A Bibliometric and Visual Analysis of the Most Cited Articles on Disc Arthroplasty.

STUDY DESIGN: Bibliometric literature review. OBJECTIVE: Total disc arthroplasty (TDA) is an evolving technique used by surgeons to preserve neural elements and range of motion in patients with degenerative disc disease, spondylosis and more. In comparison to discectomy, disc arthroplasty has been shown to decrease morbidity, reduce adjacent-level disease, and preserve pre-operative range of motion. The objective of this manuscript is to provide a bibliometric analysis of the use of disc arthroplasty in scientific literature and highlight the main contributing authors and their publication characteristics. METHODS: The Scopus database was used to perform a title-specific, keyword-based search for all publications until June 2022. The keyword "total disc arthroplasty" was used. The most-cited 100 articles were selected for analysis. Parameters included the following: title, citation count, citations per year, authors, specialty first author, institution, country of origin, publishing journal, Source Normalized, Impact Per Paper (SNIP), and Hirsch index. RESULTS: Our search on disc arthroplasty yielded 580 articles that were published, with the first publication on the topic found in 1966. The most cited 100 articles received a total of 8694 citations, with an average of 86.94 citations per article. After assessing countries with the greatest contributions, United States, China, and Germany were top 3 with 54, 14, and 7 articles, respectively. CONCLUSION: The bibliometric analysis provides an overview of how medical research is analyzed in academic medicine. In the present study, we evaluated the global trends in disc arthroplasty for the treatment of degenerative disc disease.

Unique magnetic resonance spectroscopy profile of intracranial meningiomas compared to gliomas: a systematic review.

BACKGROUND AND PURPOSE: The goal of this study was to systematically review the metabolic profile of meningiomas using magnetic resonance spectroscopy in comparison to gliomas, as measured by mean metabolite ratios. METHODS: Following the PRISMA guidelines, a systematic literature review was performed using the PubMed, Ovid Embase, Web of Science, and the Cochrane databases from inception to May 2021. Studies were selected based on predetermined inclusion and exclusion criteria. RESULTS: Eight studies were ultimately selected with 207 patients included. Fifty-nine patients were diagnosed with meningioma (age = 48.4, 66.7% female) and 148 patients diagnosed with glioma (age = 56.4, 49.2% female). Three studies reported elevated Cho/Cr in meningiomas compared to gliomas (5.71 vs. 1.46, p < 0.05, 7.02 vs. 2.62, p < 0.05, and 4.64 vs. 2.52, p = 0.001). One study reported Ala/Cr to be significantly elevated in meningiomas compared to gliomas (1.30 vs. undetectable, p < 0.001). One study reported myo-Inositol/Cr to be significantly elevated in meningiomas in comparison to gliomas (1.44 vs. 1.08, p < 0.05). One study reported Glu/Cr to be significantly elevated in meningiomas in comparison to gliomas (3.47 vs. 0.89, p = 0.002). Two studies reported Cho/NAA to be significantly elevated in meningiomas in comparison to gliomas (4.46 vs. 2.6, p = 0.004, and 5.8 vs. 2.55, p < 0.05). Two studies reported NAA/Cr was significantly elevated in gliomas compared to meningiomas (undetectable vs. 1.54, p < 0.001 and undetectable vs. 0.58, p < 0.05). CONCLUSIONS: Significant differences in metabolite ratios between tumor types were reported in Cho/Cr, Ala/Cr, Glu/Cr, Cho/NAA, myoI/Cr and NAA/Cr between meningiomas and gliomas.

A Bibliometric Analysis of the Top 100 Most Influential Articles on Cerebral Cavernous Malformations.

OBJECTIVE: Cerebral cavernous malformations (CCMs) or cavernomas, are low-flow sinusoidal vascular anomalies of the central nervous system comprised of capillary networks filled with blood in various stages of thrombosis. This bibliometric analysis summarizes the most-cited articles on CCM and highlights the contributing articles to today's evidence-based practice. METHODS: In the execution of this bibliometric-based review article, the Scopus database was used to perform a title-specific, keyword-based search for all publications until June 2022. The keyword "cerebral cavernous malformations" OR "cerebral cavernous hemangioma" OR "cerebral cavernous angioma" OR "cerebral cavernoma." was used. Our results were arranged in descending order based on the article's citation count. The 100 most-cited articles were selected for analysis. Parameters included the following: title, citation count, citations per year, authors, specialty of first author, institution, country of origin, publishing journal, Source Normalized Impact per Paper, and Hirsch index were collected. RESULTS: The keyword-based search showed that 806 articles were published between 1974 and 2022 on CCMs. The top 100 articles were published between 1980 and 2018. The top 100 most cited articles collected a total of 12,928 citations with an average of 129.3 citations per paper. The rate of self-citations accounted for an average of 2.79% of the total number of citations. CONCLUSIONS: The bibliometric analysis provides a quantitative overview of how medical topics and interventions are analyzed in academic medicine. In the present study, we evaluated the global trends in CCMs by analyzing the top 100 most cited papers.

Brain Metastasis from Pancreatic Cancer: Our Experience and Systematic Review.

OBJECTIVE: To systematically review existing literature on the neurosurgical management and outcomes of brain metastasis from pancreatic cancer in comparison with our institutional experience of this patient cohort. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was performed using PubMed, Ovid Embase, Scopus, and Web of Science databases from date of database inception to March 2022. Studies were selected based on predetermined inclusion and exclusion criteria. Simultaneously, a retrospective analysis was conducted of patients who underwent neurosurgical evaluation and treatment for intracranial metastatic lesions from pancreatic origin at a single institution. RESULTS: The original literature search yielded 292 articles, of which 17 studies comprising 23 patients with brain metastases of pancreatic origin were ultimately selected. Median overall survival from primary diagnosis of pancreatic cancer was 22 months (interquartile range: 3-84) and 3 months (interquartile range: 1-36) after diagnosis of brain metastasis. In our institutional cohort, 4 patients were identified with a median overall survival of 30.5 months (interquartile range: 2-108). Our institutional cohort experienced a prolonged median overall survival (3 months vs. 30.5 months, P = 0.03) compared with the literature. CONCLUSIONS: Brain metastasis from pancreatic cancer is rare and associated with a fatal outcome. However, based on the data presented in this review, patient-specific and treatment-related factors could signal better prognosis. Further studies are needed to elucidate multimodal therapy and survival to suggest a more personalized decision-making process.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment. METHODS: Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign). RESULTS: The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures. CONCLUSIONS: Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.