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Tumor metastasis is a common event in patients with gastric cancer (GC) who previously underwent curative gastrectomy. It is meaningful to employ high-volume clinical data for predicting the survival of metastatic GC patients. We aim to establish an improved machine learning (ML) classifier for predicting if a patient with metastatic GC would die within 12 months. Eligible patients were enrolled from a Chinese GC cohort, and the complete detailed information from medical records was extracted to generate a high-dimensional dataset. Appropriate feature engineering and feature filter were conducted before modeling with eight algorithms. A 10-fold cross validation (CV) nested in a holdout CV (8:2) was employed for hyperparameter tuning and model evaluation. Model selection was based on the area under the receiver operating characteristic (AUROC) curve, recall, and precision. The selected model was globally explained using interpretable surrogate models. Of the total 399 cases (median survival of 8.2 months), 242 patients survived less than 12 months. The linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) model had the highest AUROC (0.78 ± 0.021), recall (0.93 ± 0.031), and precision (0.80 ± 0.026), respectively. The LDA model created a new function that generally separated the two classes. The predicted probability of the SVM model was interpreted using a linear regression model visualized by a nomogram. The predicted class of the RF model was explained using a decision tree model. In summary, analyzing high-volume medical data by ML is helpful to produce an improved model for predicting the survival in patients with metastatic GC. The algorithm should be carefully selected in different practical scenarios.

Introduction: Anterior cervical discectomy and fusion (ACDF) is widely accepted as the gold standard surgical procedure for treating cervical radiculopathy and myelopathy. However, there is concern about the low fusion rate in the early period after ACDF surgery using the Zero-P fusion cage. We creatively designed an assembled uncoupled joint fusion device to improve the fusion rate and solve the implantation difficulties. This study aimed to assess the biomechanical performance of the assembled uncovertebral joint fusion cage in single-level ACDF and compare it with the Zero-P device.Methods: A three-dimensional finite element (FE) of a healthy cervical spine (C2−C7) was constructed and validated. In the one-level surgery model, either an assembled uncovertebral joint fusion cage or a zero-profile device was implanted at the C5–C6 segment of the model. A pure moment of 1.0 Nm combined with a follower load of 75 N was imposed at C2 to determine flexion, extension, lateral bending, and axial rotation. The segmental range of motion (ROM), facet contact force (FCF), maximum intradiscal pressure (IDP), and screw−bone stress were determined and compared with those of the zero-profile device.Results: The results showed that the ROMs of the fused levels in both models were nearly zero, while the motions of the unfused segments were unevenly increased. The FCF at adjacent segments in the assembled uncovertebral joint fusion cage group was less than that that of the Zero-P group. The IDP at the adjacent segments and screw–bone stress were slightly higher in the assembled uncovertebral joint fusion cage group than in those of the Zero-P group. Stress on the cage was mainly concentrated on both sides of the wings, reaching 13.4–20.4 Mpa in the assembled uncovertebral joint fusion cage group.Conclusion: The assembled uncovertebral joint fusion cage provided strong immobilization, similar to the Zero-P device. When compared with the Zero-P group, the assembled uncovertebral joint fusion cage achieved similar resultant values regarding FCF, IDP, and screw–bone stress. Moreover, the assembled uncovertebral joint fusion cage effectively achieved early bone formation and fusion, probably due to proper stress distributions in the wings of both sides.

In recent years, cervical spondylosis has become one of the most common chronic diseases and has received much attention from the public. Magnetic resonance imaging (MRI) is the most widely used imaging modality for the diagnosis of degenerative cervical spondylosis. The manual identification and segmentation of the cervical spine on MRI makes it a laborious, time-consuming, and error-prone process. In this work, we collected a new dataset of 300 patients with a total of 600 cervical spine images in the MRI T2-weighted (T2W) modality for the first time, which included the cervical spine, intervertebral discs, spinal cord, and spinal canal information. A new instance segmentation approach called SeUneter was proposed for cervical spine segmentation. SeUneter expanded the depth of the network structure based on the original U-Net and added a channel attention module to the double convolution of the feature extraction. SeUneter could enhance the semantic information of the segmentation and weaken the characteristic information of non-segmentation to the screen for important feature channels in double convolution. In the meantime, to alleviate the over-fitting of the model under insufficient samples, the Cutout was used to crop the pixel information in the original image at random positions of a fixed size, and the number of training samples in the original data was increased. Prior knowledge of the data was used to optimize the segmentation results by a post-process to improve the segmentation performance. The mean of Intersection Over Union (mIOU) was calculated for the different categories, while the mean of the Dice similarity coefficient (mDSC) and mIOU were calculated to compare the segmentation results of different deep learning models for all categories. Compared with multiple models under the same experimental settings, our proposed SeUneter’s performance was superior to U-Net, AttU-Net, UNet++, DeepLab-v3+, TransUNet, and Swin-Unet on the spinal cord with mIOU of 86.34% and the spinal canal with mIOU of 73.44%. The SeUneter matched or exceeded the performance of the aforementioned segmentation models when segmenting vertebral bodies or intervertebral discs. Among all models, SeUneter achieved the highest mIOU and mDSC of 82.73% and 90.66%, respectively, for the whole cervical spine.

ObjectiveTo conduct a high-level meta-analysis of the RCTs to evaluate perioperative steroids use in the management of fusion rate, dysphagia, and VAS following anterior cervical spine surgery for up to 1 year.MethodsWe searched the database PubMed, EMBASE, Web of Science, Cochrane Library, Google Scholar, Ovid, and ClinicalTrials.gov without time restriction to identify RCTs that evaluate the effectiveness of perioperative steroids after anterior cervical spine surgery. A subgroup analysis was undertaken to investigate the effects of intravenous and local steroids. This study was registered in the PROSPERO database prior to initiation (CRD42022313444).ResultsA total of 14 RCTs were eligible for final inclusion. This meta-analysis showed that steroids could achieve lower dysphagia rate (p

BackgroundMore than 70 percent of the world's population is tortured with neck pain more than once in their vast life, of which 50–85% recur within 1–5 years of the initial episode. With medical resources affected by the epidemic, more and more people seek health-related knowledge via YouTube. This article aims to assess the quality and reliability of the medical information shared on YouTube regarding neck pain.MethodsWe searched on YouTube using the keyword “neck pain” to include the top 50 videos by relevance, then divided them into five and seven categories based on their content and source. Each video was quantitatively assessed using the Journal of American Medical Association (JAMA), DISCERN, Global Quality Score (GQS), Neck Pain-Specific Score (NPSS), and video power index (VPI). Spearman correlation analysis was used to evaluate the correlation between JAMA, GQS, DISCERN, NPSS and VPI. A multiple linear regression analysis was applied to identify video features affecting JAMA, GQS, DISCERN, and NPSS.ResultsThe videos had a mean JAMA score of 2.56 (SD = 0.43), DISCERN of 2.55 (SD = 0.44), GQS of 2.86 (SD = 0.72), and NPSS of 2.90 (SD = 2.23). Classification by video upload source, non-physician videos had the greatest share at 38%, and sorted by video content, exercise training comprised 40% of the videos. Significant differences between the uploading sources were observed for VPI (P = 0.012), JAMA (P < 0.001), DISCERN (P < 0.001), GQS (P = 0.001), and NPSS (P = 0.007). Spearman correlation analysis showed that JAMA, DISCERN, GQS, and NPSS significantly correlated with each other (JAMA vs. DISCERN, p < 0.001, JAMA vs. GQS, p < 0.001, JAMA vs. NPSS, p < 0.001, DISCERN vs. GQS, p < 0.001, DISCERN vs. NPSS, p < 0.001, GQS vs. NPSS, p < 0.001). Multiple linear regression analysis suggested that a higher JAMA score, DISCERN, or GQS score were closely related to a higher probability of an academic, physician, non-physician or medical upload source (P < 0.005), and a higher NPSS score was associated with a higher probability of an academic source (P = 0.001) than of an individual upload source.ConclusionsYouTube videos pertaining to neck pain contain low quality, low reliability, and incomplete information. Patients may be put at risk for health complications due to inaccurate, and incomplete information, particularly during the COVID-19 crisis. Academic groups should be committed to high-quality video production and promotion to YouTube users.

Background: The traditional titanium mesh cage (TTMC) has become common as a classical instrument for Anterior Cervical Corpectomy and Fusion (ACCF), but a series of complications such as cage subsidence, adjacent segment degeneration (ASD), and implant-related complications by using the TTMC have often been reported in the previous literature. The aim of this study was to assess whether a novel anatomic titanium mesh cage (NTMC) could improve the biomechanical condition after surgery.Methods: The NTMC model consists of two spacers located on both sides of the TTMC which match the anatomic structure between the endplates by measuring patient preoperative cervical computed tomography (CT) data. The ranges of motion (ROMs) of the surgical segments and the stress peaks in the C6 superior endplates, titanium mesh cage (TMC), screw–bone interface, anterior titanium plate, and adjacent intervertebral disc were compared.Results: Compared with the TTMC, the NTMC reduced the surgical segmental ROMs by 89.4% postoperatively. The C6 superior endplate stress peaks were higher in the TTMC (4.473–23.890 MPa), followed by the NTMC (1.923–5.035 MPa). The stress peaks on the TMC were higher in the TTMC (47.896–349.525 MPa), and the stress peaks on the TMC were lower in the NTMC (17.907–92.799 MPa). TTMC induced higher stress peaks in the screw–bone interface (40.0–153.2 MPa), followed by the NTMC (14.8–67.8 MPa). About the stress peaks on the anterior titanium plate, the stress of TTMC is from 16.499 to 58.432 MPa, and that of the NTMC is from 12.456 to 34.607 MPa. Moreover, the TTMC induced higher stress peaks in the C3/4 and C6/7 intervertebral disc (0.201–6.691 MPa and 0.248–4.735 MPa, respectively), followed by the NTMC (0.227–3.690 MPa and 0.174–3.521 MPa, respectively).Conclusion: First, the application of the NTMC can effectively decrease the risks of TMC subsidence after surgery. Second, in the NTMC, the stresses at the anterior screw-plate, bone–screw, and TMC interface are much less than in the TTMC, which decreased the risks of instrument-related complications after surgery. Finally, increases in IDP at adjacent levels are associated with the internal stresses of adjacent discs which may lead to ASD; therefore, the NTMC can effectively decrease the risks of ASD.

Introduction: Anterior cervical discectomy and fusion (ACDF) is a widely accepted surgical procedure in the treatment of cervical radiculopathy and myelopathy. A solid interbody fusion is of critical significance in achieving satisfactory outcomes after ACDF. However, the current radiographic techniques to determine the degree of fusion are inaccurate and radiative. Several animal experiments suggested that the mechanical load on the spinal instrumentation could reflect the fusion process and evaluated the stability of implant. This study aims to investigate the biomechanical changes during the fusion process and explore the feasibility of reflecting the fusion status after ACDF through the load changes borne by the interbody fusion cage.Methods: The computed tomography (CT) scans preoperatively, immediately after surgery, at 3 months, and 6 months follow-up of patients who underwent ACDF at C5/6 were used to construct the C2–C7 finite element (FE) models representing different courses of fusion stages. A 75-N follower load with 1.0-Nm moments was applied to the top of C2 vertebra in the models to simulate flexion, extension, lateral bending, and axial rotation with the C7 vertebra fixed. The Von Mises stress at the surfaces of instrumentation and the adjacent intervertebral disc and force at the facet joints were analyzed.Results: The facet contact force at C5/6 suggested a significantly stepwise reduction as the fusion proceeded while the intradiscal pressure and facet contact force of adjacent levels changed slightly. The stress on the surfaces of titanium plate and screws significantly decreased at 3 and 6 months follow-up. A markedly changed stress distribution in extension among three models was noted in different fusion stages. After solid fusion is achieved, the stress was more uniformly distributed interbody fusion in all loading conditions.Conclusions: Through a follow-up study of 6 months, the stress on the surfaces of cervical instrumentation remarkably decreased in all loading conditions. After solid intervertebral fusion formed, the stress distributions on the surfaces of interbody cage and screws were more uniform. The stress distribution in extension altered significantly in different fusion status. Future studies are needed to develop the interbody fusion device with wireless sensors to achieve longitudinal real-time monitoring of the stress distribution during the course of fusion.

Meningiomas, the most common brain tumor, inevitably require surgical treatment. However, the efficacy of prophylactic antiepileptic drugs (AEDs), in reducing the frequency of new-onset seizures during the perioperative period remains controversial. To further clarify if prophylactic antiepileptic drug treatment for patients with meningioma had value, we reviewed the medical records of 186 supratentorial meningioma patients who were operated at our hospital between 2016 and 2018. SPSS 24.0 software was used for statistical analysis. The results of univariate analysis showed that factors including age, sex, the course of the disease (years), maximum cross-sectional area of the tumor, location of the tumor, multiple or single tumors, adjacent to the cortex, peritumoral brain edema, World Health Organization classification, and peritumoral adhesion were not associated with perioperative seizures (P >0.05). Furthermore, the results of multivariate analysis revealed hydrocephalus (OR 4.87 P = 0.05) and non-skull base location (OR 1.88 P = 0.04) were significant risk factors for perioperative in-hospital seizures. Prophylactic valproic acid treatment did not contribute to the alleviation of perioperative seizures (OR 1.76 P = 0.04). However, Multivariate logistic regression analyses excluding the patients with seizures before operation confirmed prophylactic valproic acid treatment did not reduce the frequency of seizures during the perioperative period (OR 1.84 P = 0.04). Taken together, the data suggest that prophylactic valproic acid treatment for patients with supratentorial meningioma does not reduce the rate of perioperative seizures.