Advanced 2D image processing technique to predict hip fracture risk in an older population based on single DXA scans

Trabecular bone mineral density as measured by thoracic vertebrae predicts incident hip and vertebral fractures: the multi-ethnic study of atherosclerosis

We evaluated the relationship of bone mineral density (BMD) by computed tomography (CT), to predict fractures in a multi-ethnic population. We demonstrated that vertebral and hip fractures were more likely in those patients with low BMD. This is one of the first studies to demonstrate that CT BMD derived from thoracic vertebrae can predict future hip and vertebral fractures.

PURPOSE/INTRODUCTION

Osteoporosis affects an enormous number of patients, of all races and both sexes, and its prevalence increases as the population ages. Few studies have evaluated the association between the vertebral trabecular bone mineral density(vBMD) and osteoporosis-related hip fracture in a multiethnic population, and no studies have demonstrated the predictive value of vBMD for fractures.

METHOD

We sought to determine the predictive value of QCT-based trabecular vBMD of thoracic vertebrae derived from coronary artery calcium scan for hip fractures in the Multi-Ethnic Study of Atherosclerosis(MESA), a nationwide multicenter cohort included 6814 people from six medical centers across the USA and assess if low bone density by QCT can predict future fractures. Measures were done using trabecular bone measures, adjusted for individual patients, from three consecutive thoracic vertebrae (BDI Inc, Manhattan Beach CA, USA) from non-contrast cardiac CT scans.

RESULTS

Six thousand eight hundred fourteen MESA baseline participants were included with a mean age of 62.2 ± 10.2 years, and 52.8% were women. The mean thoracic BMD is 162.6 ± 46.8 mg/cm3 (95% CI 161.5, 163.7), and 27.6% of participants (n = 1883) had osteoporosis (T-score 2.5 or lower). Over a median follow-up of 17.4 years, Caucasians have a higher rate of vertebral fractures (6.9%), followed by Blacks (4.4%), Hispanics (3.7%), and Chinese (3.0%). Hip fracture patients had a lower baseline vBMD as measured by QCT than the non-hip fracture group by 13.6 mg/cm3 [P < 0.001]. The same pattern was seen in the vertebral fracture population, where the mean BMD was substantially lower 18.3 mg/cm3 [P < 0.001] than in the non-vertebral fracture population. Notably, the above substantial relationship was unaffected by age, gender, race, BMI, hypertension, current smoking, medication use, or activity. Patients with low trabecular BMD of thoracic vertebrae showed a 1.57-fold greater risk of first hip fracture (HR 1.57, 95% CI 1.38-1.95) and a nearly threefold increased risk of first vertebral fracture (HR 2.93, 95% CI 1.87-4.59) compared to normal BMD patients.

CONCLUSION

There is significant correlation between thoracic trabecular BMD and the incidence of future hip and vertebral fracture. This study demonstrates that thoracic vertebrae BMD, as measured on cardiac CT (QCT), can predict both hip and vertebral fractures without additional radiation, scanning, or patient burden. Osteopenia and osteoporosis are markedly underdiagnosed. Finding occult disease affords the opportunity to treat the millions of people undergoing CT scans every year for other indications.

The effect of femur positioning on dual-energy X-ray absorptiometry (DXA) measures and statistical shape and appearance modeling (SSAM) fracture risk assessments

The diagnosis of osteoporosis using Dual-energy X-ray Absorptiometry (DXA) relies on accurate hip scans, whereby variability in measurements may be introduced by altered patient positioning, as could occur with repeated scans over time. The goal herein was to test how altered postures affect diagnostic metrics (i.e., standard clinical metrics and a newer image processing tool) for femur positioning. A device was built to support cadaveric femurs and adjust their orientation in 3° increments in flexion and internal/external rotation. Seven isolated femurs were scanned in six flexion postures (0° (neutral) to 15° of flexion) and eleven rotational postures (15° external to 15° internal rotation) while collecting standard clinical DXA-based measures for each scan. The fracture risk tool was applied to each scan to calculate fracture risk. Two separate one-way repeated measures ANOVAs (α = 0.05) were performed on the DXA-based measures and fracture risk prediction output. Flexion had a significant effect on T-score, Bone Mineral Density (BMD), and Bone Mineral Content (BMC), but not area, at angles greater than 12°. Internal and external rotation did not have a significant effect on any clinical metric. Fracture risk (as assessed by the image processing tool) was not affected by either rotation mode. Overall, this suggests clinicians can adjust patient posture to accommodate discomfort if deviations are less than 12 degrees, and the greatest care should be taken in flexion. Furthermore, the tool is relatively insensitive to postural adjustments, and as such may be a good option for tracking risk over repeated patient scans.

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

Hip fracture is the most common complication of osteoporosis, and its major contributor is compromised femoral strength. This study aimed to develop practical machine learning models based on clinical quantitative computed tomography (QCT) images for predicting proximal femoral strength. Eighty subjects with entire QCT data of the right hip region were randomly selected from the full MrOS cohorts, and their proximal femoral strengths were calculated by QCT-based finite element analysis (QCT/FEA). A total of 50 parameters of each femur were extracted from QCT images as the candidate predictors of femoral strength, including grayscale distribution, regional cortical bone mapping (CBM) measurements, and geometric parameters. These parameters were simplified by using feature selection and dimensionality reduction. Support vector regression (SVR) was used as the machine learning algorithm to develop the prediction models, and the performance of each SVR model was quantified by the mean squared error (MSE), the coefficient of determination (R² ), the mean bias, and the SD of bias. For feature selection, the best prediction performance of SVR models was achieved by integrating the grayscale value of 30% percentile and specific regional CBM measurements (MSE ≤ 0.016, R² ≥ 0.93); and for dimensionality reduction, the best prediction performance of SVR models was achieved by extracting principal components with eigenvalues greater than 1.0 (MSE ≤ 0.014, R² ≥ 0.93). The femoral strengths predicted from the well-trained SVR models were in good agreement with those derived from QCT/FEA. This study provided effective machine learning models for femoral strength prediction, and they may have great potential in clinical bone health assessments.

Quantitating Age-Related BMD Textural Variation from DXA Region-Free-Analysis: A Study of Hip Fracture Prediction in Three Cohorts

The risk of osteoporotic fracture is inversely related to bone mineral density (BMD), but how spatial BMD pattern influences fracture risk remains incompletely understood. This study used a pixel-level spatiotemporal atlas of proximal femoral BMD in 13,338 white European women (age 20-97 years) to quantitate age-related texture variation in BMD maps and generate a "reference" map of bone aging. We introduce a new index, called Densitometric Bone Age (DBA), as the age at which an individual site-specific BMD map (the proximal femur is studied here) best matches the median aging trajectory at that site in terms of the root mean squared error (RMSE). The ability of DBA to predict incident hip fracture and hip fracture pattern over 5 years following baseline BMD was compared against conventional region-based BMD analysis in a subset of 11,899 women (age 45-97 years), for which follow-up fracture records exist. There were 208 subsequent incident hip fractures in the study populations (138 femoral necks [FNs], 52 trochanteric [TR], 18 sites unspecified). DBA had modestly better performance compared to the conventional FN-BMD, TR-BMD, and total hip (TOT)-BMD in identifying hip fractures measured as the area under the curve (AUC) using receiver operating characteristics (ROC) curve analysis by 2% (95% confidence interval [CI], -0.5% to 3.5%), 3% (95% CI, 1.0% to 4.0%), and 1% (95% CI, 0.4% to 1.6%), respectively. Compared to FN-BMD T-score, DBA improved the ROC-AUC for predicting TR fractures by ~5% (95% CI, 1.1% to 9.8%) with similar performance in identifying FN fractures. Compared to TR-BMD T-score, DBA improved the ROC-AUC for the prediction of FN fractures by ~3% (95% CI, 1.1% to 4.9%), with similar performance in identifying TR fractures. Our findings suggest that DBA may provide a spatially sensitive measure of proximal femoral fragility that is not captured by FN-BMD or TR-BMD alone. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

PURPOSE OF REVIEW

Statistical models of shape and appearance have increased their popularity since the 1990s and are today highly prevalent in the field of medical image analysis. In this article, we review the recent literature about how statistical models have been applied in the context of osteoporosis and fracture risk estimation.

RECENT FINDINGS

Recent developments have increased their ability to accurately segment bones, as well as to perform 3D reconstruction and classify bone anatomies, all features of high interest in the field of osteoporosis and fragility fractures diagnosis, prevention, and treatment. An increasing number of studies used statistical models to estimate fracture risk in retrospective case-control cohorts, which is a promising step towards future clinical application. All the reviewed application areas made considerable steps forward in the past 5-6 years. Heterogeneities in validation hinder a thorough comparison between the different methods and represent one of the future challenges to be addressed to reach clinical implementation.

3D Analysis of the Proximal Femur Compared to 2D Analysis for Hip Fracture Risk Prediction in a Clinical Population

Due to the adverse impacts of hip fractures on patients' lives, it is crucial to enhance the identification of people at high risk through accessible clinical techniques. Reconstructing the 3D geometry and BMD distribution of the proximal femur could be beneficial in enhancing hip fracture risk predictions; however, it is associated with a high computational burden. It is also not clear whether it provides a better performance than 2D model analysis. Therefore, the purpose of this study was to compare the 2D and 3D model reconstruction's ability to predict hip fracture risk in a clinical population of patients. The DXA scans and CT scans of 16 cadaveric femurs were used to create training sets for the 2D and 3D model reconstruction based on statistical shape and appearance modeling. Subsequently, these methods were used to predict the risk of sustaining a hip fracture in a clinical population of 150 subjects (50 fractured, and 100 non-fractured) that were monitored for five years in the Canadian Multicentre Osteoporosis Study. 3D model reconstruction was able to improve the identification of patients who sustained a hip fracture more accurately than the standard clinical practice (by 40%). Also, the predictions from the 2D statistical model didn't differ significantly from the 3D ones (p > 0.76). These results indicated that to enhance hip fracture risk prediction in clinical practice implementing 2D statistical modeling has comparable performance with lower associated computational load.