Intracranial ependymomas in pediatric patients: patterns of care, disparities, and survival outcomes from the National Cancer Database.

OBJECTIVE: This study aimed to extract and analyze comprehensive data from the National Cancer Database (NCDB) to gain insights into the epidemiological prevalence, treatment patterns, and survival outcomes associated with intracranial ependymomas in pediatric patients. METHODS: The authors examined data extracted from the NCDB spanning the years 2010 to 2017, with a specific emphasis on intracranial ependymomas in individuals aged 0-21 years. The study used logistic and Poisson regression, along with Kaplan-Meier survival estimates and Cox proportional hazards models, for analysis. RESULTS: Among 908 included pediatric patients, 495 (54.5%) were male, and 702 (80.6%) were White. Kaplan-Meier analysis determined overall survival (OS) rates of 97.1% (95% CI 96%-98.2%) at 1 year postdiagnosis, 89% (95% CI 86.9%-91.1%) at 3 years, 82.9% (95% CI 80.3%-85.7%) at 5 years, and 74.5% (95% CI 69.8%-79.4%) at 10 years. Grade 3 tumors predicted a more than fourfold higher mortality hazard (p < 0.001; reference = grade 2). Infratentorial localization was also associated with a 1.7-fold increase in mortality hazard (p = 0.002; reference = supratentorial). Larger maximum tumor size (> 5 cm) correlated with a lower mortality hazard (HR 0.64, p = 0.011; reference ≤ 5 cm). The vast majority of patients (85.9%, n = 780) underwent resection. Uninsured patients had over fourfold higher prolonged length of stay (LOS) odds than those privately insured (OR 4.645, p = 0.007). Radiotherapy was received by 76.1% of patients, and the highest rates of radiotherapy occurred among children aged 5-12 years (p < 0.001). Only 25.6% received chemotherapy at any point during their treatment. Peak chemotherapy use emerged within ages 0-4 years, reaching 33.6% in this age group. Kaplan-Meier analysis indicated chemotherapy was associated with significantly worse OS (p = 0.041). CONCLUSIONS: This comprehensive analysis of the NCDB provides valuable insights into the epidemiology, treatment patterns, and survival outcomes of intracranial ependymomas in pediatric patients. Higher tumor grade, infratentorial localization, and chemotherapy use was associated with worse OS, while larger tumor size correlated with lower mortality hazard. Disparities in care were identified, with uninsured patients experiencing prolonged LOS. These findings underscore the need for tailored treatment strategies based on patient and tumor characteristics and highlight the importance of addressing socioeconomic barriers to optimize outcomes for children with ependymomas.

Exploring Pediatric Vertebral, Sacral, and Pelvic Osteosarcomas through the NCDB: Demographics, Treatment Utilization, and Survival Outcomes.

BACKGROUND AND OBJECTIVES: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. METHODS: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. CONCLUSIONS: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes.

Factors associated with substance use among preclinical medical students in Turkey: a cross-sectional study.

Introduction: Medical students experience high levels of stress due to their rigorous training, which can negatively affect their mental health. This study aimed to investigate substance use habits of medical students at Istanbul University-Cerrahpasa and the association on their mental health and demographic factors. Methods: This cross-sectional survey study was conducted in March-April 2022 among preclinical medical students (years 1-3 of a 6-year program). A confidential, anonymous online survey consisting of four sections on sociodemographic and educational characteristics, nicotine use and dependence [Fagerström Test for Nicotine Dependence (FTND)], alcohol use [Alcohol Use Disorders Identification Test (AUDIT)], mental health status [12-item General Health Questionnaire (GHQ-12)], was distributed to 1131 students via WhatsApp and Telegram text messages. Mann-Whitney U and Kruskal Wallis tests compared variables' distribution in the questionnaire categories. Spearman's correlation assessed associations between scales. Significance was p < 0.05. Results: The study included 190 medical students. A total of 26.3% of the participants were smokers, with 8.4% showing moderate to high levels of nicotine dependence. An estimated 45.8% and 8.4%reported low-risk consumption and risky usage of alcohol, respectively. There were statistically significant associations between substance use and demographic factors such as sex, GPA, and religious belief. The study found a statistically significant correlation between FTND scores and GHQ-12 scores, and, between FTND scores and AUDIT scores. Conclusion: The findings of this study will inform the development of interventions to improve the mental health and academic performance of medical students at Istanbul University-Cerrahpasa. Furthermore, it will raise awareness about the importance of addressing substance use among medical students in Turkey.

Introduction: Les étudiants en médecine sont assujettis à des niveaux élevés de stress en raison de leur formation rigoureuse, ce qui peut avoir un impact négatif sur leur santé mentale. Cette étude avait pour but d'étudier les habitudes de consommation de substances des étudiants en médecine de l'Université d'Istanbul-Cerrahpasa et l'association avec leur santé mentale et les facteurs démographiques. Méthodes: Cette étude transversale a été menée en mars-avril 2022 parmi les étudiants en médecine au pré-clinique (années 1 à 3 d'un programme de 6 ans). Un questionnaire en ligne confidentiel et anonyme comprenant quatre sections sur les caractéristiques sociodémographiques et éducatives, l'usage et la dépendance à la nicotine [Test de Fagerström pour la dépendance à la nicotine (FTND)], la consommation d'alcool [Test d'identification des troubles liés à la consommation d'alcool (AUDIT)], l'état de santé mentale [Questionnaire général sur la santé en 12 points (GHQ-12)], a été distribué à 1131 étudiants au moyen de messages texte WhatsApp et Telegram. Les tests de Mann-Whitney U et de Kruskal Wallis ont comparé la distribution des variables dans les catégories du questionnaire. La corrélation de Spearman a évalué les associations entre les échelles. Le niveau de signification statistique était p<0,05. Résultats: L'étude a porté sur 190 étudiants en médecine. Au total, 26,3 % des participants étaient des fumeurs, dont 8,4 % présentaient des niveaux modérés à élevés de dépendance à la nicotine. On estime que 45,8 % et 8,4 % des participants ont déclaré une consommation d'alcool à faible risque et une consommation d'alcool à risque, respectivement. Des associations statistiquement significatives ont été observées entre la consommation de substances et des facteurs démographiques tels que le sexe, la moyenne générale et les croyances religieuses. L'étude a mis en évidence une corrélation statistiquement significative entre les scores FTND et les scores GHQ-12, ainsi qu'entre les scores FTND et les scores AUDIT. Conclusion: Les résultats de cette étude permettront d'élaborer des interventions visant à améliorer la santé mentale et les résultats universitaires des étudiants en médecine de l'université d'Istanbul-Cerrahpasa. En outre, elle sensibilisera à l'importance de la prise en charge de l'utilisation de substances chez les étudiants en médecine en Turquie.

A machine learning-based approach for individualized prediction of short-term outcomes after anterior cervical corpectomy.

STUDY DESIGN: A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). PURPOSE: To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. OVERVIEW OF LITERATURE: Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. METHODS: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. RESULTS: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. CONCLUSIONS: This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.

Artificial intelligence/machine learning for neuroimaging to predict hemorrhagic transformation: Systematic review/meta-analysis.

BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. METHODS: A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method. RESULTS: We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively. CONCLUSION: AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.

Advancing precision prognostication in neuro-oncology: Machine learning models for data-driven personalized survival predictions in IDH-wildtype glioblastoma.

Background: Glioblastoma (GBM) remains associated with a dismal prognoses despite standard therapies. While population-level survival statistics are established, generating individualized prognosis remains challenging. We aim to develop machine learning (ML) models that generate personalized survival predictions for GBM patients to enhance prognostication. Methods: Adult patients with histologically confirmed IDH-wildtype GBM from the National Cancer Database (NCDB) were analyzed. ML models were developed with TabPFN, TabNet, XGBoost, LightGBM, and Random Forest algorithms to predict mortality at 6, 12, 18, and 24 months postdiagnosis. SHapley Additive exPlanations (SHAP) were employed to enhance the interpretability of the models. Models were primarily evaluated using the area under the receiver operating characteristic (AUROC) values, and the top-performing models indicated by the highest AUROCs for each outcome were deployed in a web application that was created for individualized predictions. Results: A total of 7537 patients were retrieved from the NCDB. Performance evaluation revealed the top-performing models for each outcome were built using the TabPFN algorithm. The TabPFN models yielded mean AUROCs of 0.836, 0.78, 0.732, and 0.724 in predicting 6, 12, 18, and 24 month mortality, respectively. Conclusions: This study establishes ML models tailored to individual patients to enhance GBM prognostication. Future work should focus on external validation and dynamic updating as new data emerge.

Pediatric intramedullary spinal cord tumors: A national cancer database analysis of demographics, patterns of care, and survival.

OBJECTIVE: Query the National Cancer Database (NCDB) to delineate epidemiologic frequency, care patterns, and survival outcomes of pediatric intramedullary spinal cord tumors (IMSCTs). METHODS: IMSCTs included ependymoma, astrocytoma, and hemangioblastoma. We examined data from the NCDB spanning 2004-2018, focusing on IMSCT in children aged 0-21 years. Our analysis included logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: This study included 1066 patients aged 0-21 years. 59.4 % of patients were male, while 83.1 % were white. The most common tumor histology was ependymoma (57.5 %), followed by astrocytoma (36.1 %) and hemangioblastoma (6.4 %). 24.9 % of patients received radiotherapy, with radiotherapy utilization being highest among patients aged 6-10 years. Chemotherapy utilization was highest in patients aged 0-5 years. 87.2 % of patients underwent surgical resection, with higher rates in patients aged 16-21 years. Overall survival did not differ significantly between resected and non-resected patients (p = 0.315). Patients in rural areas had worse OS than those in metro areas (HR = 4.42, p = 0.048). Patients with astrocytoma had worse OS compared to other histologies (HR = 2.21, p = 0.003). Astrocytoma patients were over twice as likely to have prolonged LOS compared to ependymoma patients (OR = 2.204, p < 0.001). CONCLUSIONS: In summary, our analysis utilizing the NCDB database provides a comprehensive overview of demographics, care patterns, and outcomes for the largest cohort of pediatric IMSCTs to date. These insights underscore the complexity of managing IMSCTs and emphasize the need for tailored approaches to improve patient outcomes.

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.

Chondrosarcoma of the Mobile Spine in the Elderly: A National Cancer Database Study.

BACKGROUND: The current research on geriatric patients with spinal chondrosarcoma is limited. This study aimed to investigate the demographics, patterns of care, and survival of geriatric patients with chondrosarcoma of the mobile spine. METHODS: The National Cancer Database was queried from 2008 to 2018 for geriatric patients (60-89 years) with chondrosarcoma of the mobile spine. The primary outcome of this study was overall survival. The secondary outcome was treatment utilization patterns. Survival analyses were conducted using log-rank tests and Cox proportional hazards regressions. Logistic regression models were utilized to assess correlations between baseline variables and treatment utilization. RESULTS: The database retrieved 122 patients. While 43.7% of the patients presented with tumors exceeding 5 cm in size, the incidence of regional lymph node involvement or distant metastases was relatively low, affecting only 5% of the patients. Furthermore, 22.3% of the patients had tumors graded as 3-4. The 5-year overall survival rate was 52.9% (95% confidence interval: 42-66.6). The mortality risk was significantly associated with age, tumor grade and stage, and treatment plan. Most patients (79.5%) underwent surgery, while 35.9% and 4.2% were treated with radiotherapy and chemotherapy, respectively. Age, race, comorbidities, geographical region, tumor stage, and healthcare facility type significantly correlated with treatment utilization. CONCLUSIONS: Surgical resection significantly lowered the mortality risk in geriatric patients with spinal chondrosarcomas. Demographic and geographical factors significantly dictated treatment plans. Further studies are required to assess the role of radiotherapy and chemotherapy in treating these patients in the modern era.

Atypical and anaplastic meningiomas in the later decades of life: A national cancer database analysis.

PURPOSE: We conducted a National Cancer Database (NCDB) study to investigate the epidemiological characteristics and identify predictors of outcomes associated with geriatric meningiomas. METHODS: The NCDB was queried for adults aged 60-89 years diagnosed between 2010 and 2017 with grade 2 and 3 meningiomas. The patients were classified into three age groups based on their age: 60-69 (hexagenarians), 70-79 (septuagenarians), and 80-89 (octogenarians). The log-rank test was utilized to compare the differences in overall survival (OS). Univariate and multivariate Cox proportional hazards regressions were used to evaluate the mortality risk associated with various patient and disease parameters. RESULTS: A total of 6585 patients were identified. Hexagenerians were the most common age group (49.8%), with the majority of meningiomas being classified as grade 2 (89.5%). The incidence of high-grade meningiomas increased in all age groups during the study period. Advanced age, male sex, black race, lower socioeconomic status, Charlson-Deyo score ≥ 2, and higher tumor grade were independent factors of poor survival. Among the modes of treatment, the extent of surgical resection, adjuvant radiotherapy, and treatment at a noncommunity cancer program were linked with better outcomes. CONCLUSION: In geriatric patients with high-grade meningiomas, the greater extent of surgical resection and radiotherapy are associated with improved survival. However, the management and outcome of geriatric patients with higher-grade meningiomas are also associated with several socioeconomic factors.

Optimal Timing in Cervical Spinal Cord Injury: A Comprehensive Meta-Analysis of Ultra-Early Surgical Intervention Within Five Hours.

The optimal timing of surgery for cervical spinal cord injuries (SCI) and its impact on neurological recovery continue to be subjects of debate. This systematic review and meta-analysis aims to consolidate and assess the existing evidence regarding the efficacy of ultra-early decompression surgery in improving clinical outcomes after cervical SCI. A search was conducted in PubMed, Embase, Cochrane, and CINAHL databases from inception until September 18, 2023, focusing on human studies. The groups were categorized into ultra-early decompression (decompression surgery ≤ 5 hours post-injury) and a control group (decompression surgery between 5-24 hours post-injury). A random effects meta-analysis was performed on all studies using R Studio. Outcomes were reported as effect size (OR, treatment effect, and 95% CI. Of the 140 patients, 63 (45%) underwent decompression ≤ 5 hours, while 77 (55%) underwent decompression > 5 hours post-injury. Analysis using the OR model showed no statistically significant difference in the odds of neurological improvement between the ultra-early group and the early group (OR = 1.33, 95% CI: 0.22-8.18, p = 0.761). This study did not observe significant neurological improvement among cervical SCI patients who underwent decompression within five hours. Due to the scarcity of literature on the ultra-early decompression of cervical SCI, this study underscores the necessity for additional investigation into the potential benefits of earlier interventions for cervical SCI to enhance patient outcomes.

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.

Mapping the Degenerative Cervical Myelopathy Research Landscape: Topic Modeling of the Literature.

STUDY DESIGN: Topic modeling of literature. OBJECTIVES: Our study has 2 goals: (i) to clarify key themes in degenerative cervical myelopathy (DCM) research, and (ii) to evaluate the current trends in the popularity or decline of these topics. Additionally, we aim to highlight the potential of natural language processing (NLP) in facilitating research syntheses. METHODS: Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic, an NLP-based topic modeling method. We specified a minimum topic size of 25 documents and 50 words per topic. After the models were trained, they generated a list of topics and corresponding representative documents. We utilized linear regression models to examine trends within the identified topics. In this context, topics exhibiting increasing linear slopes were categorized as "hot topics," while those with decreasing slopes were categorized as "cold topics". RESULTS: Our analysis retrieved 3510 documents that were classified into 21 different topics. The 3 most frequently occurring topics were "OPLL" (ossification of the posterior longitudinal ligament), "Anterior Fusion," and "Surgical Outcomes." Trend analysis revealed the hottest topics of the decade to be "Animal Models," "DCM in the Elderly," and "Posterior Decompression" while "Morphometric Analyses," "Questionnaires," and "MEP and SSEP" were identified as being the coldest topics. CONCLUSIONS: Our NLP methodology conducted a thorough and detailed analysis of DCM research, uncovering valuable insights into research trends that were otherwise difficult to discern using traditional techniques. The results provide valuable guidance for future research directions, policy considerations, and identification of emerging trends.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

Comprehensive assessment of atypical and anaplastic pediatric meningiomas utilizing national cancer database: a retrospective cohort study.

PURPOSE: To examine demographic and clinical characteristics and their association with survival in grade 2 and 3 pediatric meningiomas in a large cohort using the National Cancer Database (NCDB). METHODS: We conducted a comprehensive analysis using data from NCDB between 2004 to 2018. Tumor-specific data included tumor grade and size. Treatment details, including surgical resection, extent of resection, and radiotherapy, were gathered. Our analytic approach incorporated logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: Among the included 239 patients aged 0-21 years, age category distribution was significantly different between grade 2 and grade 3 tumors (p = 0.018). For grade 2 meningiomas, 51.5% of patients were female, and 76.7% were white. 85.3% of patients with grade 2 meningiomas underwent surgical resection, of which 67% underwent gross total resection. Overall survival (OS) was significantly different between resected and non-resected patients (p = 0.048). Uninsured patients were over seven times as likely to have prolonged length of stay (LOS) versus those with private insurance (OR = 7.663, p = 0.014). For grade 3 meningiomas, 51.4% of patients were male, and 82.9% were white. 91.4% of patients with grade 3 meningiomas underwent surgical resection, of which 53.3% underwent subtotal resection. OS was not significantly different between resected and non-resected patients (p = 0.659). CONCLUSION: In summary, there were significant differences in age, maximum tumor dimension, unplanned readmission, radiotherapy, and treatment combinations between grade 2 and 3 meningiomas. These findings highlight the intricacies of managing pediatric meningiomas and emphasize the necessity for tailored therapeutic approaches to enhance outcomes in the future.

An Interpretable Machine Learning Approach to Predict Survival Outcomes in Spinal and Sacropelvic Chordomas.

STUDY DESIGN: Retrospective, population-based cohort study. OBJECTIVE: This study aimed to develop machine learning (ML) models to predict five-year and 10-year mortality in spinal and sacropelvic chordoma patients and integrate them into a web application for enhanced prognostication. SUMMARY OF BACKGROUND DATA: Past research has uncovered factors influencing survival in spinal chordoma patients. While identifying individual predictors is important, personalized survival predictions are equally vital. Though prior efforts have resulted in nomograms aiming to serve this purpose, they cannot capture complex interactions within data and rely on statistical assumptions that may not fit real-world data. METHODS: Adult spinal and sacropelvic chordoma patients were identified from the National Cancer Database. Sociodemographic, clinicopathologic, diagnostic, and treatment-related variables were utilized as predictive features. Five supervised ML algorithms (TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest) were implemented to predict mortality at five and 10 years postdiagnosis. Model performance was primarily evaluated using the area under the receiver operating characteristic (AUROC). SHapley Additive exPlanations (SHAP) values and partial dependence plots provided feature importance and interpretability. The top models were integrated into a web application. RESULTS: From the NCDB, 1206 adult patients diagnosed with histologically confirmed spinal and sacropelvic chordomas were retrieved for the five-year mortality outcome [423 (35.1%) with five-year mortality] and 801 patients for the 10-year mortality outcome [588 (73.4%) with 10-year mortality]. Top-performing models for both of the outcomes were the models created with the CatBoost algorithm. The CatBoost model for five-year mortality predictions displayed a mean AUROC of 0.801, and the CatBoost model predicting 10-year mortality yielded a mean AUROC of 0.814. CONCLUSIONS: This study developed ML models that can accurately predict five-year to 10-year survival probabilities in spinal chordoma patients. Integrating these interpretable, personalized prognostic models into a web application provides quantitative survival estimates for a given patient. The local interpretability enables transparency into how predictions are influenced. Further external validation is warranted to support generalizability and clinical utility.

Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research.

Traumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study.

BACKGROUND AND PURPOSE: We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models. METHODS: Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single-center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0-2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented. RESULTS: A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety-two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome. CONCLUSIONS: Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals.

Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

Trends in stroke-related journals: Examination of publication patterns using topic modeling.

OBJECTIVES: This study aims to demonstrate the capacity of natural language processing and topic modeling to manage and interpret the vast quantities of scholarly publications in the landscape of stroke research. These tools can expedite the literature review process, reveal hidden themes, and track rising research areas. MATERIALS AND METHODS: Our study involved reviewing and analyzing articles published in five prestigious stroke journals, namely Stroke, International Journal of Stroke, European Stroke Journal, Translational Stroke Research, and Journal of Stroke and Cerebrovascular Diseases. The team extracted document titles, abstracts, publication years, and citation counts from the Scopus database. BERTopic was chosen as the topic modeling technique. Using linear regression models, current stroke research trends were identified. Python 3.1 was used to analyze and visualize data. RESULTS: Out of the 35,779 documents collected, 26,732 were classified into 30 categories and used for analysis. "Animal Models," "Rehabilitation," and "Reperfusion Therapy" were identified as the three most prevalent topics. Linear regression models identified "Emboli," "Medullary and Cerebellar Infarcts," and "Glucose Metabolism" as trending topics, whereas "Cerebral Venous Thrombosis," "Statins," and "Intracerebral Hemorrhage" demonstrated a weaker trend. CONCLUSIONS: The methodology can assist researchers, funders, and publishers by documenting the evolution and specialization of topics. The findings illustrate the significance of animal models, the expansion of rehabilitation research, and the centrality of reperfusion therapy. Limitations include a five-journal cap and a reliance on high-quality metadata.