A modification and validation of quantitative morphometry classification system for osteoporotic vertebral compressive fractures in mainland Chinese

Age-Related changes in the morphological features of medial column of the proximal humerus in the Chinese population

Background

Age-related changes in the medial column (MC) of the proximal humerus have a major impact on fracture management; however, the changes in the morphological features remain unclear. This study aimed to investigate the age-related changes in the morphological features of MC and present the morphological grading.

Methods

One hundred computed tomography (CT) images of the proximal humerus of 100 individuals (19-95 years) were retrospectively obtained. The individuals were categorized into five age groups to quantify the differences among different ages; the youngest group (18-44 years) served as the baseline group. Parameters of the morphological features were measured on CT images with multiplanar reconstruction based on an explicit definition of MC, including length, thickness, width, oblique thickness (DSM), humeral head diameter (DHM), and ratio (RSM) of DSM to DHM. The morphological grading of MC was presented based on the value of RSM deviating different standard deviations (SD) from the mean value in the baseline group.

Results

Significant negative correlations were observed between age and the morphological parameters of MC (r ranged from -0.875 to -0.926; all P < 0.05), excluding DHM (r = 0.081, P = 0.422). Significant differences in the values of morphological feature parameters were detected among the five age groups (all P < 0.001). The highest mean values of morphological feature parameters were observed in the youngest group (18-44 years), which decreased gradually with increasing age until the lowest mean values were observed in the oldest group (≥90 years) (all P < 0.05). The morphological features of MC were categorized into three grades based on the value of RSM deviating 1.5 SD or 3 SD from the mean value in the baseline group.

Conclusion

Our study shows that the parameter values of morphological features of MC decreased with increasing age. The morphological features of MC could be categorized into three grades. Our findings may provide a more comprehensive insight into age-related changes in the morphological features of MC that facilitate risk stratification and optimize the management of proximal humeral fractures.

Short-term Efficacy of the Percutaneous Vertebroplasty Using a Curved Versus Straight Vertebroplasty Needle in Osteoporotic Vertebral Compression Fractures

The purpose of this study was to compare the efficacy of the curved puncture approach with 2 conventional approaches in percutaneous vertebroplasty (PVP) for the treatment of single-level osteoporotic vertebral compression fractures. Ninety-six patients with a single-level thoracolumbar vertebral fracture were surgically treated in the authors' department from February 2016 to February 2018. Patients were randomly divided into 3 groups, including 25 patients who had PVP punctured with a curved vertebroplasty needle (group C), 40 patients with unipedicular PVP with a straight vertebroplasty needle (group U), and 31 patients with bipedicular PVP with a straight vertebroplasty needle (group B). The short-term efficacies of PVP using different vertebroplasty needles were compared. Significant differences were tested preoperatively and postoperatively in vertebral body height variation, visual analog scale score, and Oswestry Disability Index in each of the 3 groups (P<.05). There was no significant difference among the groups in terms of Cobb angle correction and bone cement leakage. Group C and group U were superior to group B in terms of operative time and injected cement volume (P<.05). Twenty-four (96.0%) patients in group C and 29 (93.5%) patients in group B had centered cement distribution without significant differences (P>.05), which was superior to group U (P<.05). Curved puncture PVP achieved a satisfactory clinical outcome for osteoporotic vertebral compression fractures, with the advantages of less operative time, less injected cement volume, and more reasonable cement distribution for stabilization of the affected vertebrae. [Orthopedics. 2021;44(1):e131-e138.].

Prediction of lumbar vertebral strength of elderly men based on quantitative computed tomography images using machine learning

The parameters extracted from quantitative computed tomography (QCT) images were used to predict vertebral strength through machine learning models, and the highly accurate prediction indicated that it may be a promising approach to assess fracture risk in clinics.

INTRODUCTION

Vertebral fracture is common in elderly populations. The main factor contributing to vertebral fracture is the reduced vertebral strength. This study aimed to predict vertebral strength based on clinical QCT images by using machine learning.

METHODS

Eighty subjects with QCT data of lumbar spine were randomly selected from the MrOS cohorts. L1 vertebral strengths were computed by QCT-based finite element analysis. A total of 58 features of each L1 vertebral body were extracted from QCT images, including grayscale distribution, grayscale values of 39 partitioned regions, BMD_QCT, structural rigidity, axial rigidity, and BMD_QCTA_min. Feature selection and dimensionality reduction were used to simplify the 58 features. General regression neural network and support vector regression models were developed to predict vertebral strength. Performance of prediction models was quantified by the mean squared error, the coefficient of determination, the mean bias, and the SD of bias.

RESULTS

The 58 parameters were simplified to five features (grayscale value of the 60% percentile, grayscale values of three specific partitioned regions, and BMD_QCTA_min) and nine principal components (PCs). High accuracy was achieved by using the five features or the nine PCs to predict vertebral strength.

CONCLUSIONS

This study provided an effective approach to predict vertebral strength and showed that it may have great potential in clinical applications for noninvasive assessment of vertebral fracture risk.