Increased Risk of Recurrent Stroke in Symptomatic Large Vessel Disease With Impaired BOLD Cerebrovascular Reactivity.

BACKGROUND: Impaired cerebrovascular reactivity (CVR) has been correlated with recurrent ischemic stroke. However, for clinical purposes, most CVR techniques are rather complex, time-consuming, and lack validation for quantitative measurements. The recent adaptation of a standardized hypercapnic stimulus in combination with a blood-oxygenation-level-dependent (BOLD) magnetic resonance imaging signal as a surrogate for cerebral blood flow offers a potential universally comparable CVR assessment. We investigated the association between impaired BOLD-CVR and risk for recurrent ischemic events. METHODS: We conducted a retrospective analysis of patients with symptomatic cerebrovascular large vessel disease who had undergone a prospective hypercapnic-challenged BOLD-CVR protocol at a single tertiary stroke referral center between June 2014 and April 2020. These patients were followed up for recurrent acute ischemic events for up to 3 years. BOLD-CVR (%BOLD signal change per mm Hg CO2) was calculated on a voxel-by-voxel basis. Impaired BOLD-CVR of the affected (ipsilateral to the vascular pathology) hemisphere was defined as an average BOLD-CVR, falling 2 SD below the mean BOLD-CVR of the right hemisphere in a healthy age-matched reference cohort (n=20). Using a multivariate Cox proportional hazards model, the association between impaired BOLD-CVR and ischemic stroke recurrence was assessed and Kaplan-Meier survival curves to visualize the acute ischemic stroke event rate. RESULTS: Of 130 eligible patients, 28 experienced recurrent strokes (median, 85 days, interquartile range, 5-166 days). Risk factors associated with an increased recurrent stroke rate included impaired BOLD-CVR, a history of atrial fibrillation, and heart insufficiency. After adjusting for sex, age group, and atrial fibrillation, impaired BOLD-CVR exhibited a hazard ratio of 10.73 (95% CI, 4.14-27.81; P<0.001) for recurrent ischemic stroke. CONCLUSIONS: Among patients with symptomatic cerebrovascular large vessel disease, those exhibiting impaired BOLD-CVR in the affected hemisphere had a 10.7-fold higher risk of recurrent ischemic stroke events compared with individuals with nonimpaired BOLD-CVR.

Role of microsurgical tumor burden reduction in patients with breast cancer brain metastases considering molecular subtypes: a two-center volumetric survival analysis.

BACKGROUND: Advancements in metastatic breast cancer (BC) treatment have enhanced overall survival (OS), leading to increased rates of brain metastases (BM). This study analyzes the association between microsurgical tumor reduction and OS in patients with BCBM, considering tumor molecular subtypes and perioperative treatment approaches. METHODS: Retrospective analysis of surgically treated patients with BCBM from two tertiary brain tumor Swiss centers. The association of extent of resection (EOR), gross-total resection (GTR) achievement, and postoperative residual tumor volume (RV) with OS and intracranial progression-free survival (IC-PFS) was evaluated using Cox proportional hazard model. RESULTS: 101 patients were included in the final analysis, most patients (38%) exhibited HER2-/HR + BC molecular subtype, followed by HER2 + /HR + (25%), HER2-/HR- (21%), and HER2 + /HR- subtypes (13%). The majority received postoperative systemic treatment (75%) and radiotherapy (84%). Median OS and intracranial PFS were 22 and 8 months, respectively. The mean pre-surgery intracranial tumor volume was 26 cm3, reduced to 3 cm3 post-surgery. EOR, GTR achievement and RV were not significantly associated with OS or IC-PFS, but higher EOR and lower RV correlated with extended OS in patients without extracranial metastases. HER2-positive tumor status was associated with longer OS, extracranial metastases at BM diagnosis and symptomatic lesions with shorter OS and IC-PFS. CONCLUSIONS: Our study found that BC molecular subtypes, extracranial disease status, and BM-related symptoms were associated with OS in surgically treated patients with BCBM. Additionally, while extensive resection to minimize residual tumor volume did not significantly affect OS across the entire cohort, it appeared beneficial for patients without extracranial metastases.

Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning.

OBJECTIVES: The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). METHODS: Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s-indicating presence of OFI. K-means clustering-an unsupervised ML algorithm-was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. RESULTS: We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI-with female gender, lower body mass index, and less smokers as Type I hallmarks. CONCLUSIONS: Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics.

Safety of microneurosurgical interventions for superficial and deep-seated brain metastases: single-center cohort study of 637 consecutive cases.

PURPOSE: Microneurosurgical techniques have greatly improved over the past years due to the introduction of new technology and surgical concepts. To reevaluate the role of micro-neurosurgery in brain metastases (BM) resection in the era of new systemic and local treatment options, its safety profile needs to be reassessed. The aim of this study was to analyze the rate of adverse events (AEs) according to a systematic, comprehensive and reliably reproducible grading system after microneurosurgical BM resection in a large and modern microneurosurgical series with special emphasis on anatomical location. METHODS: Prospectively collected cases of BM resection between 2013 and 2022 were retrospectively analyzed. Number of AEs, defined as any deviations from the expected postoperative course according to Clavien-Dindo-Grade (CDG) were evaluated. Patient, surgical, and lesion characteristics, including exact anatomic tumor locations, were analyzed using uni- and multivariate logistic regression and survival analysis to identify predictive factors for AEs. RESULTS: We identified 664 eligible patients with lung cancer being the most common primary tumor (44%), followed by melanoma (25%) and breast cancer (11%). 29 patients (4%) underwent biopsy only whereas BM were resected in 637 (96%) of cases. The overall rate of AEs was 8% at discharge. However, severe AEs (≥ CDG 3a; requiring surgical intervention under local/general anesthesia or ICU treatment) occurred in only 1.9% (n = 12) of cases with a perioperative mortality of 0.6% (n = 4). Infratentorial tumor location (OR 5.46, 95% 2.31-13.8, p = .001), reoperation (OR 2.31, 95% 1.07-4.81, p = .033) and central region tumor location (OR 3.03, 95% 1.03-8.60) showed to be significant predictors in a multivariate analysis for major AEs (CDG ≥ 2 or new neurological deficits). Neither deep supratentorial nor central region tumors were associated with more major AEs compared to convexity lesions. CONCLUSIONS: Modern microneurosurgical resection can be considered an excellent option in the management of BM in terms of safety, as the overall rate of major AEs are very rare even in eloquent and deep-seated lesions.

Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge.

OBJECTIVES: Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this feasibility study, we employed blood oxygenation level-dependent (BOLD) MRI with standardized and precise carbon dioxide (CO2) and oxygen (O2) modulation to investigate specific tumor tissue response patterns in patients with newly diagnosed glioblastoma. MATERIALS AND METHODS: Seven newly diagnosed untreated patients with suspected glioblastoma were prospectively included to undergo a BOLD study with combined CO2 and O2 standardized protocol. %BOLD signal change/mmHg during hypercapnic, hypoxic, and hyperoxic stimulus was calculated in the whole brain, tumor lesion and segmented volumes of interest (VOI) [contrast-enhancing (CE) - tumor, necrosis and edema] to analyze their tissue response patterns. RESULTS: Quantification of BOLD signal change after gas challenges can be used to identify specific responses to standardized stimuli in glioblastoma patients. Integration of this approach with automatic VOI segmentation grants improved characterization of tumor subzones and edema. Magnitude of BOLD signal change during the 3 stimuli can be visualized at voxel precision through color-coded maps overlayed onto whole brain and identified VOIs. CONCLUSIONS: Our preliminary investigation shows good feasibility of BOLD with standardized and precise CO2 and O2 modulation as an emerging physiologic imaging technique to detail specific glioblastoma characteristics. The unique tissue response patterns generated can be further investigated to better detail glioblastoma lesions and gauge treatment response.

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease.

BACKGROUND: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. METHODS: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. RESULTS: Models were developed and integrated into a web-app ( https://neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59-0.74], back pain (0.72, 95%CI: 0.64-0.79), and leg pain (0.64, 95%CI: 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. CONCLUSIONS: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population.

Machine Learning in Pituitary Surgery.

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management.

Foundations of Feature Selection in Clinical Prediction Modeling.

Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive performance by explaining a large proportion of the variance within the dependent variable must be balanced. With this aim, one must consider the clinical setting and what data are readily available to clinicians at specific timepoints, as well as more obvious aspects such as the availability of computational power and size of the training dataset. This chapter elucidates the importance and pitfalls in feature selection, focusing on applications in clinical prediction modeling. We demonstrate simple methods such as correlation-, significance-, and variable importance-based filtering, as well as intrinsic feature selection methods such as Lasso and tree- or rule-based methods. Finally, we focus on two algorithmic wrapper methods for feature selection that are commonly used in machine learning: Recursive Feature Elimination (RFE), which can be applied regardless of data and model type, as well as Purposeful Variable Selection as described by Hosmer and Lemeshow, specifically for generalized linear models.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Machine Learning Algorithms in Neuroimaging: An Overview.

Machine learning (ML) and artificial intelligence (AI) applications in the field of neuroimaging have been on the rise in recent years, and their clinical adoption is increasing worldwide. Deep learning (DL) is a field of ML that can be defined as a set of algorithms enabling a computer to be fed with raw data and progressively discover-through multiple layers of representation-more complex and abstract patterns in large data sets. The combination of ML and radiomics, namely the extraction of features from medical images, has proven valuable, too: Radiomic information can be used for enhanced image characterization and prognosis or outcome prediction. This chapter summarizes the basic concepts underlying ML application for neuroimaging and discusses technical aspects of the most promising algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), in order to provide the readership with the fundamental theoretical tools to better understand ML in neuroimaging. Applications are highlighted from a practical standpoint in the last section of the chapter, including: image reconstruction and restoration, image synthesis and super-resolution, registration, segmentation, classification, and outcome prediction.

Impact of intraoperative magnetic resonance imaging on gross total resection, extent of resection, and residual tumor volume in pituitary surgery: systematic review and meta-analysis.

BACKGROUND: Residual tumor tissue after pituitary adenoma surgery, is linked with additional morbidity and mortality. Intraoperative magnetic resonance imaging (ioMRI) could improve resection. We aim to assess the improvement in gross total resection (GTR), extent of resection (EOR), and residual tumor volume (RV) achieved using ioMRI. METHODS: A systematic review was carried out on PubMed/MEDLINE to identify any studies reporting intra- and postoperative (1) GTR, (2) EOR, or (3) RV in patients who underwent resection of pituitary adenomas with ioMRI. Random effects meta-analysis of the rate of improvement after ioMRI for these three surgical outcomes was intended. RESULTS: Among 34 included studies (2130 patients), the proportion of patients with conversion to GTR (∆GTR) after ioMRI was 0.19 (95% CI 0.15-0.23). Mean ∆EOR was + 9.07% after ioMRI. Mean ∆RV was 0.784 cm3. For endoscopically treated patients, ∆GTR was 0.17 (95% CI 0.09-0.25), while microscopic ∆GTR was 0.19 (95% CI 0.15-0.23). Low-field ioMRI studies demonstrated a ∆GTR of 0.19 (95% CI 0.11-0.28), while high-field and ultra-high-field ioMRI demonstrated a ∆GTR of 0.19 (95% CI 0.15-0.24) and 0.20 (95% CI 0.13-0.28), respectively. CONCLUSIONS: Our meta-analysis demonstrates that around one fifth of patients undergoing pituitary adenoma resection convert from non-GTR to GTR after the use of ioMRI. EOR and RV can also be improved to a certain extent using ioMRI. Endoscopic versus microscopic technique or field strength does not appear to alter the impact of ioMRI. Statistical heterogeneity was high, indicating that the improvement in surgical results due to ioMRI varies considerably by center.

Comparison between VII-to-VII and XII-to-VII coaptation techniques for early facial nerve reanimation after surgical intra-cranial injuries: a systematic review and pooled analysis of the functional outcomes.

The surgical injury of the intracranial portion of the facial nerve (FN) is a severe complication of many skull base procedures, and it represents a relevant issue in terms of patients' discomfort, social interactions, risk for depression, and social costs. The aim of this study was to investigate the surgical and functional outcomes of the most common facial nerve rehabilitation techniques. The present study is a systematic review of the pertinent literature, according to the PRISMA guidelines. Two different online medical databases (PubMed, Scopus) were screened for studies reporting the functional outcome, measured by the House-Brackman (HB) scale, and complications, in FN early reanimation, following surgical injuries on its intracranial portion. Data on the VII-to-VII and XII-to-VII coaptation, the surgical technique, the use of a nerve graft, the duration of the deficit, and complications were collected and pooled. The XII-to-VII end-to-side coaptation seems to provide higher chances for functional restoration (HB 1-3) than the VII-to-VII (68.8% vs 60.6%), regardless of the duration of the palsy deficit, the use or not of a nerve graft, and the use of stitches or glues. However, its complication rate was as high as 28.6%, and a second procedure is then often needed. The XII-to-VII side-to-end coaptation is the most effective in providing a functional outcome (HB 1-3), even though it is associated to a higher complication rate. Further trials are needed to better investigate this relevant topic, in terms of health-related social costs and patients' quality of life.

Retrospective application of risk scores to ruptured intracranial aneurysms: would they have predicted the risk of bleeding?

As the incidental diagnosis of unruptured intracranial aneurysms has been increasing, several scores were developed to predict risk of rupture and growth to guide the management choice. We retrospectively applied these scores to a multicenter series of patients with subarachnoid hemorrhage to test whether they would have predicted the risk of bleeding in the event of aneurysm discovery previous to its rupture. Demographical, clinical, and radiological information of 245 adults were retrieved from two neurovascular centers' database. Data were pooled and PHASES, UCAS, and ELAPSS scores were retrospectively calculated for the whole population and their performances in identifying aneurysms at risk of rupture were compared. Mean PHASES, UCAS, and ELAPSS scores were 5.12 ± 3.08, 5.09 ± 2.62, and 15.88 ± 8.07, respectively. Around half (46%) of patients would have been assigned to the low- or very low-risk class (5-year rupture risk < 1%) in PHASES. Around 28% of patients would have been in a low-risk class, with a probability of 3-year rupture risk < 1% according to UCAS. Finally, ELAPSS score application showed a wider distribution among the risk classes, but a significant proportion of patients (45.5%) lie in the low- or intermediate-risk class for aneurysm growth. A high percentage of patients with ruptured aneurysms in this multicenter cohort would have been assigned to the lower risk categories for aneurysm growth and rupture with all the tested scores if they had been discovered before the rupture. Based on these observations, physicians should be careful about drawing therapeutic conclusions solely based on application of these scores.

Global adoption of robotic technology into neurosurgical practice and research.

Recent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Our aim was to carry out a worldwide survey among neurosurgeons to assess the adoption of and attitude toward robotic technology in the neurosurgical operating room and to identify factors associated with use of robotic technology. The online survey was made up of nine or ten compulsory questions and was distributed via the European Association of the Neurosurgical Societies (EANS) and the Congress of Neurological Surgeons (CNS) in February and March 2018. From a total of 7280 neurosurgeons who were sent the survey, we received 406 answers, corresponding to a response rate of 5.6%, mostly from Europe and North America. Overall, 197 neurosurgeons (48.5%) reported having used robotic technology in clinical practice. The highest rates of adoption of robotics were observed for Europe (54%) and North America (51%). Apart from geographical region, only age under 30, female gender, and absence of a non-academic setting were significantly associated with clinical use of robotics. The Mazor family (32%) and ROSA (26%) robots were most commonly reported among robot users. Our study provides a worldwide overview of neurosurgical adoption of robotic technology. Almost half of the surveyed neurosurgeons reported having clinical experience with at least one robotic system. Ongoing and future trials should aim to clarify superiority or non-inferiority of neurosurgical robotic applications and balance these potential benefits with considerations on acquisition and maintenance costs.

Machine learning in neurosurgery: a global survey.

BACKGROUND: Recent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use. METHODS: The online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS). RESULTS: Out of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging. CONCLUSIONS: This report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.

Mir-370-3p Impairs Glioblastoma Stem-Like Cell Malignancy Regulating a Complex Interplay between HMGA2/HIF1A and the Oncogenic Long Non-Coding RNA (lncRNA) NEAT1.

Glioblastoma (GBM) is the most aggressive and prevalent form of a human brain tumor in adults. Several data have demonstrated the implication of microRNAs (miRNAs) in tumorigenicity of GBM stem-like cells (GSCs). The regulatory functions of miRNAs in GSCs have emerged as potential therapeutic candidates for glioma treatment. The current study aimed at investigating the function of miR-370-3p in glioma progression, as aberrant expression of miR-370-3p, is involved in various human cancers, including glioma. Analyzing our collection of GBM samples and patient-derived GSC lines, we found the expression of miR-370-3p significantly downregulated compared to normal brain tissues and normal neural stem cells. Restoration of miR-370-3p expression in GSCs significantly decreased proliferation, migration, and clonogenic abilities of GSCs, in vitro, and tumor growth in vivo. Gene expression analysis performed on miR-370-3p transduced GSCs, identified several transcripts involved in Epithelial to Mesenchymal Transition (EMT), and Hypoxia signaling pathways. Among the genes downregulated by the restored expression of miR-370-3p, we found the EMT-inducer high-mobility group AT-hook 2 (HMGA2), the master transcriptional regulator of the adaptive response to hypoxia, Hypoxia-inducible factor (HIF)1A, and the long non-coding RNAs (lncRNAs) Nuclear Enriched Abundant Transcript (NEAT)1. NEAT1 acts as an oncogene in a series of human cancers including gliomas, where it is regulated by the Epidermal Growth Factor Receptor (EGFR) pathways, and contributes to tumor growth and invasion. Noteworthy, the expression levels of miR-370-3p and NEAT1 were inversely related in both GBM tumor specimens and GSCs, and a dual-luciferase reporter assay proved the direct binding between miR-370-3p and the lncRNAs NEAT1. Our results identify a critical role of miR-370-3p in the regulation of GBM development, indicating that miR-370-3p acts as a tumor-suppressor factor inhibiting glioma cell growth, migration and invasion by targeting the lncRNAs NEAT1, HMGA2, and HIF1A, thus, providing a potential candidate for GBM patient treatment.

Mastering Craniovertebral Junction Surgical Approaches: The Dissection Laboratory Experience at the Catholic University of Rome.

The craniovertebral junction is an intricate anatomical region frequently affected by neoplastic, vascular, traumatic, congenital and degenerative pathology. Because the topography of this region is complex, direct knowledge and full mastery of craniocervical anatomy is mainly obtained through anatomical dissections performed in neuroanatomical laboratories.

Transoral Approach to the Craniovertebral Junction: A Neuronavigated Cadaver Study.

More than 100 years after the first description by Kanavel of a transoral-transpharyngeal approach to remove a bullet impacted between the atlas and the clivus [1], the transoral approach (TOA) still represents the 'gold standard' for surgical treatment of a variety of conditions resulting in anterior craniocervical compression and myelopathy [2, 3]. Nevertheless, some concerns-such as the need for a temporary tracheostomy and a postoperative nasogastric tube, and the increased risk of infection resulting from possible bacterial contamination and nasopharyngeal incompetence [4-6]-led to the introduction of the endoscopic endonasal approach (EEA) by Kassam et al. [7] in 2005. Although this approach, which was conceived to overcome those surgical complications, soon gained wide attention, its clear predominance over the TOA in the treatment of craniovertebral junction (CVJ) pathologies is still a matter of debate [3]. In recent years, several papers have reported anatomical studies and surgical experience with the EEA, targeting different areas of the midline skull base, from the olfactory groove to the CVJ [8-19]. Starting from these preliminary experiences, further anatomical studies have defined the theoretical (radiological) and practical (surgical) craniocaudal limits of the endonasal route [20-25]. Our group has done the same for the TOA [26, 27] and compared the reliability of the radiological and surgical lines of the two different approaches. Very recently, a cadaver study, with the aid of neuronavigation, tried to define the upper and lower limits of the endoscopic TOA [28].

The Extreme Lateral Approach to the Craniovertebral Junction: An Anatomical Study.

BACKGROUND: The extreme lateral approach is a direct lateral approach which allows a good control of the entire length of the vertebral artery (VA), the jugular foramen, the lowest cranial nerves, and the jugular-sigmoid complex. Herein we try to exploit the variants of the approach and we identify indications, advantages, and disadvantages. METHODS: All phases of the study were conducted at the Institute of Public Health Section of Legal Medicine and Insurance of the University. We performed the extreme lateral approach in four subjects, who died between 24 and 48 h before in non-traumatic circumstances (three men and one woman). RESULTS: The great auricular nerve, the spinal accessory, the branches of the first ventral spinal nerves, the jugular vein, and the vertebral artery were identified in all the cadavers. In all cases the right VA exited from the transverse foramen of C1. The site of SCM piercing the accessory nerve was at a distance from the tip of the mastoid between 3 and 4 cm (3.3 in one case, 3.4 in 2 cases, and 3.7 in one case). No vessels and nerves have been damaged after being identified and isolated. CONCLUSIONS: Extradural lesions at the ventro-lateral aspect of the CVJ may require an extreme lateral approach, a procedure more aggressive comparing with far lateral approach, which represents a reasonable option for large anterior and anterolateral lesions when greater exposure is required.

Endoscope-Assisted Far Lateral Approach to the Craniovertebral Junction with Neuronavigation: A Cadaver Laboratory Experience.

The far lateral approach (FLA) is a technique performed nowadays to gain access to and remove intradural lesions located ventrolaterally to the brainstem and to the craniovertebral junction (CVJ).