European Spine Journal最新文献

Purpose: The pathomechanism of dropped head syndrome (DHS) is unclear. In this study, we aimed to examine the features of the paraspinal cervical muscles in patients with DHS by analyzing the volume of these muscles using magnetic resonance imaging (MRI).

Methods: Thirty-six patients with DHS and 25 patients with cervical spondylotic myelopathy (controls) were enrolled. The volume analyzer measured the cross-sectional area (CSA) of the paraspinal muscles on the axial image of a T2-weighted MRI at each level, from C2/3 to C6/7. The histogram used pixel intensities to measure the fat infiltration in the extensor muscles. The data were compared between the groups.

Results: The CSA of the semispinalis capitis and the splenius capitis and cervicis from the extensor muscles in DHS was larger than that of the control group at almost all levels. The CSAs of other extensor muscles were equivalent to those of the controls. The CSA of the sternocleidomastoideus in DHS was smaller than in the control group at C4/5/6/7. The CSA of any extensor muscle in the chronic group of the DHS was smaller than that of the acute group at the lower levels. The percentage of fat infiltration was not significantly different between the groups.

Conclusion: MRI analyses of the present study revealed that neither the extensor muscles in DHS were atrophic nor the flexor muscles were hypertrophic. Further, fatty infiltration of the extensor muscles may not induce muscle weakness of the extensors in patients with DHS.

Purpose: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking. Machine Learning (ML) has the potential to aid physicians in this process by automating segmentation and classification of LSS. However, it is unclear what models currently exist to perform these tasks.

Methods: A systematic review of literature was performed by searching the Cochrane Library, Embase, Emcare, PubMed, and Web of Science databases for studies describing an ML-based algorithm to perform segmentation or classification of the lumbar spine for LSS. Risk of bias was assessed through an adjusted version of the Newcastle-Ottawa Quality Assessment Scale that was more applicable to ML studies. Qualitative analyses were performed based on type of algorithm (conventional ML or Deep Learning (DL)) and task (segmentation or classification).

Results: A total of 27 articles were included of which nine on segmentation, 16 on classification and 2 on both tasks. The majority of studies focused on algorithms for MRI analysis. There was wide variety among the outcome measures used to express model performance. Overall, ML algorithms are able to perform segmentation and classification tasks excellently. DL methods tend to demonstrate better performance than conventional ML models. For segmentation the best performing DL models were U-Net based. For classification U-Net and unspecified CNNs powered the models that performed the best for the majority of outcome metrics. The number of models with external validation was limited.

Conclusion: DL models achieve excellent performance for segmentation and classification tasks for LSS, outperforming conventional ML algorithms. However, comparisons between studies are challenging due to the variety in outcome measures and test datasets. Future studies should focus on the segmentation task using DL models and utilize a standardized set of outcome measures and publicly available test dataset to express model performance. In addition, these models need to be externally validated to assess generalizability.

Purpose: Evaluate the validity of rasterstereography compared to low-dose, biplanar spine radiography for assessing thoracic kyphosis (TK) angles in Scheuermann's disease patients.

Methods: This prospective study included all the Scheuermann's disease patients consulting our clinic from 2016 to 2018. Recruited patients underwent two-dimensional low-dose biplanar anteroposterior full-length spine radiography and rasterstereography on the same day. Relationships between the TK angles measured were evaluated using Pearson correlation coefficients. Agreement between radiographic and rasterstereographic TK angles was evaluated using two-way intraclass correlation coefficients (ICCs) and Bland-Altman plots. Proportional biases were assessed using the slope regression lines fitted to Bland-Altman plots.

Results: The mean demographic and radiological characteristics of the 52 patients (20 girls; 39%) included were: age 13.1 ± 2 years; BMI 17.3 ± 2.8; and TK max. 50.4° ± 10°. Rasterstereographic TK angles were strongly correlated with radiographic TK angles evaluated from T2-T12 (r = 0.677) and from C7-Max (r = 0.704), with 'good' agreement (ICC > 0.75). A proportional bias was revealed in the slope regression line fitted to the Bland-Altman plot from the C7-Max radiography and the rasterstereography measurements (p = 0.034) but not from the T2-T12 and rasterstereographic TK angles (p = 0.997).

Conclusion: Rasterstereography is a reliable means of quantifying TK angles in Scheuermann's disease patients. It could directly reduce the number of radiographic scans patients need over time, minimising their radiation exposure.

Background: Magnetic resonance imaging (MRI) is increasingly used to estimate the geometric dimensions of lower lumbar vertebrae. While MRI-based measurements have demonstrated good reliability with interclass correlation coefficients (ICCs) of 0.80 or higher, many evaluations focus solely on the comparison of identical MRI images. This approach primarily reflects analyst dexterity and does not assess the reliability of the entire process, including imaging and image selection.

Objective: To evaluate the inter- and intra-rater reliability of the entire process of using MRI to measure biomechanically relevant lumbar spinal characteristics, incorporating imaging, image selection, and analysis.

Methods: A dataset of 144 low-back MRI scans was analyzed. Reliability assessments were performed under different conditions: (1) identical scans rated by the same analyst at different times (intra-rater reliability) and (2) distinct scans of the same subject obtained by different MRI operators and analyzed by different analysts (inter-rater reliability). Mean absolute differences in measurements were calculated, and sources of variability, such as breathing artifacts, were noted.

Results: Larger discrepancies were observed when comparing distinct scans analyzed by different MRI operators and analysts. In the "worst-case" scenario, where both the MRI operator and analyst differed, a 4.05% mean absolute difference was noted for anterior endplate measurements. This was higher than the 2.76% difference observed when analysts re-rated their own scans after one month. Despite these discrepancies, the variability in measurements was relatively low and primarily attributed to factors like breathing artifacts.

Conclusion: The process of using MRI to derive biomechanical measures, particularly for bony structures, demonstrates robust reliability. Variability in measurements is minimal even under challenging conditions, supporting the use of MRI for biomechanical assessments.

Purpose: To develop a deep learning system for automatic segmentation of compression fracture vertebral bodies on thoracolumbar CT and differentiate between fresh and old fractures.

Methods: We included patients with thoracolumbar fractures treated at our Hospital South Campus from January 2020 to December 2023, with prospective validation from January to June 2024, and used data from the North Campus from January to December 2023 for external validation. Fresh fractures were defined as back pain lasting less than 4 weeks, with MRI showing bone marrow edema (BME). We utilized a 3D V-Net for image segmentation and several ResNet and DenseNet models for classification, evaluating performance with ROC curves, accuracy, sensitivity, specificity, precision, F1 score, and AUC. The optimal model was selected to construct deep learning system and its diagnostic efficacy was compared with that of two clinicians.

Results: The training dataset included 238 vertebras (man/women: 55/183; age: 72.11 ± 11.55), with 59 in internal validation (man/women: 13/46; age: 74.76 ± 8.96), 34 in external validation, and 48 in prospective validation. The 3D V-Net model achieved a DSC of 0.90 on the validation dataset. ResNet18 performed best among classification models, with an AUC of 0.96 in validation, 0.89 in external dataset, and 0.87 in prospective validation, surpassing the two clinicians in both external and prospective validations.

Conclusion: The deep learning model can automatically and accurately segment the vertebral bodies with compression fractures and classify them as fresh or old fractures, thereby assisting clinicians in making clinical decisions.

Purpose: This study aimed to elucidate the correlation between the degree of fat infiltration (FI) in thoracic paraspinal muscles and thoracic vertebral degeneration (TVD).

Methods: This cross-sectional study comprised 474 patients who underwent standard thoracic computed tomography (CT) scans. The FI was quantified as the percentage of adipose tissues within the cross-sectional area of thoracic paraspinal muscles. Thoracic vertebra was assessed in terms of osteoporosis, ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), intervertebral disc calcification, intervertebral disc cavity, and facet joint osteoarthritis (FJO). Logistic regression, linear regression, subgroup, and receiver operating characteristic (ROC) analyses were assessed to evaluate the association between FI and TVD.

Results: Multivariate logistic regression revealed that more severe FI was closely associated with more serious osteoporosis (P < 0.001). Furthermore, after adjusting for only age, higher FI was significantly associated with nastier FJO (P < 0.05). In male patients, severe FI was greatly associated with worse osteoporosis (P < 0.05). In female patients, severe FI maintained close correlations with more severe osteoporosis and FJO (P < 0.05). Furthermore, in patients aged < 60 or ≥ 60 years, higher FI had a strong correlation with more severe osteoporosis (P < 0.001). In patients aged < 60 years, higher FI was associated with worse intervertebral disc calcification, OALL, and FJO (P < 0.05). Meanwhile, in patients aged ≥ 60 years, increased FI was only associated with severe OPLL (P < 0.05). Multivariate linear regression showed that FI negatively correlated with bone mineral density in the general population and different sex and age groups (P < 0.001). ROC analysis indicated that FI could predict the occurrence of TVD (P < 0.05).

Conclusion: Higher FI is associated with more severe TVD. Studies on TVD are currently limited; therefore, this study enriches the related research on TVD, and our findings would facilitate the early prediction and diagnosis of TVD in clinical practice. Furthermore, our findings indicate that thoracic spine pain (TSP) caused by TVD can be prevented, potentially improving the prognosis of patients with TSP.

Background: Based on the phenomenon that most thoracolumbar primary fracture line passes the center of the pedicle, we proposed an injury mechanism model to evaluate.

Methods: Consecutive patients with thoracolumbar fractures treated operatively between October 2019, and December 2020 were analyzed retrospectively. Demographic and spinal radiographical parameters were measured and recorded. Pedicle hyperintensity on T2-weighted sagittal MR images was labeled. We examined the relationship between the course of the line (Radius) connecting the center of the pedicle of the injured vertebra and the IAR and orientation of the thoracolumbar primary fracture line. A partial correlation test was calculated to find correlations between demographic and spinal radiographical parameters. Nonlinear regression analysis was run with the Radius as the dependent variable and the other spinal kyphosis parameters as the independent variables to verify this model.

Results: Ninety-seven patients with 104 thoracolumbar fractures were included in this study. Ninety-four (90.4%) thoracolumbar fractures showed a high signal on MRI T2 through the pedicle. Involvement of the center of the pedicle was distributed among most AOTL Type A and Type B thoracolumbar fractures. In total, 92.3% of primary vertebral fracture lines followed the Radius of the model (r2 = 0.940).

Conclusions: We provide a simple and quantifiable spinal instantaneous injury mechanism model for thoracolumbar fractures. Specifically, most AOTL type A and B thoracolumbar primary fracture line conforms to this model.

Purpose: This study aimed to investigate the differences in postural control effects due to plantar flexor fatigue between individuals with chronic low back pain (CLBP) and healthy controls.

Method: A total of 36 individuals with CLBP and 36 healthy participants took part in the study. Postural stability was evaluated using a force plate before and after a fatigue protocol that involved heel raises. Center-of-pressure (CoP) data were recorded during quiet standing on a rigid surface with eyes open (EO), a rigid surface with eyes closed (EC), and a foam surface with eyes closed (FC). Measurements included mean velocity, the area of the 95% confidence ellipse, and the standard deviation of velocity in both the anteroposterior and mediolateral directions.

Results: After fatigue, individuals with CLBP exhibited greater variability in sway velocity in the mediolateral direction on the foam surface with eyes closed (p = 0.035) and a larger sway area in the eyes closed condition (p = 0.027) compared to healthy controls. All participants demonstrated increased postural sway after fatigue in the more challenging task (EC) compared to the easier task (EO) (p < 0.01). However, the reduction in postural stability due to plantar flexor fatigue was not influenced by the increased difficulty of the postural task in the foam condition compared to the EO condition (p > 0.05).

Conclusions: Localized fatigue in the plantar flexor muscles negatively affected postural control in both CLBP and healthy groups, with a more significant impact observed in individuals with CLBP. This effect was particularly pronounced when visual input was removed.

Purpose: This systematic review aimed to identify predictors of brace treatment outcomes for adolescents or adults with idiopathic scoliosis.

Methods: Four databases including MEDLINE, EMBASE, Web of Science, and CINAHL were searched. Free text and indexed terms identifying the populations, predictions analyses and key outcomes were combined to search the literature. Pairs from eight independent reviewers conducted abstracts and full-text screening, and data extraction. The Quality in Prognostic Studies (QUIPS) tool was used to assess the risk of bias (ROB). Strength of evidence summary statements were formulated based on the risk of bias and the consistency of the research findings.

Results: The search found 2224 articles. After screening, seven articles were included. Only one article showed low ROB, while the others showed moderate ROB. All articles reported on patient-related outcome measurements (PROMS) of quality of life (QOL). Only one parameter achieved limited strength of evidence; shorter treatment time from one study predicted better long-term SRS-22 total scores. For other predictors, the level of evidence was unclear. Other predictors of long-term outcomes (> 1 year follow-up), from only 1 study on each outcome, were: larger Cobb angle predicted worse Spinal Appearance Questionnaire (SAQ) chest scores and worse depression; higher age predicted better SAQ curve scores, larger apical translations predicted worse SAQ shoulders and chest scores; a passive introverted personality or an active outgoing (MPI) character type predicted worse SRS-22 satisfaction; higher BMI predicted better SAQ curve, Rolland-Morris questionnaire (RMQ) lumbosacral pain, Quebec Back Pain Disability Scale (QDS) moving scores, and worse SRS-22 total; larger vital capacity predicted better QDS score; longer bracing (total) predicted worse depression; negative parental attitudes predicted worse depression; higher Strengths and Difficulties Questionnaire emotional symptoms, peer problems, prosocial behavior, and total scores predicted worse depression. Poor compliance from one short-term follow-up study predicted worse change of brace questionnaire (BRQ) for health perception, pain, physical and emotional functioning, and total scores. Moderate evidence from two studies with low and moderate RoB showed that age and Cobb angle did not predict long-term total SRS-22 score for prediction.

Conclusion: Eleven parameters predicted bracing outcomes, but most studies presented moderate risk of bias. Only one parameter, longer treatment time, with limited strength of evidence was predictive of better long-term SRS-22 total scores. Since most findings still present an unclear level of evidence, common weaknesses were identified to encourage design of high-quality studies predicting bracing outcomes.