Global Trends in Orthopedic Biofilm Research: A Bibliometric Analysis of 1994-2022.

Objective: Bibliometric analysis is commonly used to visualize the knowledge foundation, trends, and patterns in a specific scientific field by performing a quantitative evaluation of the relevant literature. The purpose of this study was to perform a bibliometric analysis of recent studies in the field of orthopedic biofilm research and identify its current trends and hotspots. Methods: Research studies were retrieved from the Web of Science Core Collection and Scopus databases and analyzed in bibliometrix with R package (4.2.2). Results: A total of 2426 literature were included in the study. Journal of orthopaedic research and Clinical orthopaedics and related research ranked first in terms of productivity and impact, with 57 published articles and 32 h-index, respectively. Trampuz A, Ohio State Univ and the United States ranked as the most productive authors, institutions, and countries. Biofilm formation, role of sonication, biomaterial mechanism and antibiotic loading have been investigated as the trend and hotspots in the field of orthopedic biofilm research. Conclusion: This study provides a thorough overview of the state of the art of current orthopedic biofilm research and offers valuable insights into recent trends and hotspots in this field.

Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.

BACKGROUND: The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS. METHODS: A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 150:30:30. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared. RESULTS: The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805). CONCLUSIONS: A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.

A Suture Technique for Ruptured Annulus Fibrosus Following Decompression Under Percutaneous Transforaminal Endoscopic Discectomy.

The suture technique for a ruptured annulus fibrosus (AF) under full-endoscopy remains challenging. Direct suturing of a ruptured annular tear after full decompression has been shown to decrease the recurrence rate of lumbar disc herniation during endoscopic surgery. Traditional suture operations under endoscopy involve only simple suturing of the ruptured AF. Due to the weak and poor quality of the AF tissue around the tear portal, using this area as needle insertion points during suturing may lead to insufficient tension and a low success rate of AF closure. Currently, there is no detailed technical illustration based on video for AF tear suturing under lumbar full-endoscopy. We innovatively propose a method of covering and suturing the AF tear by pulling up the posterior longitudinal ligament (PLL) under lumbar endoscopy and using three stitches (PLL-AF suture technique). The patient who received the novel suture technique achieved satisfactory results. Six months after the operation, lumbar MRI showed no evidence of recurrence in the outpatient clinic.

Mapping the hotspots and future trends of electrical stimulation for peripheral nerve injury: A bibliometric analysis from 2002 to 2023.

Peripheral nerve injuries often result in severe personal and social burden, and even with surgical treatment, patients continue to have poor clinical outcomes. Over the past two decades, electrical stimulation has been shown to promote axonal regeneration and alleviate refractory neuropathic pain. The aim of this study was to analyse this field using a bibliometric approach. Literature was searched through Web of Science Core Collection (WOSCC) for the years 2002-2023. Literature analysis included: (1) Describing publication trends in the field. (2) Exploring collaborative network relationships. (3) Finding research advances and research hotspots in the field. (4) Summarizing research trends in the field. With the number of studies in this field still increasing, a total of 693 publications were included in the analysis. This field of research is interdisciplinary in nature. Research hotspots include peripheral nerve regeneration, the treatment of neuropathic pain, materials for nerve injury repair, and the restoration of sensory function in patients with peripheral nerve injury. Correspondingly, the development of nerve conduits and systems for peripheral nerve electrical stimulation, clinical trials of peripheral nerve electrical stimulation, and tactile recovery and movement for amputees have shown significant promise as future research trends in this field.

Early prognostication of critical patients with spinal cord injury: A machine learning study with 1485 cases.

STUDY DESIGN: A retrospective case-series. OBJECTIVE: The study aims to use machine-learning (ML) to predict the discharge destination of spinal cord injury (SCI) patients in the intensive care unit (ICU). SUMMARY OF BACKGROUND DATA: Prognostication following SCI is vital, especially for critical patients who need intensive care. METHODS: Clinical data of patients diagnosed with SCI were extracted from a publicly available ICU database. The firstly recorded data of the included patients were used to develop a total of 98 ML classifiers, seeking to predict discharge destination (e.g. death, further medical care, home). The micro-average area under the curve (AUC) was the main indicator to assess discrimination. The best average-AUC classifier and the best death-sensitivity classifier were integrated into an ensemble classifier. The discrimination of the ensemble classifier was compared with top death-sensitivity classifiers and top average-AUC classifiers. Additionally, prediction consistency and clinical utility were also assessed. RESULTS: A total of 1485 SCI patients were included. The ensemble classifier had a micro-average AUC of 0.851, which was only slightly inferior to the best average-AUC classifier (P=0.10) The best average-AUC classifier death sensitivity was much lower than that of the ensemble classifier. The ensemble classifier had a death sensitivity of 0.452, which was inferior to top 8 death-sensitivity classifiers, whose micro-average AUC were inferior to the ensemble classifier (P<0.05). Additionally, the ensemble classifier demonstrated a comparable Brier score and superior Net benefit in the decision curve analysis, when compared to the performance of the origin classifiers. CONCLUSIONS: The ensemble classifier shows an overall superior performance in predicting discharge destination considering discrimination ability, prediction consistency and clinical utility. This classifier system may aid in the clinical management of critical SCI patients in the early phase following injury. LEVEL OF EVIDENCE: 3.

Global Scientific Trends in Virtual Reality for Pain Treatment From 2000 to 2022: Bibliometric Analysis.

Background: Virtual reality (VR) is a computer simulation technique that has been increasingly applied in pain management over the past 2 decades. Objective: In this study, we used bibliometrics to explore the literature on VR and pain control, with the aim of identifying research progress and predicting future research hot spots. Methods: We extracted literature on VR and pain control published between 2000 and 2022 from the Web of Science Core Collections and conducted bibliometric analyses. We analyzed the publication and citation trends in the past 2 decades, as well as publication and citation analyses of different countries, institutions, journals, and authors. For references, we conducted cocitation and burst analyses. For keywords, we conducted co-occurrence, clustering, timeline view, and citation burst analyses. Results: Based on 1176 publications, we found that there was a continuous increase in publication and citation volumes, especially in the last 5 years. The United States was the most representative country, and the University of Washington was the most representative institution, with both having the most publications and citations. The most popular journal in this field was Burns, and Hoffman HG was the most productive author, leading many studies on patients with burn pain. The reference with the most citation burst was a study on the verification of new hardware in pain control. The keywords with the highest citation bursts related to various situations of pain such as "burn pain," "wound care," "low back pain," and "phantom limb." Conclusions: VR has been applied in various clinical situations for pain management, among which burns and pediatric surgery have achieved satisfactory results. We infer that VR will be extended to more clinical pain situations in the future, such as pain control in wound care, low back pain, and phantom limb pain. New research hot spots will include the development of software and hardware to improve the immersive experience of VR for pain control. However, our work was based solely on English literature from the Web of Science database. For future studies, we recommend that researchers explore literature from multiple databases to enhance the scope of their research.

Prevalence and risk factors of bone metastasis and the development of bone metastatic prognostic classification system: a pan-cancer population study.

BACKGROUND: The prevalence of bone metastasis (BM) varies among primary cancer patients, and it has a significant impact on prognosis. However, there is a lack of research in this area. This study aims to explore the clinical characteristics, prevalence, and risk factors, and to establish a prognostic classification system for pan-cancer patients with BM. METHODS: The data obtained from the Surveillance, Epidemiology and End Results database were investigated. The prevalence and prognosis of patients with BM were analyzed. Hierarchical clustering was used to develop a prognostic classification system. RESULTS: From 2010 to 2019, the prevalence of BM has increased by 41.43%. BM most commonly occurs in cancers that originate in the adrenal gland, lung and bronchus and overlapping lesion of digestive systems. Negative prognostic factors included older age, male sex, poorer grade, unmarried status, low income, non-metropolitan living, advanced tumor stages, previous chemotherapy, and synchronous liver, lung, and brain metastasis. Three categories with significantly different survival time were identified in the classification system. CONCLUSIONS: The clinical features, prevalence, risk factors, and prognostic factors in pan-cancer patients with BM were investigated. A prognostic classification system was developed to provide survival information and aid physicians in selecting personalized treatment plans for patients with BM.

Research hotspots and trends on spinal cord stimulation for pain treatment: a two-decade bibliometric analysis.

Background: Chronic pain poses a significant social burden. Spinal cord stimulation (SCS) is considered to be the most promising treatment for refractory pain. The aim of this study was to summarize the current research hotspots on SCS for pain treatment during the past two decades and to predict the future research trends by bibliometric analysis. Methods: The literature over the last two decades (2002-2022) which was related to SCS in pain treatment was obtained from the Web of Science Core Collection. Bibliometric analyses were conducted based on the following aspects: (1) Annual publication and citation trends; (2) Annual publication changes of different publication types; (3) Publications and citations/co-citations of different country/institution/journal/author; (4) Citations/co-citation and citation burst analysis of different literature; and (5) Co-occurrence, cluster, thematic map, trend topics, and citation burst analysis of different keywords. (6) Comparison between the United States and Europe. All analyses were performed on CiteSpace, VOSviewer, and R bibliometrix package. Results: A total of 1,392 articles were included in this study, with an increasing number of publications and citations year by year. The most highly published type of literature was clinical trial. United States was the country with the most publications and citations; Johns Hopkins University was the institution with the most publications; NEUROMODULATION published the most papers; the most published author was Linderoth B; and the most cited paper was published in the PAIN by Kumar K in 2007. The most frequently occurring keywords were "spinal cord stimulation," "neuropathic pain," and "chronic pain," etc. Conclusion: The positive effect of SCS on pain treatment has continued to arouse the enthusiasm of researchers in this field. Future research should focus on the development of new technologies, innovative applications, and clinical trials for SCS. This study might facilitate researchers to comprehensively understand the overall perspective, research hotspots, and future development trends in this field, as well as seek collaboration with other researchers.

Novel Nomograms and Web-Based Tools Predicting Overall Survival and Cancer-specific Survival of Solitary Plasmacytoma of the Spine.

STUDY DESIGN: Retrospective analysis. OBJECTIVE: This study aimed to establish nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with solitary plasmacytoma of the spine (SPS). SUMMARY OF BACKGROUND DATA: SPS is a rare type of malignant spinal tumor. A systematic study of prognostic factors associated with survival can provide guidance to clinicians and patients. Consideration of other causes of death (OCOD) in CSS will improve clinical practicability. METHODS: A total of 1078 patients extracted from the SEER database between 2000 and 2018 were analyzed. Patients were grouped into training and testing data sets (7:3). Factors associated with OS and CSS were identified by Cox regression and competing risk regression, respectively, for the establishment of nomograms on a training data set. The testing data set was used for the external validation of the performance of the nomograms using calibration curves, Brier's scores, C-indexes, time-dependent receiver operating characteristic curves, and decision curve analysis (DCA). RESULTS: Age and grade were identified as factors associated with both OS and CSS, along with marital status, radiation for OS, and chemotherapy for CSS. Heart disease, cerebrovascular disease, and diabetes mellitus were found to be the 3 most common causes of OCOD. The nomograms showed satisfactory agreement on calibration plots for both training and testing data sets. Integrated Brier score, C-index, and overall area under the curve on the testing data set were 0.162/0.717/0.789 and 0.173/0.709/0.756 for OS and CSS, respectively. DCA curves showed a good clinical net benefit. Nomogram-based web tools were developed for clinical application. CONCLUSION: This study provides evidence for risk factors and prognostication of survival in SPS patients. The novel nomograms and web-based tools we developed demonstrated good performance and might serve as accessory tools for clinical decision-making and SPS management. LEVEL OF EVIDENCE: 3.

Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database.

BACKGROUND: Our study aimed to explore the prognostic factors of bladder cancer with bone metastasis (BCBM) and develop prediction models to predict the overall survival (OS) and cancer-specific survival (CSS) of BCBM patients. METHODS: A total of 1438 patients with BCBM were obtained from the SEER database. Patients from 2010 to 2016 were randomly divided into training and validation datasets (7:3), while patients from 2017 were divided for external testing. Nomograms were established using prognostic factors identified through Cox regression analyses and validated internally and externally. The concordance index (C-index), calibration plots, and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the discrimination and calibration of nomogram models, while decision curve analyses (DCA) and Kaplan-Meier (KM) curves were used to estimate the clinical applicability. RESULTS: Marital status, tumor metastasis (brain, liver, and lung), primary site surgery, and chemotherapy were indicated as independent prognostic factors for OS and CSS. Calibration plots and the overall C-index showed a novel agreement between the observed and predicted outcomes. Nomograms revealed significant advantages in OS and CSS predictions. AUCs for internal and external validation were listed as follows: for OS, 3-month AUCs were 0.853 and 0.849; 6-month AUCs were 0.873 and 0.832; 12-month AUCs were 0.825 and 0.805; for CSS, 3-month AUCs were 0.849 and 0.847; 6-month AUCs were 0.870 and 0.824; 12-month AUCs were 0.815 and 0.797, respectively. DCA curves demonstrated good clinical benefit, and KM curves showed distinct stratification performance. CONCLUSION: The nomograms as web-based tools were proved to be accurate, efficient, and clinically beneficial, which might help in patient management and clinical decision-making for BCBM patients.

Surgically Relevant Morphological Parameters of the L5-S1 Interlaminar Window: A Statistical Analysis Based on 3D Reconstruction of CT Data.

BACKGROUND: The interlaminar window is the most important anatomical corridor during the posterior approach for lumbar and lumbosacral pathologies. Three-dimensional (3D) reconstruction of the L5-S1 interlaminar window including accurate measurements may be beneficial for the surgeon. The aim of this study was to measure relevant surgical parameters of the L5-S1 interlaminar window based on 3D reconstruction of lumbar computed tomography (CT). METHODS: Fifty thin-layer CT data were retrospectively collected, segmented, and reconstructed. Relevant surgical parameters included the width, left height, right height, interpedicular distance (IPD), area, and suitable approach area of the L5-S1 interlaminar window. Morphological measurements were performed independently by two experienced experts. Patients with disk herniation at L5-S1 were regarded as group A (n = 28) and those without L5-S1 disk herniation were regarded as group B (n = 22). RESULTS: The average left height, right height, width, and area of the L5-S1 interlaminar window were 9.14 ± 2.45 mm, 9.55 ± 2.46 mm, 23.55 ± 4.91 mm, and 144.57 ± 57.05 mm2, respectively. The average IPD at the superior, middle, and inferior pedicle levels was 29.29 ± 3.39, 27.96 ± 3.38, and 37.46 ± 4.23 mm, respectively, with significant differences among these three parameters (p < 0.05). The average suitable approach areas of the L5-S1 interlaminar window were the following: left axilla-24.52 ± 15.91 mm2; left shoulder-27.14 ± 15.48 mm2; right axilla-29.95 ± 17.17 mm2; and right shoulder-31.12 ± 16.40 mm2 (p > 0.05). There were no significant differences between groups A and B in these parameters (p > 0.05), except the inferior IPD (36.69 ± 3.73 vs. 39.23 ± 3.01 mm, p = 0.017 < 0.05). CONCLUSION: The morphological measurement of the L5-S1 interlaminar window based on 3D reconstruction provided accurate and reliable reference data for posterior microsurgical and endoscopic approaches as well as percutaneous infiltrations.

Machine Learning Predicts Decompression Levels for Lumbar Spinal Stenosis Using Canal Radiomic Features from Computed Tomography Myelography.

BACKGROUND: The accurate preoperative identification of decompression levels is crucial for the success of surgery in patients with multi-level lumbar spinal stenosis (LSS). The objective of this study was to develop machine learning (ML) classifiers that can predict decompression levels using computed tomography myelography (CTM) data from LSS patients. METHODS: A total of 1095 lumbar levels from 219 patients were included in this study. The bony spinal canal in CTM images was manually delineated, and radiomic features were extracted. The extracted data were randomly divided into training and testing datasets (8:2). Six feature selection methods combined with 12 ML algorithms were employed, resulting in a total of 72 ML classifiers. The main evaluation indicator for all classifiers was the area under the curve of the receiver operating characteristic (ROC-AUC), with the precision-recall AUC (PR-AUC) serving as the secondary indicator. The prediction outcome of ML classifiers was decompression level or not. RESULTS: The embedding linear support vector (embeddingLSVC) was the optimal feature selection method. The feature importance analysis revealed the top 5 important features of the 15 radiomic predictors, which included 2 texture features, 2 first-order intensity features, and 1 shape feature. Except for shape features, these features might be eye-discernible but hardly quantified. The top two ML classifiers were embeddingLSVC combined with support vector machine (EmbeddingLSVC_SVM) and embeddingLSVC combined with gradient boosting (EmbeddingLSVC_GradientBoost). These classifiers achieved ROC-AUCs over 0.90 and PR-AUCs over 0.80 in independent testing among the 72 classifiers. Further comparisons indicated that EmbeddingLSVC_SVM appeared to be the optimal classifier, demonstrating superior discrimination ability, slight advantages in the Brier scores on the calibration curve, and Net benefits on the Decision Curve Analysis. CONCLUSIONS: ML successfully extracted valuable and interpretable radiomic features from the spinal canal using CTM images, and accurately predicted decompression levels for LSS patients. The EmbeddingLSVC_SVM classifier has the potential to assist surgical decision making in clinical practice, as it showed high discrimination, advantageous calibration, and competitive utility in selecting decompression levels in LSS patients using canal radiomic features from CTM.

Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database.

STUDY DESIGN: Retrospective Cohort Study. OBJECTIVES: This study aimed to develop survival prediction models for spinal Ewing's sarcoma (EWS) based on machine learning (ML). METHODS: We extracted the SEER registry's clinical data of EWS diagnosed between 1975 and 2016. Three feature selection methods extracted clinical features. Four ML algorithms (Cox, random survival forest (RSF), CoxBoost, DeepCox) were trained to predict the overall survival (OS) and cancer-specific survival (CSS) of spinal EWS. The concordance index (C-index), integrated Brier score (IBS) and mean area under the curves (AUC) were used to assess the prediction performance of different ML models. The top initial ML models with best performance from each evaluation index (C-index, IBS and mean AUC) were finally stacked to ensemble models which were compared with the traditional TNM stage model by 3-/5-/10-year Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA). RESULTS: A total of 741 patients with spinal EWS were identified. C-index, IBS and mean AUC for the final ensemble ML model in predicting OS were .693/0.158/0.829 during independent testing, while .719/0.171/0.819 in predicting CSS. The ensemble ML model also achieved an AUC of .705/0.747/0.851 for predicting 3-/5-/10-year OS during independent testing, while .734/0.779/0.830 for predicting 3-/5-/10-year CSS, both of which outperformed the traditional TNM stage. DCA curves also showed the advantages of the ensemble models over the traditional TNM stage. CONCLUSION: ML was an effective and promising technique in predicting survival of spinal EWS, and the ensemble models were superior to the traditional TNM stage model.

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT.

Introduction: Three-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs. Materials and Methods: A total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction. Results: The average Dice score of IFFCT in the local test dataset for "proximal femur", "fragment" and "distal femur" were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8. Conclusion: The proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs.

Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study.

Background: This study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability. Methods: The clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis. Results: Our study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851-0.875) and 0.859 (95% CI, 0.839-0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance. Conclusions: Several factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.

Research trends and hotspots on connectomes from 2005 to 2021: A bibliometric and latent Dirichlet allocation application study.

Background: This study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm. Methods: Documents were retrieved from the Web of Science Core Collection (WoSCC) and Scopus databases and analyzed in Rstudio 1.3.1. Through quantitative and qualitative methods, the most productive and impactful academic journals in the field of connectomes were compared in terms of the total number of publications and h-index over time. Meanwhile, the countries/regions and institutions involved in connectome research were compared, as well as their scientific collaboration. The study analyzed topics and research trends by R package "bibliometrix." The major topics of connectomes were classified by Latent Dirichlet allocation (LDA). Results: A total of 14,140 publications were included in the study. NEUROIMAGE ranked first in terms of publication volume (1,427 articles) and impact factor (h-index:122) among all the relevant journals. The majority of articles were published by developed countries, with the United States having the most. Harvard Medical School and the University of Pennsylvania were the two most productive institutions. Neuroimaging analysis technology and brain functions and diseases were the two major topics of connectome research. The application of machine learning, deep learning, and graph theory analysis in connectome research has become the current trend, while an increasing number of studies were concentrating on dynamic functional connectivity. Meanwhile, researchers have begun investigating alcohol use disorders and migraine in terms of brain connectivity in the past 2 years. Conclusion: This study illustrates a comprehensive overview of connectome research and provides researchers with critical information for understanding the recent trends and hotspots of connectomes.

Machine Learning-based Prediction of Prolonged Intensive Care Unit Stay for Critical Patients with Spinal Cord Injury.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: The objective of the study was to develop machine-learning (ML) classifiers for predicting prolonged intensive care unit (ICU)-stay and prolonged hospital-stay for critical patients with spinal cord injury (SCI). SUMMARY OF BACKGROUND DATA: Critical patients with SCI in ICU need more attention. SCI patients with prolonged stay in ICU usually occupy vast medical resources and hinder the rehabilitation deployment. METHODS: A total of 1599 critical patients with SCI were included in the study and labeled with prolonged stay or normal stay. All data were extracted from the eICU Collaborative Research Database and the Medical Information Mart for Intensive Care III-IV Database. The extracted data were randomly divided into training, validation and testing (6:2:2) subdatasets. A total of 91 initial ML classifiers were developed, and the top three initial classifiers with the best performance were further stacked into an ensemble classifier with logistic regressor. The area under the curve (AUC) was the main indicator to assess the prediction performance of all classifiers. The primary predicting outcome was prolonged ICU-stay, while the secondary predicting outcome was prolonged hospital-stay. RESULTS: In predicting prolonged ICU-stay, the AUC of the ensemble classifier was 0.864 ±â€Š0.021 in the three-time five-fold cross-validation and 0.802 in the independent testing. In predicting prolonged hospital-stay, the AUC of the ensemble classifier was 0.815 ±â€Š0.037 in the three-time five-fold cross-validation and 0.799 in the independent testing. Decision curve analysis showed the merits of the ensemble classifiers, as the curves of the top three initial classifiers varied a lot in either predicting prolonged ICU-stay or discriminating prolonged hospital-stay. CONCLUSION: The ensemble classifiers successfully predict the prolonged ICU-stay and the prolonged hospital-stay, which showed a high potential of assisting physicians in managing SCI patients in ICU and make full use of medical resources.Level of Evidence: 3.

Research topics and hotspot trends of lumbar spondylolisthesis: A text-mining study with machine learning.

Objectives: The study aimed to conduct a bibliometric analysis of publications concerning lumbar spondylolisthesis, as well as summarize its research topics and hotspot trends with machine-learning based text mining. Methods: The data were extracted from the Web of Science Core Collection (WoSCC) database and then analyzed in Rstudio1.3.1 and CiteSpace5.8. Annual publication production and the top-20 productive authors over time were obtained. Additionally, top-20 productive journals and top-20 influential journals were compared by spine-subspecialty or not. Similarly, top-20 productive countries/regions and top-20 influential countries/regions were compared by they were developed countries/regions or not. The collaborative relationship among countries and institutions were presented. The main topics of lumbar spondylolisthesis were classified by Latent Dirichlet allocation (LDA) analysis, and the hotspot trends were indicated by keywords with strongest citation bursts. Results: Up to 2021, a total number of 4,245 articles concerning lumbar spondylolisthesis were finally included for bibliometric analysis. Spine-subspecialty journals were found to be dominant in the productivity and the impact of the field, and SPINE, EUROPEAN SPINE JOURNAL and JOURNAL OF NEUROSURGERY-SPINE were the top-3 productive and the top-3 influential journals in this field. USA, Japan and China have contributed to over half of the publication productivity, but European countries seemed to publish more influential articles. It seemed that developed countries/regions tended to produce more articles and more influential articles, and international collaborations mainly occurred among USA, Europe and eastern Asia. Publications concerning surgical management was the major topic, followed by radiographic assessment and epidemiology for this field. Surgical management especially minimally invasive technique for lumbar spondylolisthesis were the recent hotspots over the past 5 years. Conclusions: The study successfully summarized the productivity and impact of different entities, which should benefit the journal selection and pursuit of international collaboration for researcher who were interested in the field of lumbar spondylolisthesis. Additionally, the current study may encourage more researchers joining in the field and somewhat inform their research direction in the future.

Development and Validation of a Personalized Prognostic Prediction Model for Patients With Spinal Cord Astrocytoma.

BACKGROUND: The study aimed to investigate the prognostic factors of spinal cord astrocytoma (SCA) and establish a nomogram prognostic model for the management of patients with SCA. METHODS: Patients diagnosed with SCA between 1975 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training and testing datasets (7:3). The primary outcomes of this study were overall survival (OS) and cancer-specific survival (CSS). Cox hazard proportional regression model was used to identify the prognostic factors of patients with SCA in the training dataset and feature importance was obtained. Based on the independent prognostic factors, nomograms were established for prognostic prediction. Calibration curves, concordance index (C-index), and time-dependent receiver operating characteristic (ROC) curves were used to evaluate the calibration and discrimination of the nomogram model, while Kaplan-Meier (KM) survival curves and decision curve analyses (DCA) were used to evaluate the clinical utility. Web-based online calculators were further developed to achieve clinical practicability. RESULTS: A total of 818 patients with SCA were included in this study, with an average age of 30.84 ± 21.97 years and an average follow-up time of 117.57 ± 113.51 months. Cox regression indicated that primary site surgery, age, insurance, histologic type, tumor extension, WHO grade, chemotherapy, and post-operation radiotherapy (PRT) were independent prognostic factors for OS. While primary site surgery, insurance, tumor extension, PRT, histologic type, WHO grade, and chemotherapy were independent prognostic factors for CSS. For OS prediction, the calibration curves in the training and testing dataset illustrated good calibration, with C-indexes of 0.783 and 0.769. The area under the curves (AUCs) of 5-year survival prediction were 0.82 and 0.843, while 10-year survival predictions were 0.849 and 0.881, for training and testing datasets, respectively. Moreover, the DCA demonstrated good clinical net benefit. The prediction performances of nomograms were verified to be superior to that of single indicators, and the prediction performance of nomograms for CSS is also excellent. CONCLUSIONS: Nomograms for patients with SCA prognosis prediction demonstrated good calibration, discrimination, and clinical utility. This result might benefit clinical decision-making and patient management for SCA. Before further use, more extensive external validation is required for the established web-based online calculators.