE: We examined the role of intrawound vancomycin powder as prophylaxis against postoperative surgical site infection (SSI) after spinal cord stimulator (SCS) implantation. METHODS: We performed a retrospective analysis of 153 consecutive patients who had undergone permanent SCS implantation surgery via open laminectomy between 2014 and 2020. We queried the patients' medical records for patient age, sex, relevant medical history, and whether intrawound vancomycin had been administered. We compared the rates of SSI (primary outcome) and seroma (secondary outcome) within 3 months after surgery between the vancomycin and no-vancomycin groups. Finally, we conducted multivariable logistic regression analyses to identify independent predictors of postoperative SSI or seroma. RESULTS: Of the 153 patients, 59% were women, and the average age was 65.4 years. Overall, 3 patients (2%) had developed an SSI: 2 (methicillin-resistant Staphylococcus aureus, Klebsiella) in the vancomycin group and 1 (methicillin-sensitive Staphylococcus aureus) in the no-vancomycin group. This difference in SSI rate between the 2 groups was insignificant (P = 0.73). Three seromas, all in the no-vancomycin group, accounted for a statistically significant difference in seroma formation between the 2 groups (P = 0.04). Multivariate logistic regression failed to identify any perioperative characteristics as independent predictors of postoperative SSI or seroma. CONCLUSIONS: Our experience suggests open laminectomy for SCS implantation surgery can be performed with a low postoperative SSI rate, with or without the use of powdered vancomycin. We found no evidence suggesting that the use of powdered vancomycin is unsafe or related to postoperative seroma formation. We failed to draw any definitive conclusions regarding its efficacy, despite referencing the largest single case series of SCS implantation to date.

Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

BACKGROUND: The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative. OBJECTIVE: To develop a machine-learning-based algorithm which accurately identifies patients as candidates for consultation with a spine surgeon, using only magnetic resonance imaging (MRI). METHODS: We trained a deep U-Net machine learning model to delineate spinal canals on axial slices of 100 normal lumbar MRI scans which were previously delineated by expert radiologists and neurosurgeons. We then tested the model against lumbar MRI scans for 140 patients who had undergone lumbar spine MRI at our institution (60 of whom ultimately underwent surgery, and 80 of whom did not). The model generated automated segmentations of the lumbar spinal canals and calculated a maximum degree of spinal stenosis for each patient, which served as our biomarker for surgical pathology warranting expert consultation. RESULTS: The machine learning model correctly predicted surgical candidacy (ie, whether patients ultimately underwent lumbar spinal decompression) with high accuracy (area under the curve = 0.88), using only imaging data from lumbar MRI scans. CONCLUSION: Automated interpretation of lumbar MRI scans was sufficient to correctly determine surgical candidacy in nearly 90% of cases. Given that a significant proportion of referrals placed for spine surgery evaluation fail to meet criteria for surgical intervention, our model could serve as a valuable tool for patient triage and thereby address some of the inefficiencies within the outpatient surgical referral process.

The 100 Most Influential Publications on Medulloblastoma: Areas of Past, Current, and Future Focus.

BACKGROUND: This article is the first to identify the most influential articles on medulloblastoma using the citation analysis methodology. OBJECTIVE: To perform a bibliometric analysis of the 100 most-cited articles on medulloblastoma. METHODS: Using the Web of Science database, search criteria included the title-specific keyword "medulloblastoma" OR "cerebellar primitive neuroectodermal tumor (PNET)" OR "cerebellar PNET." Publications from 1900 to 2020 labeled "article," "review," "data set," or "clinical trial" were chosen and ranked based on total number of citations in descending order. Each article was evaluated based on the following variables: total citations, average citations per year, first author, institution of first author, title, publication year, country of origin, SCImago Journal Rank, and Scopus SNIP (Source Normalized Impact per Paper). RESULTS: Our search yielded 4928 articles on medulloblastoma. The 100 most-cited articles ranged from 192 to 2017 across 42 unique journals; Journal of Clinical Oncology accounted for the most publications (16%). Paul A. Northcott was first author of the most articles on the list (n = 7.7%), and the most widely cited article was "Altered neural cell fates and medulloblastoma in mouse patched mutants" by Goodrich et al., published in Science (1997). CONCLUSIONS: Because medulloblastoma represents the most common form of pediatric cancerous brain tumor, it is important to identify works that have significantly contributed to the body of knowledge regarding this disease. The 100 most-cited medulloblastoma articles comprise a significant collection of data regarding the histopathologic and molecular classification of medulloblastoma as well as clinical outcomes of therapeutics used to treat this disease.

Eigenrank by committee: Von-Neumann entropy based data subset selection and failure prediction for deep learning based medical image segmentation.

Manual delineation of anatomy on existing images is the basis of developing deep learning algorithms for medical image segmentation. However, manual segmentation is tedious. It is also expensive because clinician effort is necessary to ensure correctness of delineation. Consequently most algorithm development is based on a tiny fraction of the vast amount of imaging data collected at a medical center. Thus, selection of a subset of images from hospital databases for manual delineation - so that algorithms trained on such data are accurate and tolerant to variation, becomes an important challenge. We address this challenge using a novel algorithm. The proposed algorithm named 'Eigenrank by Committee' (EBC) first computes the degree of disagreement between segmentations generated by each DL model in a committee. Then, it iteratively adds to the committee, a DL model trained on cases where the disagreement is maximal. The disagreement between segmentations is quantified by the maximum eigenvalue of a Dice coefficient disagreement matrix a measure closely related to the Von Neumann entropy. We use EBC for selecting data subsets for manual labeling from a larger database of spinal canal segmentations as well as intervertebral disk segmentations. U-Nets trained on these subsets are used to generate segmentations on the remaining data. Similar sized data subsets are also randomly sampled from the respective databases, and U-Nets are trained on these random subsets as well. We found that U-Nets trained using data subsets selected by EBC, generate segmentations with higher average Dice coefficients on the rest of the database than U-Nets trained using random sampling (p < 0.05 using t-tests comparing averages). Furthermore, U-Nets trained using data subsets selected by EBC generate segmentations with a distribution of Dice coefficients that demonstrate significantly (p < 0.05 using Bartlett's test) lower variance in comparison to U-Nets trained using random sampling for all datasets. We believe that this lower variance indicates that U-Nets trained with EBC are more robust than U-Nets trained with random sampling.

Ethical considerations and patient safety concerns for cancelling non-urgent surgeries during the COVID-19 pandemic: a review.

At the time of writing of this article, there have been over 110 million cases and 2.4 million deaths worldwide since the start of the Coronavirus Disease 2019 (COVID-19) pandemic, postponing millions of non-urgent surgeries. Existing literature explores the complexities of rationing medical care. However, implications of non-urgent surgery postponement during the COVID-19 pandemic have not yet been analyzed within the context of the four pillars of medical ethics. The objective of this review is to discuss the ethics of elective surgery cancellation during the COVID-19 pandemic in relation to beneficence, non-maleficence, justice, and autonomy. This review hypothesizes that a more equitable decision-making algorithm can be formulated by analyzing the ethical dilemmas of elective surgical care during the pandemic through the lens of these four pillars. This paper's analysis shows that non-urgent surgeries treat conditions that can become urgent if left untreated. Postponement of these surgeries can cause cumulative harm downstream. An improved algorithm can address these issues of beneficence by weighing local pandemic stressors within predictive algorithms to appropriately increase surgeries. Additionally, the potential harms of performing non-urgent surgeries extend beyond the patient. Non-maleficence is maintained through using enhanced screening protocols and modifying surgical techniques to reduce risks to patients and clinicians. This model proposes a system to transfer patients from areas of high to low burden, addressing the challenge of justice by considering facility burden rather than value judgments concerning the nature of a particular surgery, such as cosmetic surgeries. Autonomy can be respected by giving patients the option to cancel or postpone non-urgent surgeries. However, in the context of limited resources in a global pandemic, autonomy is not absolute. Non-urgent surgeries can ethically be postponed in opposition to the patient's preference. The proposed algorithm attempts to uphold the four principles of medical ethics in rationing non-urgent surgical care by building upon existing decision models, using additional measures of resource burden and surgical safety to increase health care access and decrease long-term harm as much as possible. The next global health crisis will undoubtedly present its own unique challenges. This model may serve as a comprehensive starting point in determining future guidelines for non-urgent surgical care.

Amygdala Structural Connectivity Is Associated With Impulsive Choice and Difficulty Quitting Smoking.

Introduction: The amygdala is known to play a role in mediating emotion and possibly addiction. We used probabilistic tractography (PT) to evaluate whether structural connectivity of the amygdala to the brain reward network is associated with impulsive choice and tobacco smoking. Methods: Diffusion and structural MRI scans were obtained from 197 healthy subjects (45 with a history of tobacco smoking) randomly sampled from the Human Connectome database. PT was performed to assess amygdala connectivity with several brain regions. Seed masks were generated, and statistical maps of amygdala connectivity were derived. Connectivity results were correlated with a subject performance both on a delayed discounting task and whether they met specified criteria for difficulty quitting smoking. Results: Amygdala connectivity was spatially segregated, with the strongest connectivity to the hippocampus, orbitofrontal cortex (OFC), and brainstem. Connectivity with the hippocampus was associated with preference for larger delayed rewards, whereas connectivity with the OFC, rostral anterior cingulate cortex (rACC), and insula were associated with preference for smaller immediate rewards. Greater nicotine dependence with difficulty quitting was associated with less hippocampal and greater brainstem connectivity. Scores on the Fagerstrom Test for Nicotine Dependence (FTND) correlated with rACC connectivity. Discussion: These findings highlight the importance of the amygdala-hippocampal-ACC network in the valuation of future rewards and substance dependence. These results will help to identify potential targets for neuromodulatory therapies for addiction and related disorders.

Visual Outcomes After Pituitary Surgery.

Visual signs and symptoms are a common manifestation of pituitary adenomas from compression or ischemia of the optic nerves and optic chiasm. Although bitemporal hemianopsia is a classic presenting visual field deficit, additional visual disturbances can result from these tumors. After endoscopic endonasal pituitary surgery, most patients have improvement in visual symptoms. Preoperative factors including retinal nerve fiber layer thickness, severity of preoperative deficit, duration of visual symptoms, tumor size, extent of resection, and patient age serve as possible predictors of postoperative visual outcomes.