Level and change in bone microarchitectural parameters and their relationship with previous fracture and established bone mineral density loci

A Novel CT-Based Fracture Risk Prediction Model for COPD Patients

RATIONALE AND OBJECTIVES

The aim of this study was to develop and validate a novel computed tomography (CT)-based fracture risk assessment model (FRCT) specifically tailored for patients suffering from chronic obstructive pulmonary disease (COPD).

METHODS

We conducted a retrospective analysis encompassing a cohort of 284 COPD patients, extracting data on demographics, clinical profiles, pulmonary function tests, and CT-based bone quantification metrics. The Boruta feature selection algorithm was employed to identify key variables for model construction, resulting in a user-friendly nomogram.

RESULTS

Our analysis revealed that 37.32% of the patients suffered fragility fractures post-follow-up. The FRCT model, integrating age, cancellous bone volume, average cancellous bone density, high-density lipoprotein levels, and prior fracture incidence, demonstrated superior predictive accuracy over the conventional fracture risk assessment tool (FRAX), with a C-index of 0.773 in the training group and 0.797 in the validation group. Calibration assessments via the Hosmer-Lemeshow test confirmed the model's excellent fit, and decision curve analysis underscored the FRCT model's substantial positive net benefit.

CONCLUSION

The FRCT model, leveraging opportunistic CT screening, offers a highly accurate and personalized approach to fracture risk prediction in COPD patients, surpassing the capabilities of existing tools. This model is poised to become an indispensable asset for clinicians in managing osteoporotic fracture risks within the COPD population.

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors

BACKGROUND

Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk.

METHODS

Participants of the Hertfordshire Cohort Study (HCS) attended research visits at which height and weight were measured; fracture history was determined via self-report and vertebral fracture assessment. Bone microarchitecture was assessed via HR-pQCT scans of the non-dominant distal tibia (Scanco XtremeCT), and bone mineral density measurement and lateral vertebral assessment were performed using dual-energy X-ray absorptiometry (DXA) (Lunar Prodigy Advanced). Images were cropped, pre-processed and texture analysis was performed using a three-dimensional local binary pattern method. These image data, together with age, sex, height, weight, BMI, dietary calcium and femoral neck BMD, were used in a random-forest classification algorithm. Receiver operating characteristic (ROC) analysis was used to compare fracture risk identification methods.

RESULTS

Overall, 180 males and 165 females were included in this study with a mean age of approximately 76 years and 97 (28 %) participants had sustained a previous fracture. Using clinical risk factors alone resulted in an area under the curve (AUC) of 0.70 (95 % CI: 0.56-0.84), which improved to 0.71 (0.57-0.85) with the addition of DXA-measured BMD. The addition of HR-pQCT image data to the machine learning classifier with clinical risk factors and DXA-measured BMD as inputs led to an improved AUC of 0.90 (0.83-0.96) with a sensitivity of 0.83 and specificity of 0.74.

CONCLUSION

These results suggest that using a three-dimensional computer vision method to HR-pQCT scanning may enhance the identification of those at risk of fracture beyond that afforded by clinical risk factors and DXA-measured BMD. This approach has the potential to make the information offered by HR-pQCT more accessible (and therefore) applicable to healthcare professionals in the clinic if the technology becomes more widely available.

Relationship between oseteoporosis with fatty infiltration of paraspinal muscles based on QCT examination

INTRODUCTION

To investigate the correlation between paraspinal muscles features and osteoporosis in lumbar spine.

MATERIALS AND METHODS

A total of 367 subjects who underwent quantitative computed tomography (QCT) examination were enrolled in this study. QCT pro workstation was used to obtain the mean bone mineral density (BMD) of the lower lumbar spine. Fat fraction (FF) and cross-section area (CSA) of the paraspinal muscles at the corresponding levels were measured. All participants were divided into normal, osteopenia, and osteoporosis groups. One-way ANOVA and independent samples t tests were performed to compare differences between groups. Pearson and Spearman correlation coefficients and partial correlation analysis after controlling for confounding factors were used to analyze the correlation between BMD and paraspinal muscles measurements.

RESULTS

Among the 367 participants included, 116 were in the normal group, 130 in the osteopenia group and 121 in the osteoporosis group. There were significant differences among the three groups for the mean and multifidus FF. BMD showed negative correlations with the FF of the paraspinal muscles. Multifidus and mean FF showed the best correlation (r = - 0.654, - 0.777). There were also significant differences in the mean and multifidus FF between different age groups, while after controlling for confounding factors, there was no correlation between age and FF of the paraspinal muscles.

CONCLUSION

This preliminary study demonstrated the association of BMD with fatty infiltration of paraspinal muscles. Different muscles might have specific effects in different sex and age groups.

The value of radiomics to predict abnormal bone mass in type 2 diabetes mellitus patients based on CT imaging for paravertebral muscles

OBJECTIVE

To investigate the value of CT imaging features of paravertebral muscles in predicting abnormal bone mass in patients with type 2 diabetes mellitus.

METHODS

The clinical and QCT data of 149 patients with type 2 diabetes mellitus were collected retrospectively. Patients were randomly divided into the training group (n = 90) and the validation group (n = 49). The radiologic model and Nomogram model were established by multivariate Logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

A total of 829 features were extracted from CT images of paravertebral muscles, and 12 optimal predictive features were obtained by the mRMR and Lasso feature selection methods. The radiomics model can better predict bone abnormality in type 2 diabetes mellitus, and the (Area Under Curve) AUC values of the training group and the validation group were 0.94(95% CI, 0.90-0.99) and 0.90(95% CI, 0.82-0.98). The combined Nomogram model, based on radiomics and clinical characteristics (vertebral CT values), showed better predictive efficacy with an AUC values of 0.97(95% CI, 0.94-1.00) in the training group and 0.95(95% CI, 0.90-1.00) in the validation group, compared with the clinical model.

CONCLUSION

The combination of Nomogram model and radiomics-clinical features of paravertebral muscles has a good predictive value for abnormal bone mass in patients with type 2 diabetes mellitus.