Comparative analysis of the spatial distribution of brain metastases across several primary cancers using machine learning and deep learning models.

OBJECTIVE: Brain metastases (BM) are associated with poor prognosis and increased mortality rates, making them a significant clinical challenge. Studying BMs can aid in improving early detection and monitoring. Systematic comparisons of anatomical distributions of BM from different primary cancers, however, remain largely unavailable. METHODS: To test the hypothesis that anatomical BM distributions differ based on primary cancer type, we analyze the spatial coordinates of BMs for five different primary cancer types along principal component (PC) axes. The dataset includes 3949 intracranial metastases, labeled by primary cancer types and with six features. We employ PC coordinates to highlight the distinctions between various cancer types. We utilized different Machine Learning (ML) algorithms (RF, SVM, TabNet DL) models to establish the relationship between primary cancer diagnosis, spatial coordinates of BMs, age, and target volume. RESULTS: Our findings revealed that PC1 aligns most with the Y axis, followed by the Z axis, and has minimal correlation with the X axis. Based on PC1 versus PC2 plots, we identified notable differences in anatomical spreading patterns between Breast and Lung cancer, as well as Breast and Renal cancer. In contrast, Renal and Lung cancer, as well as Lung and Melanoma, showed similar patterns. Our ML and DL results demonstrated high accuracy in distinguishing BM distribution for different primary cancers, with the SVM algorithm achieving 97% accuracy using a polynomial kernel and TabNet achieving 96%. The RF algorithm ranked PC1 as the most important discriminating feature. CONCLUSIONS: In summary, our results support accurate multiclass ML classification regarding brain metastases distribution.

A quantitative characterization of the spatial distribution of brain metastases from breast cancer and respective molecular subtypes.

PURPOSE: Brain metastases (BM) remain a significant cause of morbidity and mortality in breast cancer (BC) patients. Specific factors promoting the process of BM and predilection for selected neuro-anatomical regions remain unknown, yet may have major implications for prevention or treatment. Anatomical spatial distributions of BM from BC suggest a predominance of metastases in the hindbrain and cerebellum. Systematic approaches to quantifying BM location or location-based analyses based on molecular subtypes, however, remain largely unavailable. METHODS: We analyzed stereotactic Cartesian coordinates derived from 134 patients undergoing gamma- knife radiosurgery (GKRS) for treatment of 407 breast cancer BMs to quantitatively study BM spatial distribution along principal component axes and by intrinsic molecular subtype (ER, PR, Herceptin). We used kernel density estimators (KDE) to highlight clustering and distribution regions in the brain, and we used the metric of mutual information (MI) to tease out subtle differences in the BM distributions associated with different molecular subtypes of BC. BM location maps according to vascular and anatomical distributions using Cartesian coordinates to aid in systematic classification of tumor locations were additionally developed. RESULTS: We corroborated that BC BMs show a consistent propensity to arise posteriorly and caudally, and that Her2+ tumors are relatively more likely to arise medially rather than laterally. To compare the distributions among varying BC molecular subtypes, the mutual information metric reveal that the ER-PR-Her2+ and ER-PR-Her2- subtypes show the smallest amount of mutual information and are most molecularly distinct. The kernel density contour plots show a propensity for triple negative BC to arise in more superiorly or cranially situated BMs. CONCLUSIONS: We present a novel and shareable workflow for characterizing and comparing spatial distributions of BM which may aid in identifying therapeutic or diagnostic targets and interactions with the tumor microenvironment. Further characterization of these patterns with larger multi-institutional data-sets may have major impacts on treatment or management of cancer patients.

Growth hormone secreting pituitary adenomas show distinct extrasellar extension patterns compared to nonfunctional pituitary adenomas.

PURPOSE: Patterns of extension of pituitary adenomas (PA) may vary according to PA subtype. Understanding extrasellar extension patterns in growth hormone PAs (GHPA) vis-a-vis nonfunctional PAs (NFPAs) may provide insights into the biology of GHPA and future treatment avenues. METHODS: Preoperative MR imaging (MRI) in 179 consecutive patients treated surgically for NFPA (n = 139) and GHPA (n = 40) were analyzed to determine patterns of extrasellar growth. Extension was divided into two principal directions: cranio-caudal (measured by infrasellar/suprasellar extension), and lateral cavernous sinus invasion (CSI) determined by Knosp grading score of 3-4. Suprasellar extension was defined as tumor extension superior to the tuberculum sellae- dorsum sellae line, and inferior extension as invasion through the sellar floor into the sphenoid sinus or clivus. Categorical analysis was performed using Fisher's exact test. RESULTS: GHPAs were overall more likely to remain purely intrasellar compared to NFPA (50% vs 26%, p < 0.001). GHPAs, however, were 7 times more likely to exhibit isolated infrasellar extension compared to NFPA (20% vs 2.8%, p = 0.001). Conversely, NFPAs were twice as likely to exhibit isolated suprasellar extension compared to GHPA (60% vs 28%, p < 0.001), as well as combined suprasellar/infrasellar extension (25% vs 3%, p = 0.011). There were no overall differences in CSI between the two subgroups. DISCUSSION: GHPA and NFPA demonstrate distinct extrasellar extension patterns on MRI. GHPAs show proclivity for inferior extension with bony invasion, whereas NFPAs are more likely to exhibit suprasellar extension through the diaphragmatic aperture. These distinctions may have implications into the biology and future treatment of PAs.

Code-free machine learning for object detection in surgical video: a benchmarking, feasibility, and cost study.

OBJECTIVE: While the utilization of machine learning (ML) for data analysis typically requires significant technical expertise, novel platforms can deploy ML methods without requiring the user to have any coding experience (termed AutoML). The potential for these methods to be applied to neurosurgical video and surgical data science is unknown. METHODS: AutoML, a code-free ML (CFML) system, was used to identify surgical instruments contained within each frame of endoscopic, endonasal intraoperative video obtained from a previously validated internal carotid injury training exercise performed on a high-fidelity cadaver model. Instrument-detection performances using CFML were compared with two state-of-the-art ML models built using the Python coding language on the same intraoperative video data set. RESULTS: The CFML system successfully ingested surgical video without the use of any code. A total of 31,443 images were used to develop this model; 27,223 images were uploaded for training, 2292 images for validation, and 1928 images for testing. The mean average precision on the test set across all instruments was 0.708. The CFML model outperformed two standard object detection networks, RetinaNet and YOLOv3, which had mean average precisions of 0.669 and 0.527, respectively, in analyzing the same data set. Significant advantages to the CFML system included ease of use, relatively low cost, displays of true/false positives and negatives in a user-friendly interface, and the ability to deploy models for further analysis with ease. Significant drawbacks of the CFML model included an inability to view the structure of the trained model, an inability to update the ML model once trained with new examples, and the inability for robust downstream analysis of model performance and error modes. CONCLUSIONS: This first report describes the baseline performance of CFML in an object detection task using a publicly available surgical video data set as a test bed. Compared with standard, code-based object detection networks, CFML exceeded performance standards. This finding is encouraging for surgeon-scientists seeking to perform object detection tasks to answer clinical questions, perform quality improvement, and develop novel research ideas. The limited interpretability and customization of CFML models remain ongoing challenges. With the further development of code-free platforms, CFML will become increasingly important across biomedical research. Using CFML, surgeons without significant coding experience can perform exploratory ML analyses rapidly and efficiently.

Robotic and robot-assisted skull base neurosurgery: systematic review of current applications and future directions.

OBJECTIVE: The utility of robotic instrumentation is expanding in neurosurgery. Despite this, successful examples of robotic implementation for endoscopic endonasal or skull base neurosurgery remain limited. Therefore, the authors performed a systematic review of the literature to identify all articles that used robotic systems to access the sella or anterior, middle, or posterior cranial fossae. METHODS: A systematic review of MEDLINE and PubMed in accordance with PRISMA guidelines performed for articles published between January 1, 1990, and August 1, 2021, was conducted to identify all robotic systems (autonomous, semiautonomous, or surgeon-controlled) used for skull base neurosurgical procedures. Cadaveric and human clinical studies were included. Studies with exclusively otorhinolaryngological applications or using robotic microscopes were excluded. RESULTS: A total of 561 studies were identified from the initial search, of which 22 were included following full-text review. Transoral robotic surgery (TORS) using the da Vinci Surgical System was the most widely reported system (4 studies) utilized for skull base and pituitary fossa procedures; additionally, it has been reported for resection of sellar masses in 4 patients. Seven cadaveric studies used the da Vinci Surgical System to access the skull base using alternative, non-TORS approaches (e.g., transnasal, transmaxillary, and supraorbital). Five cadaveric studies investigated alternative systems to access the skull base. Six studies investigated the use of robotic endoscope holders. Advantages to robotic applications in skull base neurosurgery included improved lighting and 3D visualization, replication of more traditional gesture-based movements, and the ability for dexterous movements ordinarily constrained by small operative corridors. Limitations included the size and angulation capacity of the robot, lack of drilling components preventing fully robotic procedures, and cost. Robotic endoscope holders may have been particularly advantageous when the use of a surgical assistant or second surgeon was limited. CONCLUSIONS: Robotic skull base neurosurgery has been growing in popularity and feasibility, but significant limitations remain. While robotic systems seem to have allowed for greater maneuverability and 3D visualization, their size and lack of neurosurgery-specific tools have continued to prevent widespread adoption into current practice. The next generation of robotic technologies should prioritize overcoming these limitations.

Neural network modeling for prediction of recurrence, progression, and hormonal non-remission in patients following resection of functional pituitary adenomas.

PURPOSE: Functional pituitary adenomas (FPAs) cause severe neuro-endocrinopathies including Cushing's disease (CD) and acromegaly. While many are effectively cured following FPA resection, some encounter disease recurrence/progression or hormonal non-remission requiring adjuvant treatment. Identification of risk factors for suboptimal postoperative outcomes may guide initiation of adjuvant multimodal therapies. METHODS: Patients undergoing endonasal transsphenoidal resection for CD, acromegaly, and mammosomatotroph adenomas between 1992 and 2019 were identified. Good outcomes were defined as hormonal remission without imaging/biochemical evidence of disease recurrence/progression, while suboptimal outcomes were defined as hormonal non-remission or MRI evidence of recurrence/progression despite adjuvant treatment. Multivariate regression modeling and multilayered neural networks (NN) were implemented. The training sets randomly sampled 60% of all FPA patients, and validation/testing sets were 20% samples each. RESULTS: 348 patients with mean age of 41.7 years were identified. Eighty-one patients (23.3%) reported suboptimal outcomes. Variables predictive of suboptimal outcomes included: Requirement for additional surgery in patients who previously had surgery and continue to have functionally active tumor (p = 0.0069; OR = 1.51, 95%CI 1.12-2.04), Preoperative visual deficit not improved after surgery (p = 0.0033; OR = 1.12, 95%CI 1.04-1.20), Transient diabetes insipidus (p = 0.013; OR = 1.27, 95%CI 1.05-1.52), Higher MIB-1/Ki-67 labeling index (p = 0.038; OR = 1.08, 95%CI 1.01-1.15), and preoperative low cortisol axis (p = 0.040; OR = 2.72, 95%CI 1.06-7.01). The NN had overall accuracy of 87.1%, sensitivity of 89.5%, specificity of 76.9%, positive predictive value of 94.4%, and negative predictive value of 62.5%. NNs for all FPAs were more robust than for CD or acromegaly/mammosomatotroph alone. CONCLUSION: We demonstrate capability of predicting suboptimal postoperative outcomes with high accuracy. NNs may aid in stratifying patients for risk of suboptimal outcomes, thereby guiding implementation of adjuvant treatment in high-risk patients.

A systematic review of virtual reality for the assessment of technical skills in neurosurgery.

OBJECTIVE: Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed. METHODS: A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted through MEDLINE and PubMed. Studies published in English between January 1990 and February 2021 describing the use of VR or AR to quantify surgical technical performance of neurosurgeons without the use of human raters were included. The types and categories of automated performance metrics (APMs) from each of these studies were recorded. RESULTS: Thirty-three VR studies were included in the review; no AR studies met inclusion criteria. VR APMs were categorized as either distance to target, force, kinematics, time, blood loss, or volume of resection. Distance and time were the most well-studied APM domains, although all domains were effective at differentiating surgeon experience levels. Distance was successfully used to track improvements with practice. Examining volume of resection demonstrated that attending surgeons removed less simulated tumor but preserved more normal tissue than trainees. More recently, APMs have been used in machine learning algorithms to predict level of training with a high degree of accuracy. Key limitations to enhanced-reality systems include limited AR usage for automated surgical assessment and lack of external and longitudinal validation of VR systems. CONCLUSIONS: VR has been used to assess surgeon performance across a wide spectrum of domains. The VR environment can be used to quantify surgeon performance, assess surgeon proficiency, and track training progression. AR systems have not yet been used to provide metrics for surgeon performance assessment despite potential for intraoperative integration. VR-based APMs may be especially useful for metrics that are difficult to assess intraoperatively, including blood loss and extent of resection.

Simulated outcomes for durotomy repair in minimally invasive spine surgery.

Minimally invasive spine surgery (MISS) is increasingly performed using endoscopic and microscopic visualization, and the captured video can be used for surgical education and development of predictive artificial intelligence (AI) models. Video datasets depicting adverse event management are also valuable, as predictive models not exposed to adverse events may exhibit poor performance when these occur. Given that no dedicated spine surgery video datasets for AI model development are publicly available, we introduce Simulated Outcomes for Durotomy Repair in Minimally Invasive Spine Surgery (SOSpine). A validated MISS cadaveric dural repair simulator was used to educate neurosurgery residents, and surgical microscope video recordings were paired with outcome data. Objects including durotomy, needle, grasper, needle driver, and nerve hook were then annotated. Altogether, SOSpine contains 15,698 frames with 53,238 annotations and associated durotomy repair outcomes. For validation, an AI model was fine-tuned on SOSpine video and detected surgical instruments with a mean average precision of 0.77. In summary, SOSpine depicts spine surgeons managing a common complication, providing opportunities to develop surgical AI models.

Endoscopic Endonasal Transsphenoidal Resection of Pituitary Adenomas in Patients Presenting With Monocular Blindness.

BACKGROUND AND OBJECTIVES: Suprasellar tumors, particularly pituitary adenomas (PAs), commonly present with visual decline, and the endoscopic endonasal transsphenoidal approach (EETA) is the primary management for optic apparatus decompression. Patients presenting with complete preoperative monocular blindness comprise a high-risk subgroup, given concern for complete blindness. This retrospective cohort study evaluates outcomes after EETA for patients with PA presenting with monocular blindness. METHODS: Retrospective analysis of all EETA cases at our institution from June 2012 to August 2023 was performed. Inclusion criteria included adults with confirmed PA and complete monocular blindness, defined as no light perception, and a relative afferent pupillary defect secondary to tumor mass effect. RESULTS: Our cohort includes 15 patients (9 males, 6 females), comprising 2.4% of the overall PA cohort screened. The mean tumor diameter was 3.8 cm, with 6 being giant PAs (>4 cm). The mean duration of preoperative monocular blindness was 568 days. Additional symptoms included contralateral visual field defects (n = 11) and headaches (n = 10). Two patients presented with subacute PA apoplexy. Gross total resection was achieved in 46% of patients, reflecting tumor size and invasiveness. Postoperatively, 2 patients experienced improvement in their effectively blind eye and 2 had improved visual fields of the contralateral eye. Those with improvements were operated within 10 days of presentation, and no patients experienced worsened vision. CONCLUSION: This is the first series of EETA outcomes in patients with higher-risk PA with monocular blindness on presentation. In these extensive lesions, vision remained stable for most without further decline and improvement from monocular blindness was observed in a small subset of patients with no light perception and relative afferent pupillary defect. Timing from vision loss to surgical intervention seemed to be associated with improvement. From a surgical perspective, caution is warranted to protect remaining vision and we conclude that EETA is safe in the management of these patients.

Pilot Analysis of Surgeon Instrument Utilization Signatures Based on Shannon Entropy and Deep Learning for Surgeon Performance Assessment in a Cadaveric Carotid Artery Injury Control Simulation.

BACKGROUND AND OBJECTIVES: Assessment and feedback are critical to surgical education, but direct observational feedback by experts is rarely provided because of time constraints and is typically only qualitative. Automated, video-based, quantitative feedback on surgical performance could address this gap, improving surgical training. The authors aim to demonstrate the ability of Shannon entropy (ShEn), an information theory metric that quantifies series diversity, to predict surgical performance using instrument detections generated through deep learning. METHODS: Annotated images from a publicly available video data set of surgeons managing endoscopic endonasal carotid artery lacerations in a perfused cadaveric simulator were collected. A deep learning model was implemented to detect surgical instruments across video frames. ShEn score for the instrument sequence was calculated from each surgical trial. Logistic regression using ShEn was used to predict hemorrhage control success. RESULTS: ShEn scores and instrument usage patterns differed between successful and unsuccessful trials (ShEn: 0.452 vs 0.370, P < .001). Unsuccessful hemorrhage control trials displayed lower entropy and less varied instrument use patterns. By contrast, successful trials demonstrated higher entropy with more diverse instrument usage and consistent progression in instrument utilization. A logistic regression model using ShEn scores (78% accuracy and 97% average precision) was at least as accurate as surgeons' attending/resident status and years of experience for predicting trial success and had similar accuracy as expert human observers. CONCLUSION: ShEn score offers a summative signal about surgeon performance and predicted success at controlling carotid hemorrhage in a simulated cadaveric setting. Future efforts to generalize ShEn to additional surgical scenarios can further validate this metric.

From white to blue light: evolution of endoscope-assisted intracranial tumor neurosurgery and expansion to intraaxial tumors.

OBJECTIVE: Intraoperative use of the endoscope to assist in visualization of intracranial tumor pathology has expanded with increasing surgeon experience and improved instrumentation. The authors aimed to study how advancements in endoscopic technology have affected the evolution of endoscope use, with particular focus on blue light-filter modification allowing for discrimination of fluorescent tumor tissue following 5-ALA administration. METHODS: A retrospective analysis of patients undergoing craniotomy for tumor resection at a single institution between February 2012 and July 2021 was performed. Patients were included if the endoscope was used for diagnostic tumor cavity inspection or therapeutic assistance with tumor resection following standard craniotomy and microsurgical tumor resection, with emphasis on those cases in which blue light endoscopy was used. Medical records were queried for patient demographics, operative reports describing the use of the endoscope and extent of resection, associations with tumor pathology, and postoperative outcomes. Preoperative and postoperative MR images were reviewed for radiographic extent of resection. RESULTS: A total of 52 patients who underwent endoscope-assisted craniotomy for tumor were included. Thirty patients (57.7%) were men and the average age was 52.6 ± 16.1 years. Standard white light endoscopes were used for assistance with tumor resection in 28 cases (53.8%) for tumors primarily located in the ventricular system, parasellar region, and cerebellopontine angle. A blue light endoscope for detection of 5-ALA fluorescence was introduced into our practice in 2014 and subsequently used for assistance with tumor resection in 24 cases (46.2%) (intraaxial: n = 22, extraaxial: n = 2). Beyond the use of the surgical microscope as the primary visualization source, the blue light endoscope was used to directly perform additional tumor resection in 19/21 cases as a result of improved fluorescence detection as compared to the surgical microscope. No complications were associated with the use of the endoscope or with additional resection performed under white or blue light visualization. CONCLUSIONS: Endoscopic assistance to visualize intracranial tumors had previously been limited to white light, assisting mostly in the visualization of extraaxial tumors confined to intraventricular and cisternal compartments. Blue light-equipped endoscopes provide improved versatility and visualization of 5-ALA fluorescing tissue beyond the capability of the surgical microscope, thereby expanding its use into the realm of intraaxial tumor resections.

GPT-4 Artificial Intelligence Model Outperforms ChatGPT, Medical Students, and Neurosurgery Residents on Neurosurgery Written Board-Like Questions.

BACKGROUND: Artificial intelligence (AI) and machine learning have transformed health care with applications in various specialized fields. Neurosurgery can benefit from artificial intelligence in surgical planning, predicting patient outcomes, and analyzing neuroimaging data. GPT-4, an updated language model with additional training parameters, has exhibited exceptional performance on standardized exams. This study examines GPT-4's competence on neurosurgical board-style questions, comparing its performance with medical students and residents, to explore its potential in medical education and clinical decision-making. METHODS: GPT-4's performance was examined on 643 Congress of Neurological Surgeons Self-Assessment Neurosurgery Exam (SANS) board-style questions from various neurosurgery subspecialties. Of these, 477 were text-based and 166 contained images. GPT-4 refused to answer 52 questions that contained no text. The remaining 591 questions were inputted into GPT-4, and its performance was evaluated based on first-time responses. Raw scores were analyzed across subspecialties and question types, and then compared to previous findings on Chat Generative pre-trained transformer performance against SANS users, medical students, and neurosurgery residents. RESULTS: GPT-4 attempted 91.9% of Congress of Neurological Surgeons SANS questions and achieved 76.6% accuracy. The model's accuracy increased to 79.0% for text-only questions. GPT-4 outperformed Chat Generative pre-trained transformer (P < 0.001) and scored highest in pain/peripheral nerve (84%) and lowest in spine (73%) categories. It exceeded the performance of medical students (26.3%), neurosurgery residents (61.5%), and the national average of SANS users (69.3%) across all categories. CONCLUSIONS: GPT-4 significantly outperformed medical students, neurosurgery residents, and the national average of SANS users. The mode's accuracy suggests potential applications in educational settings and clinical decision-making, enhancing provider efficiency, and improving patient care.

Anatomic Considerations Guiding Single Versus Multiportal Endoscopic Approaches for Resection of Juvenile Nasopharyngeal Angiofibroma: Cases Series With Graded Multicorridor Resections.

BACKGROUND: Juvenile nasopharyngeal angiofibromas (JNAs) are characterized by expansive and destructive growth, often invading the midline/paranasal sinuses, pterygopalatine fossa, and infratemporal fossa and can extend into the orbit, cavernous sinus, or intracranially. OBJECTIVE: To evaluete the major benefits of the extended endoscopic endonasal approach (EEA) for JNA resection as compared with more traditional and invasive transpalatal and transfacial approaches. When JNAs extend into lateral anatomic compartments, the optimal operative trajectory often requires additional approach strategies or surgical staging. METHODS: We retrospectively reviewed 8 cases of large JNAs arising in symptomatic adolescent boys (University of Pittsburgh Medical Center Stages II, III, and V) and discuss anatomic and tumor considerations guiding the decision of a pure EEA vs combined EEA and sublabial transmaxillary approach (Caldwell-Luc). RESULTS: A pure extended EEA was used in 6 JNA cases (UPMC Stages II-III); a multiportal EEA + Caldwell-Luc maxillotomy was used in 2 cases. One of the 2 patients (UPMC Stage V) previously treated with multiportal EEA + Caldwell-Luc maxillotomy underwent staged left temporal/transzygomatic craniotomy, obtaining gross total resection. Seven patients ultimately underwent complete removal without recurrence. One patient with a small residual JNA (UPMC II) underwent stereotactic radiosurgery without progression to date. CONCLUSION: JNAs with lateral extension into the infratemporal fossa often benefited from additional lateral exposure using a Caldwell-Luc maxillotomy. Cases with significant skull base and/or dural involvement may undergo staged surgical treatment; temporalis + transzygomatic craniotomy is often useful for second-stage approaches for residual tumor in these lateral infratemporal or intracranial regions. SRS should be considered for residual tumor if additional surgery is not warranted.

Validation of Myc-Associated Protein X (MAX) regulation in growth hormone secreting and nonfunctional pituitary adenoma.

INTRODUCTION: Many patients with growth hormone-secreting pituitary adenoma (GHPA) fail to achieve biochemical remission, warranting investigation into epigenetic and molecular signatures associated with tumorigenesis and hormonal secretion. Prior work exploring the DNA methylome showed Myc-Associated Protein X (MAX), a transcription factor involved in cell cycle regulation, was differentially methylated between GHPA and nonfunctional pituitary adenoma (NFPA). We aimed to validate the differential DNA methylation and related MAX protein expression profiles between NFPA and GHPA. METHODS: DNA methylation levels were measured in 52 surgically resected tumors (37 NFPA, 15 GHPA) at ~100,000 known MAX binding sites derived using ChIP-seq analysis from ENCODE. Findings were correlated with MAX protein expression using a constructed tissue microarray (TMA). Gene ontology analysis was performed to explore downstream genetic and signaling pathways regulated by MAX. RESULTS: GHPA had more hypomethylation events across all known MAX binding sites. Of binding sites defined using ChIP-seq analysis, 1,551 sites had significantly different methylation patterns between the two cohorts; 432 occurred near promoter regions potentially regulated by MAX, including promoters of TNF and MMP9. Gene ontology analysis suggested enrichment in genes involved in oxygen response, immune system regulation, and cell proliferation. Thirteen MAX binding sites were within coding regions of genes. GHPA demonstrated significantly increased expression of MAX protein compared to NFPA. CONCLUSION: GHPA have significantly different DNA methylation and downstream protein expression levels of MAX compared to NFPA. These differences may influence mechanisms involved with cellular proliferation, tumor invasion and hormonal secretion.

Silent Corticotroph Staining Pituitary Neuroendocrine Tumors: Prognostic Significance in Radiosurgery.

BACKGROUND AND OBJECTIVES: There is conflicting evidence on the significance of adrenocorticotrophic hormone (ACTH) staining in the prognosis of nonfunctioning pituitary neuroendocrine tumors (NFpitNETs). The objective of this study was to define the effect of ACTH immunostaining on clinical and radiographic outcomes of stereotactic radiosurgery (SRS) for NFpitNETs. METHODS: This retrospective, multicenter study included patients managed with SRS for NFpitNET residuals. The patients were divided into 2 cohorts: (1) silent corticotroph (SC) for NFpitNETs with positive ACTH immunostaining and (2) non-SC NFpitNETs. Rates of local tumor control and the incidence of post-treatment pituitary and neurological dysfunction were documented. Factors associated with radiological and clinical outcomes were also analyzed. RESULTS: The cohort included 535 patients from 14 centers with 84 (15.7%) patients harboring silent corticotroph NFpitNETs (SCs). At last follow-up, local tumor progression occurred in 11.9% of patients in the SC compared with 8.1% of patients in the non-SC cohort (P = .27). No statistically significant difference was noted in new-onset hypopituitarism rates (10.7% vs 15.4%, P = .25) or visual deficits (3.6% vs 1.1%, P = .088) between the 2 cohorts at last follow-up. When controlling for residual tumor volume, maximum dose, and patient age and sex, positive ACTH immunostaining did not have a significant correlation with local tumor progression (hazard ratio = 1.69, 95% CI = 0.8-3.61, P = .17). CONCLUSION: In contemporary radiosurgical practice with a single fraction dose of 8-25 Gy (median 15 Gy), ACTH immunostaining in NFpitNETs did not appear to confer a significantly reduced rate of local tumor control after SRS.

Disparities in the Surgical Treatment of Cerebrovascular Pathologies: A Contemporary Systematic Review.

INTRODUCTION: This systematic review analyzes contemporary literature on racial/ethnic, insurance, and socioeconomic disparities within cerebrovascular surgery in the United States to determine areas for improvement. METHODS: We conducted an electronic database search of literature published between January 1990 and July 2020 using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for studies analyzing a racial/ethnic, insurance, or socioeconomic disparity within adult cerebrovascular surgery. RESULTS: Of 2873 articles screened for eligibility by title and abstract, 970 underwent full-text independent review by 3 authors. Twenty-seven additional articles were identified through references to generate a final list of 47 included studies for analysis. Forty-six were retrospective reviews and 1 was a prospective observational cohort study, thereby comprising Levels III and IV of evidence. Studies investigated carotid artery stenting (11/47, 23%), carotid endarterectomy (22/47, 46.8%), mechanical thrombectomy (8/47, 17%), and endovascular aneurysm coiling or surgical aneurysm clipping (20/47, 42.6%). Minority and underinsured patients were less likely to receive surgical treatment. Non-White patients were more likely to experience a postoperative complication, although this significance was lost in some studies using multivariate analyses to account for complication risk factors. White and privately insured patients generally experienced shorter length of hospital stay, had lower rates of in-hospital mortality, and underwent routine discharge. Twenty-five papers (53%) reported no disparities within at least one examined metric. CONCLUSIONS: This comprehensive contemporary systematic review demonstrates the existence of disparity gaps within the field of adult cerebrovascular surgery. It highlights the importance of continued investigation into sources of disparity and efforts to promote equity within the field.

Systematic Review of Racial, Socioeconomic, and Insurance Status Disparities in Neurosurgical Care for Intracranial Tumors.

BACKGROUND: The impact of race, socioeconomic status (SES), insurance status, and other social metrics on the outcomes of patients with intracranial tumors has been reported in several studies. However, these findings have not been comprehensively summarized. METHODS: We conducted a PRISMA systematic review of all published articles between 1990 and 2020 that analyzed intracranial tumor disparities, including race, SES, insurance status, and safety-net hospital status. Outcomes measured include access, standards of care, receipt of surgery, extent of resection, mortality, complications, length of stay (LOS), discharge disposition, readmission rate, and hospital charges. RESULTS: Fifty-five studies were included. Disparities in mortality were reported in 27 studies (47%), showing minority status and lower SES associated with poorer survival outcomes in 14 studies (52%). Twenty-seven studies showed that African American patients had worse outcomes across all included metrics including mortality, rates of surgical intervention, extent of resection, LOS, discharge disposition, and complication rates. Thirty studies showed that privately insured patients and patients with higher SES had better outcomes, including lower mortality, complication, and readmission rates. Six studies showed that worse outcomes were associated with treatment at safety-net and/or low-volume hospitals. The influence of Medicare or Medicaid status, or inequities affecting other minorities, was less clearly delineated. Ten studies (18%) were negative for evidence of disparities. CONCLUSIONS: Significant disparities exist among patients with intracranial tumors, particularly affecting patients of African American race and lower SES. Efforts at the hospital, state, and national level must be undertaken to identify root causes of these issues.

Systematic Review of Racial, Socioeconomic, and Insurance Status Disparities in the Treatment of Pediatric Neurosurgical Diseases in the United States.

BACKGROUND: Increasing light is being shed on how race, insurance, and socioeconomic status (SES) may be related to outcomes from disease in the United States. To better understand the impact of these health care disparities in pediatric neurosurgery, we performed a systematic review of the literature. METHODS: We conducted a systematic review using PRISMA guidelines and MeSH terms involving neurosurgical conditions and racial, ethnic, and SES disparities. Three independent reviewers screened articles and analyzed texts selected for full analysis. RESULTS: Thirty-eight studies were included in the final analysis, of which all but 2 were retrospective database reviews. Thirty-four studies analyzed race, 22 analyzed insurance status, and 13 analyzed SES/income. Overall, nonwhite patients, patients with public insurance, and patients from lower SES were shown to have reduced access to treatment and greater rates of adverse outcomes. Nonwhite patients were more likely to present at an older age with more severe disease, less likely to undergo surgery at a high-volume surgical center, and more likely to experience postoperative morbidity and mortality. Underinsured and publicly insured patients were more likely to experience delay in surgical referral, less likely to undergo surgical treatment, and more likely to experience inpatient mortality. CONCLUSIONS: Health care disparities are present within multiple populations of patients receiving pediatric neurosurgical care. This review highlights the need for continued investigation into identifying and addressing health care disparities in pediatric neurosurgery patients.

Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset of these events, surgeons do not know how much blood will be lost or whether they will successfully control the hemorrhage (achieve hemostasis). We evaluate the ability of a deep learning neural network (DNN) to predict hemostasis control ability using the first minute of surgical video and compare model performance with human experts viewing the same video. The publicly available SOCAL dataset contains 147 videos of attending and resident surgeons managing hemorrhage in a validated, high-fidelity cadaveric simulator. Videos are labeled with outcome and blood loss (mL). The first minute of 20 videos was shown to four, blinded, fellowship trained skull-base neurosurgery instructors, and to SOCALNet (a DNN trained on SOCAL videos). SOCALNet architecture included a convolutional network (ResNet) identifying spatial features and a recurrent network identifying temporal features (LSTM). Experts independently assessed surgeon skill, predicted outcome and blood loss (mL). Outcome and blood loss predictions were compared with SOCALNet. Expert inter-rater reliability was 0.95. Experts correctly predicted 14/20 trials (Sensitivity: 82%, Specificity: 55%, Positive Predictive Value (PPV): 69%, Negative Predictive Value (NPV): 71%). SOCALNet correctly predicted 17/20 trials (Sensitivity 100%, Specificity 66%, PPV 79%, NPV 100%) and correctly identified all successful attempts. Expert predictions of the highest and lowest skill surgeons and expert predictions reported with maximum confidence were more accurate. Experts systematically underestimated blood loss (mean error - 131 mL, RMSE 350 mL, R2 0.70) and fewer than half of expert predictions identified blood loss > 500 mL (47.5%, 19/40). SOCALNet had superior performance (mean error - 57 mL, RMSE 295 mL, R2 0.74) and detected most episodes of blood loss > 500 mL (80%, 8/10). In validation experiments, SOCALNet evaluation of a critical on-screen surgical maneuver and high/low-skill composite videos were concordant with expert evaluation. Using only the first minute of video, experts and SOCALNet can predict outcome and blood loss during surgical hemorrhage. Experts systematically underestimated blood loss, and SOCALNet had no false negatives. DNNs can provide accurate, meaningful assessments of surgical video. We call for the creation of datasets of surgical adverse events for quality improvement research.

Deep Neural Networks Can Accurately Detect Blood Loss and Hemorrhage Control Task Success From Video.

BACKGROUND: Deep neural networks (DNNs) have not been proven to detect blood loss (BL) or predict surgeon performance from video. OBJECTIVE: To train a DNN using video from cadaveric training exercises of surgeons controlling simulated internal carotid hemorrhage to predict clinically relevant outcomes. METHODS: Video was input as a series of images; deep learning networks were developed, which predicted BL and task success from images alone (automated model) and images plus human-labeled instrument annotations (semiautomated model). These models were compared against 2 reference models, which used average BL across all trials as its prediction (control 1) and a linear regression with time to hemostasis (a metric with known association with BL) as input (control 2). The root-mean-square error (RMSE) and correlation coefficients were used to compare the models; lower RMSE indicates superior performance. RESULTS: One hundred forty-three trials were used (123 for training and 20 for testing). Deep learning models outperformed controls (control 1: RMSE 489 mL, control 2: RMSE 431 mL, R2 = 0.35) at BL prediction. The automated model predicted BL with an RMSE of 358 mL (R2 = 0.4) and correctly classified outcome in 85% of trials. The RMSE and classification performance of the semiautomated model improved to 260 mL and 90%, respectively. CONCLUSION: BL and task outcome classification are important components of an automated assessment of surgical performance. DNNs can predict BL and outcome of hemorrhage control from video alone; their performance is improved with surgical instrument presence data. The generalizability of DNNs trained on hemorrhage control tasks should be investigated.