Multi-Dimensional Morphometric and Volumetric Analysis of the Posterior Cranial Fossa to Study Type I Chiari Malformation.

BACKGROUND: Chiari malformation type I (CM-1) is a complex disorder in which tonsillar herniation through the foramen magnum (FM) manifests with a spectrum of clinical symptoms. This work analyzes morphometric and volumetric characteristics of CM-1 patients. METHODS: With institutional review board approval, we retrospectively reviewed a total of 72 adult CM-1 patients and 26 healthy adult volunteers who underwent volumetric magnetic resonance brain imaging. Clinical data were retrospectively extracted from the electronic medical record. We analyzed multidimensional morphometric and volumetric features within the posterior cranial fossa and correlated these features with syrinx formation and the decision to undergo surgical decompression. RESULTS: In our study, CM-1 patients had decreased cerebellar (CBL), brainstem, and fourth ventricular volumes but larger tonsillar volume with increased total tonsillar length. CM-1 patients who underwent surgery had significantly more neural tissue within the cross-sectional area of the cisterna magna. Logistic regression demonstrated that combining neural tissue at the FM with CBL and fourth ventricular volumes led to a great degree of correlation with syrinx formation (area under the curve: 0.911). CONCLUSIONS: Our findings suggest that the amount of tissue at the FM correlates with CM-1 patients who underwent decompressive surgery, more so than tonsillar length. Additionally, the combination of neural tissue at the FM, CBL, and fourth ventricular volumes led to a great degree of correlation with syrinx formation. Together, these findings suggest that a global compressive phenomenon within the posterior fossa leads to CM-1 symptomatology and syrinx formation.

Simplifying synthesis of the expanding glioblastoma literature: a topic modeling approach.

PURPOSE: Our study aims to discover the leading topics within glioblastoma (GB) research, and to examine if these topics have "hot" or "cold" trends. Additionally, we aim to showcase the potential of natural language processing (NLP) in facilitating research syntheses, offering an efficient strategy to dissect the landscape of academic literature in the realm of GB research. METHODS: The Scopus database was queried using "glioblastoma" as the search term, in the "TITLE" and "KEY" fields. BERTopic, an NLP-based topic modeling (TM) method, was used for probabilistic TM. We specified a minimum topic size of 300 documents and 5% probability cutoff for outlier detection. We labeled topics based on keywords and representative documents and visualized them with word clouds. Linear regression models were utilized to identify "hot" and "cold" topic trends per decade. RESULTS: Our TM analysis categorized 43,329 articles into 15 distinct topics. The most common topics were Genomics, Survival, Drug Delivery, and Imaging, while the least common topics were Surgical Resection, MGMT Methylation, and Exosomes. The hottest topics over the 2020s were Viruses and Oncolytic Therapy, Anticancer Compounds, and Exosomes, while the cold topics were Surgical Resection, Angiogenesis, and Tumor Metabolism. CONCLUSION: Our NLP methodology provided an extensive analysis of GB literature, revealing valuable insights about historical and contemporary patterns difficult to discern with traditional techniques. The outcomes offer guidance for research directions, policy, and identifying emerging trends. Our approach could be applied across research disciplines to summarize and examine scholarly literature, guiding future exploration.

Geriatric grade 2 and 3 gliomas: A national cancer database analysis of demographics, treatment utilization, and survival.

With increasing life expectancies and population aging, the incidence of elderly patients with grade 2 and 3 gliomas is increasing. However, there is a paucity of knowledge on factors affecting their treatment selection and overall survival (OS). Geriatric patients aged between 60 and 89 years with histologically proven grade 2 and 3 intracranial gliomas were identified from the National Cancer Database between 2010 and 2017. We analyzed patients' demographic data, tumor characteristics, treatment modality, and outcomes. The Kaplan-Meier method was used to analyze OS. Univariate and multivariate analyses were performed to assess the predictive factors of mortality and treatment selection. A total of 6257 patients were identified: 3533 (56.3 %) hexagenerians, 2063 (32.9 %) septuagenarians, and 679 (10.8 %) octogenarians. We identified predictors of lower OS in patients, including demographic factors (older age, non-zero Charlson-Deyo score, non-Hispanic ethnicity), socioeconomic factors (low income, treatment at non-academic centers, government insurance), and tumor-specific factors (higher grade, astrocytoma histology, multifocality). Receiving surgery and chemotherapy were associated with a lower risk of mortality, whereas receiving radiotherapy was not associated with better OS. Our findings provide valuable insights into the complex interplay of demographic, socioeconomic, and tumor-specific factors that influence treatment selection and OS in geriatric grade 2 and 3 gliomas. We found that advancing age correlates with a decrease in OS and a reduced likelihood of undergoing surgery, chemotherapy, or radiotherapy. While receiving surgery and chemotherapy were associated with improved OS, radiotherapy did not exhibit a similar association.

The Role of Lumbar Drains in the Perioperative Management of Primary Spontaneous Temporal Lobe Encephaloceles and Cerebrospinal Fluid Leaks.

OBJECTIVE: To examine the role of lumbar drains (LDs) in the success of spontaneous temporal cerebrospinal fluid (CSF) leak and encephalocele repair. STUDY DESIGN: Retrospective cohort study. SETTING: Tertiary academic health system. PATIENTS: Patients undergoing repair of spontaneous temporal lobe encephaloceles or middle fossa CSF leaks during years 2017 to 2023. INTERVENTIONS: Transmastoid, middle fossa craniotomy, or combination approaches to CSF leak repair. OUTCOME MEASURES: Failure rate, complication rate, length of stay (LOS), readmission. RESULTS: Sixty-nine patients were included, with a combination approach performed in 78.3%, transmastoid in 17.4%, and isolated middle fossa craniotomy in 4.3%. Mean body mass index was 33.2, mean bony defect size width was 6.51 mm, and defect locations included the epitympanum, antrum, mastoid, and petrous apex. Multilayer closure with three or more layers was performed in 87.0%. LD was used in 73.9% of cases for a mean duration 2.27 days and was associated with longer LOS (3.27 vs. 1.56 d, p = 0.006) but not with failure rate, complications, discharge destination, or readmission. Only one major complication occurred as a result of the drain, but low-pressure headache was anecdotally common. CONCLUSIONS: Use of LD in the repair of spontaneous CSF leaks and temporal lobe encephaloceles is associated with longer LOS but not failure rates or other admission-level outcomes.

Exoscopic resection of a parasagittal atypical meningioma.

This video demonstrates use of the Synaptive 3D exoscope to enhance complex meningioma resection. The patient was a 58-year-old female who presented with new-onset seizures. Workup revealed a parasagittal meningioma over the bilateral cortices. She was started on 750 mg of Keppra twice daily and tapered dexamethasone and discharged. MR venography demonstrated segmental occlusion of the superior sagittal sinus. She then underwent a diagnostic angiogram and tumor Onyx embolization of the bilateral middle meningeal artery feeders. She then underwent a craniotomy for meningioma resection using 3D exoscope guidance. She awoke with a stable examination in the intensive care unit and worked with physical therapy on postoperative day 1. The video can be found here: https://stream.cadmore.media/r10.3171/2023.10.FOCVID23164.

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application.

WHO grade II and III gliomas demonstrate diverse biological behaviors resulting in variable survival outcomes. In the context of glioma prognosis, machine learning (ML) approaches could facilitate the navigation through the maze of factors influencing survival, aiding clinicians in generating more precise and personalized survival predictions. Here we report the utilization of ML models in predicting survival at 12, 24, 36, and 60 months following grade II and III glioma diagnosis. From the National Cancer Database, we analyze 10,001 WHO grade II and 11,456 grade III cranial gliomas. Using the area under the receiver operating characteristic (AUROC) values, we deploy the top-performing models in a web application for individualized predictions. SHapley Additive exPlanations (SHAP) enhance the interpretability of the models. Top-performing predictive models are the ones built with LightGBM and Random Forest algorithms. For grade II gliomas, the models yield AUROC values ranging from 0.813 to 0.896 for predicting mortality across different timeframes, and for grade III gliomas, the models yield AUROCs ranging from 0.855 to 0.878. ML models provide individualized survival forecasts for grade II and III glioma patients across multiple clinically relevant time points. The user-friendly web application represents a pioneering digital tool to potentially integrate predictive analytics into neuro-oncology clinical practice, to empower prognostication and personalize clinical decision-making.

Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models.

PURPOSE: The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas. METHODS: In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application. RESULTS: From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively. CONCLUSION: With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.