Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA

A vision transformer-convolutional neural network framework for decision-transparent dual-energy X-ray absorptiometry recommendations using chest low-dose CT

OBJECTIVE

This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations for dual-energy X-ray absorptiometry (DXA) scans from chest low-dose computed tomography (LDCT).

METHODS

The framework was developed using data from 321 individuals and validated with an independent test cohort of 186 individuals. It addresses two classification tasks: (1) distinguishing normal from abnormal bone mineral density (BMD) and (2) differentiating osteoporosis from non-osteoporosis. Three field-of-view (FOV) settings-fitFOV (entire vertebra), halfFOV (vertebral body only), and largeFOV (fitFOV + 20 %)-were analyzed to assess their impact on model performance. Model predictions were weighted and combined to enhance classification accuracy, and visualizations were generated to improve decision transparency. DXA scans were recommended for individuals classified as having abnormal BMD or osteoporosis.

RESULTS

The ensemble framework significantly outperformed individual models in both classification tasks (McNemar test, p < 0.001). In the development cohort, it achieved 91.6 % accuracy for task 1 with largeFOV (area under the receiver operating characteristic curve [AUROC]: 0.97) and 86.0 % accuracy for task 2 with fitFOV (AUROC: 0.94). In the test cohort, it demonstrated 86.6 % accuracy for task 1 (AUROC: 0.93) and 76.9 % accuracy for task 2 (AUROC: 0.99). DXA recommendation accuracy was 91.6 % and 87.1 % in the development and test cohorts, respectively, with notably high accuracy for osteoporosis detection (98.7 % and 100 %).

CONCLUSIONS

This combined ViT-CNN framework effectively assesses bone status from LDCT images, particularly when utilizing fitFOV and largeFOV settings. By visualizing classification confidence and vertebral abnormalities, the proposed framework enhances decision transparency and supports clinicians in making informed DXA recommendations following opportunistic osteoporosis screening.

Comparative effectiveness of four techniques for identifying vertebral fragility fractures among elderly patients

OBJECTIVE

The incidence of vertebral fragile fractures peaked among the elderly population, and identifying individuals at high risk of vertebral fractures and promptly instituting preventions are of critical importance. This study aims to determine the efficacy and values of Hounsfield unit (HU) values, vertebral bone quality (VBQ) scores, bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA), and quantitative computed tomography (QCT) to discriminate between patients with and without vertebral fractures.

METHODS

A thorough search was conducted across PubMed, EMBASE, Cochrane Library, Web of Science, CNKI, and Wan Fang Database to identify potential studies that met the eligibility criteria. Studies that evaluated the utility of HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD in discriminating vertebral fractures were qualified.

RESULTS

The combined results showed that there were significant differences in HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD between the fracture and non-fracture groups. Moreover, the pooled sensitivity, specificity, and AUC of HU values were 0.82, 0.67, and 0.76, respectively; the pooled sensitivity, specificity, and AUC of VBQ scores were 0.70, 0.75, and 0.78; the pooled sensitivity, specificity, and AUC of QCT-measured BMD were 0.85, 0.76 and 0.88.

CONCLUSION

All four methods, namely HU values, VBQ scores, QCT-measured BMD, and DXA-measured BMD can effectively distinguish between patients with and without vertebral fragile fractures. Among these, QCT-measured BMD exhibited a relatively high efficacy in discriminating vertebral fractures. VBQ scores and HU values demonstrated comparable efficacy for discriminating vertebral fractures among elderly patients.

KEY POINTS

Question Can four different imaging modalities effectively discriminate vertebral fragility fracture status among elderly patients? Findings These methods can effectively distinguish vertebral fractures status among elderly patients, and quantitative computed tomography (QCT)-measured bone mineral density (BMD) exhibited a relatively high efficacy. Clinical relevance The clinical applications of Hounsfield unit values, vertebral bone quality scores, and BMD measured by dual-energy X-ray absorptiometry and QCT show promising outcomes in identifying individuals at high risk of vertebral fractures.

Automated vertebrae identification and segmentation with structural uncertainty analysis in longitudinal CT scans of patients with multiple myeloma

OBJECTIVES

Optimize deep learning-based vertebrae segmentation in longitudinal CT scans of multiple myeloma patients using structural uncertainty analysis.

MATERIALS & METHODS

Retrospective CT scans from 474 multiple myeloma patients were divided into train (179 patients, 349 scans, 2005-2011) and test cohort (295 patients, 671 scans, 2012-2020). An enhanced segmentation pipeline was developed on the train cohort. It integrated vertebrae segmentation using an open-source deep learning method (Payer's) with a post-hoc structural uncertainty analysis. This analysis identified inconsistencies, automatically correcting them or flagging uncertain regions for human review. Segmentation quality was assessed through vertebral shape analysis using topology. Metrics included 'identification rate', 'longitudinal vertebral match rate', 'success rate' and 'series success rate' and evaluated across age/sex subgroups. Statistical analysis included McNemar and Wilcoxon signed-rank tests, with p < 0.05 indicating significant improvement.

RESULTS

Payer's method achieved an identification rate of 95.8% and success rate of 86.7%. The proposed pipeline automatically improved these metrics to 98.8% and 96.0%, respectively (p < 0.001). Additionally, 3.6% of scans were marked for human inspection, increasing the success rate from 96.0% to 98.8% (p < 0.001). The vertebral match rate increased from 97.0% to 99.7% (p < 0.001), and the series success rate from 80.0% to 95.4% (p < 0.001). Subgroup analysis showed more consistent performance across age and sex groups.

CONCLUSION

The proposed pipeline significantly outperforms Payer's method, enhancing segmentation accuracy and reducing longitudinal matching errors while minimizing evaluation workload. Its uncertainty analysis ensures robust performance, making it a valuable tool for longitudinal studies in multiple myeloma.

Conventional chest computed tomography-based radiomics for predicting the risk of thoracolumbar osteoporotic vertebral fractures

Our study focused on predicting thoracolumbar osteoporotic vertebral fractures through radiomic analysis of non-fractured thoracic vertebrae using conventional chest CT. Four types of radiomics models were developed and showed acceptable prediction performance. Radiomics models incorporating both cortical-appendicular and trabecular bone may have superior performance compared to those using either feature set individually. The RAD score models based on thoracic vertebral combinations achieved comparable performance with lumbar bone mineral density (BMD) measurements.

PURPOSE

To develop and validate radiomics models based on chest CT for predicting the risk of thoracolumbar osteoporotic vertebral fractures (OVFs).

METHODS

A total of 494 patients (including 198 patients with thoracolumbar OVFs) who underwent conventional chest CT scans were included in this retrospective analysis and were divided into training set 1 (n = 334) and validation set 1 (n = 160). Radiomics features (RFs) were extracted from each thoracic vertebral level on chest CT images. Four types of radiomics models (trabecular RFs, cortical-appendicular RFs, mixed RFs, and RAD score) were constructed and compared. Additionally, RAD score models based on trabecular and cortical-appendicular bone of different vertebral combinations (T1-T6, T7-T12, and top 3 vertebrae) were performed, respectively. A subset of patients with available bone mineral density (BMD) data formed training set 2 (n = 199) and validation set 2 (n = 88). We combined RAD score of different vertebral combinations with lumbar BMD for predicting thoracolumbar OVFs, and further adjusted for age. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

Among the radiomics models, the RAD score model based on trabecular and cortical-appendicular bone achieved highest AUC at the most vertebral levels. The RAD score model of top 3 (T5 + T8 + T10) vertebrae achieved higher AUC (0.813) than T7-T12 (AUC = 0.780) with a statistically significant difference (P = 0.02) and T1-T6 (AUC = 0.772) without a statistically significant difference (P = 0.062). Prior to adjusting for age, both RAD score models (AUCs 0.774-0.807) and RAD score + BMD models (AUCs 0.771-0.800) demonstrated slightly superior performance compared to BMD (AUC = 0.736) alone in predicting OVFs, although the differences were not statistically significant (P > 0.05). Following adjustment for age, our RAD score models, which utilized different vertebral combinations (AUCs 0.784-0.804), were found to be comparable to lumbar BMD (AUC = 0.785) in predicting OVFs (P > 0.05).

CONCLUSION

Radiomics analysis based on conventional chest CT can provide valuable information for predicting thoracolumbar OVFs. Radiomics models incorporating both cortical-appendicular and trabecular bone may have superior performance compared to those using either feature set alone. RAD score models based on thoracic vertebral combinations comparable performance compared to lumbar BMD highlights its clinical utility.

Automatic phantom-less calibration of routine CT scans for the evaluation of osteoporosis and hip fracture risk

BACKGROUND/PURPOSE

The diagnosis of osteoporosis remains a paramount concern for orthopedic surgeons worldwide. We aim to (1) evaluate the efficacy of automatic phantom-less quantitative computed tomography (PL-QCT) in diagnosing osteoporosis and (2) investigate its clinical value in predicting hip fracture risk.

METHODS

A cohort of 705 patients was included in the study. Hip CT scans from 310 patients and spinal CT scans from 315 patients were analyzed using automatic PL-QCT. The consistency of bone mineral density (BMD) measurement obtained by dual-energy X-ray absorptiometry (DXA), phantom-based QCT (PB-QCT), and automatic PL-QCT was examined through linear regression analysis and Bland-Altman plots. The ability of automatic PL-QCT to predict osteoporosis and hip fracture risk was assessed using ROC analysis.

RESULTS

Linear regression and Bland-Altman plots demonstrated a high level of agreement between BMD measurements from PL-QCT and those from hip DXA and lumbar PB-QCT. The AUC values for PL-QCT and PB-QCT in diagnosing osteoporosis were 0.903 (95 % CI 0.852-0.955) and 0.900 (95 % CI 0.847-0.953). The AUC values for predicting hip fracture risk, based on femoral neck BMD measured by PL-QCT and DXA, were 0.869 (95 % CI 0.823-0.915) and 0.831(95 % CI 0.778-0.885), respectively. When the femoral neck BMD was combined with the percentage of inter-muscular adipose tissue area, the AUC increased to 0.929 (95 % CI 0.897-0.961).

CONCLUSION

Automatic PL-QCT has shown superior performance in predicting hip fracture risk compared to DXA. Furthermore, the novel PL-QCT demonstrates comparable predictive efficacy to that of PB-QCT, suggesting its potential as a valuable tool in clinical practice.

Machine Learning-Based CT Radiomics Model to Predict the Risk of Hip Fragility Fracture

RATIONALE AND OBJECTIVES

This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.

METHOD

A total of 254 patients (training cohort, n=132; test cohort 1, n=56；test cohort 2, n=66) who underwent hip or pelvic CT scans were included. Three different machine learning methods were used to build the Support Vector Machine (SVM) model, Logistic Regression (LR) model and Random Forest (RF) model respectively. The method that exhibited the best performance in the training cohort and test cohort 1 was selected to represent the radiomics model for subsequent studies. The mean CT Hounsfield unit of three-dimensional CT images at the proximal femur was extracted to construct the mean CTHU model. Multivariate logistic regression was performed using mean CT Hounsfield unit together with radiomics features, and the combined model was subsequently developed with a visualized nomogram.

RESULTS

Among the radiomics models based on three machine learning methods, the LR model showed the best performance in the training cohort (AUC=0.875, 95% CI=0.806-0.926) and in the test cohort 1 (AUC=0.851, 95% CI=0.730-0.932). Compared to the mean CT model and the LR model, the combined model showed superior discriminatory power in the training cohort (AUC=0.934, 95% CI=0.895-0.972), the test cohort 1 (AUC=0.893, 95% CI=0.812-0.974) and the test cohort 2 (AUC=0.851, 95% CI=0.742-0.927).

CONCLUSION

The combined model, based on the mean CT Hounsfield unit of the proximal femur and radiomics features, can provide an accurate quantitative imaging basis for individualized risk prediction of hip fragility fracture.

Cervical Vertebra Bone Quality Score Predicts Zero-Profile Anchored Spacer Interbody Fusion Cage Subsidence after Anterior Cervical Diskectomy and Fusion: A Retrospective Study

Study DesignRetrospective study.ObjectiveThis retrospective study primary focus is to investigate the relationship between the C-VBQ score and the occurrence of postoperative zero-profile anchored spacer (ROI-C) interbody fusion cage subsidence. Additionally, we aim to evaluate the predictive efficacy of the C-VBQ scoring system for subsidence in the context of ACDF with the ROI-C.MethodsPatients who underwent ACDF with the ROI-C cage at our hospital between January 2016 and December 2022 were included in this study. Univariate analysis and multivariate logistic regression were employed to identify independent risk factors associated with ROI-C cage subsidence after ACDF. Pearson correlation analysis was utilized to assess the correlation between the C-VBQ score and the height of ROI-C cage subsidence.ResultsA total of 102 patients underwent ACDF with ROI-C in our hospital were included in this study. Univariate analysis showed that age (P = 0.021) and C-VBQ score (P < 0.001) were the influencing factors of cage subsidence. Pearson correlation analysis showed that there was a significant positive correlation between the subsidence height of ROI-C cage and C-VBQ (r = 0.55, P < 0.01). Multivariate binary logistic regression analysis showed that C-VBQ score was the only variable that could significantly predict the subsidence of ROI-C cage after ACDF. Higher C-VBQ score was significantly associated with cage subsidence (P < 0.001).The AUC was 0.89, and the cutoff value for C-VBQ was 2.70.ConclusionThe findings indicate a significant correlation between a higher C-VBQ score before surgery and ROI-C cage subsidence after ACDF. The preoperative assessment of C-VBQ proves valuable for clinicians, enabling them to identify patients with low bone mineral density and predict the risk of zero-profile anchored spacer interbody fusion cage subsidence following ACDF.

Automated opportunistic screening for osteoporosis using deep learning-based automatic segmentation and radiomics on proximal femur images from low-dose abdominal CT

RATIONALE AND OBJECTIVES

To establish an automated osteoporosis detection model based on low-dose abdominal CT (LDCT). This model combined a deep learning-based automatic segmentation of the proximal femur with a radiomics-based bone status classification.

MATERIALS AND METHODS

A total of 456 participants were retrospectively included and were divided into a development cohort comprising 355 patients, with a 7:3 ratio randomly assigned to the training and validation cohorts, and a test cohort comprising 101 patients. The automatic segmentation model for the proximal femur was trained using VB-Net. The Dice similarity coefficient (DSC) and volume difference (VD) were employed to evaluate the performance of the segmentation model. A three-classification predictive model for assessing bone mineral status was constructed utilizing radiomic analysis. The diagnostic performance of the radiomics model was assessed using the area under the curve (AUC), sensitivity, and specificity.

RESULTS

The automatic segmentation model for the proximal femur demonstrated excellent performance, achieving DSC values of 0.975 ± 0.012 and 0.955 ± 0.137 in the validation and test cohorts, respectively. In the test cohort, the radiomics model utilizing the random forest (RF) classifier achieved AUC values, sensitivity, and specificity of 0.924 (95% CI: 0.854-0.967), 0.846 (95% CI: 0.719-0.931), and 0.837 (95% CI: 0.703-0.927) for the identification of normal bone mass. For the identification of osteoporosis, the corresponding metrics were 0.960 (95% CI: 0.913-1.000), 0.947 (95% CI: 0.740-0.999), and 0.963 (95% CI: 0.897-0.992). In the case of osteopenia, the corresponding metrics were 0.828 (95% CI: 0.747-0.909), 0.767 (95% CI: 0.577-0.901), and 0.746 (95% CI: 0.629-0.842).

CONCLUSION

A three-classification predictive model combining a deep learning-based automatic segmentation of the proximal femur and a radiomics-based bone status classification on LDCT images can be used for the opportunistic detection of osteoporosis.

Opportunistic osteoporosis screening in intraoperative CT can accurately identify patients with low volumetric bone mineral density and osteoporosis during spine surgery

PURPOSE

To evaluate the accuracy of opportunistic measurements of volumetric bone mineral density (vBMD) in intraoperative multi-detector CT (MDCT) scans, using preoperative MDCT as the reference.

METHODS

This retrospective, single-center study included 105 patients (mean age: 73 ± 12.6 years, 53 women) who underwent spine surgery for various indications. All patients had preoperative MDCT with/without intravenous contrast and unenhanced intraoperative scans. VBMD of thoracolumbar vertebrae was automatically extracted using a convolutional neural network (CNN)-based framework with asynchronous calibration and contrast-phase correction. Vertebrae affected by artifacts, fractures, or severe degenerations were excluded. Root-mean-square errors (RMSEs) for associations between pair-wise vertebrae from preoperative and intraoperative vBMD values were calculated in linear regression models. Mean bias and 95%-limits of agreement (LOA) were calculated in Bland-Altman plots.

RESULTS

Strong associations between preoperative and intraoperative vBMD values were observed in the thoracic (R2 = 0.94) and lumbar spine (R2 = 0.96). Intraoperative vBMD values showed high accuracy in reference to preoperative measurements with a mean bias of -1.3 mg/cm3 for the thoracic spine (LOA: -18.7 to 16.1 mg/cm3) and - 3.0 mg/cm3 for the lumbar spine (LOA: -17.4 to 11.3 mg/cm3). RMSEs between preoperative and intraoperative vBMD values slightly increased for contrast-enhanced scans (RMSEthoracic: 8.42 vs. 10.1 mg/cm3; RMSElumbar: 7.75 vs. 8.87 mg/cm3).

CONCLUSION

Opportunistic osteoporosis screening with the presented approach is feasible and demonstrates high accuracy in reference to preoperative MDCT scans. This could enable the identification of patients with low bone mass during surgery, allowing surgeons to take measures (e.g., adapted techniques) that prevent postoperative complications and improve patient outcomes.

Automated Fast Prediction of Bone Mineral Density From Low-dose Computed Tomography

BACKGROUND

Low-dose chest CT (LDCT) is commonly employed for the early screening of lung cancer. However, it has rarely been utilized in the assessment of volumetric bone mineral density (vBMD) and the diagnosis of osteoporosis (OP).

PURPOSE

This study investigated the feasibility of using deep learning to establish a system for vBMD prediction and OP classification based on LDCT scans.

METHODS

This study included 551 subjects who underwent both LDCT and QCT examinations. First, the U-net was developed to automatically segment lumbar vertebrae from single 2D LDCT slices near the mid-vertebral level. Then, a prediction model was proposed to estimate vBMD, which was subsequently employed for detecting OP and osteopenia (OA). Specifically, two input modalities were constructed for the prediction model. The performance metrics of the models were calculated and evaluated.

RESULTS

The segmentation model exhibited a strong correlation with manual segmentation, achieving a mean Dice similarity coefficient (DSC) of 0.974, sensitivity of 0.964, positive predictive value (PPV) of 0.985, and Hausdorff distance of 3.261 in the test set. Linear regression and Bland-Altman analysis demonstrated strong agreement between the predicted vBMD from two-channel inputs and QCT-derived vBMD, with a root mean square error of 8.958 mg/mm3 and an R2 of 0.944. The areas under the curve for detecting OP and OA were 0.800 and 0.878, respectively, with an overall accuracy of 94.2%. The average processing time for this system was 1.5 s.

CONCLUSION

This prediction system could automatically estimate vBMD and detect OP and OA on LDCT scans, providing great potential for the osteoporosis screening.

Musculoskeletal characteristics in older adults with overweight or obesity: INVEST in Bone Health trial baseline analysis

OBJECTIVE

The objective of this study was to examine associations of computed tomography (CT)-derived musculoskeletal measures with demographics and traditional musculoskeletal characteristics.

METHODS

The Incorporating Nutrition, Vests, Education, and Strength Training (INVEST) in Bone Health trial (NCT04076618) acquired a battery of musculoskeletal measures in 150 older-aged adults living with overweight or obesity. At baseline, CT (i.e., volumetric bone mineral density, cortical thickness, muscle radiomics, and muscle/intermuscular adipose tissue [IMAT] area and density), dual-energy x-ray absorptiometry (DXA; i.e., areal bone mineral density, total body fat mass, appendicular lean mass, and lean body mass), and strength assessments (i.e., grip and knee extensor strength) were collected, along with demographic and clinical characteristics. Analyses employed linear regression and mixed-effects models along with factor analysis for dimensionality reduction of the radiomics data.

RESULTS

Participants were older-aged (mean [SD] age: 66 [5] years), mostly female (75%), and were living with overweight or obesity (mean [SD] BMI: 33.6 [3.3] kg/m2). Age was not significantly associated with most CT-derived bone, IMAT, or muscle measures. BMI was significantly associated with DXA and CT-derived muscle and IMAT measures, which were higher in male than female individuals (all p < 0.01). For the midthigh, muscle size was significantly related to grip and knee extensor strength (both p < 0.01).

CONCLUSIONS

Machine learning-derived CT metrics correlated strongly with DXA and muscle strength, with higher BMI linked to greater IMAT and poorer muscle quality.

Longitudinal assessment of changes in muscle composition using proton density fat fraction and T2* in patients with and without incidental vertebral compression fractures

Objective

Chemical shift encoded-based water-fat separation magnetic resonance imaging (CSE-MRI) is an emerging noninvasive tool for the assessment of bone and muscle composition. This study aims to examine both the predictive value and the longitudinal change of proton density fat fraction (PDFF) and T2* in the paraspinal muscles (PSM) in patients with and without the development of an incidental vertebral compression fracture (VCFs) after 6 months of follow-up.

Methods

Patients (N=56) with CT and 3T CSE-MRI of the lumbar spine at baseline and CSE-MRI at 6 months follow-up were included in this retrospective study. Patients who, on average, developed an incidental VCF one year after baseline MRI (VCF: N=14, 9 males, 66.8 ± 7.9 years) were frequency matched by age and sex to patients without VCFs (non-VCF) at baseline and follow-up (non-VCF: N=42, 27 males, 64.6 ± 13.3 years). Mean PDFF, T2*, and cross-sectional area (CSA) values from the autochthonous PSM of the thoracolumbar spine (T11-L4) and opportunistic CT-based bone mineral density (BMD) measurements were obtained for each individual. The associations between baseline measurements, longitudinal changes in PDFF, T2*, CSA of the PSM and the occurrence of VCFs at follow-up were evaluated using linear and logistic multivariable regression models. ROC analyses were used to assess cutoff values for predicting the development of VCFs.

Results

No significant difference in PDFF of the PSM was found between the VCF and non-VCF group at baseline (VCF/non-VCF 8.5 ± 13.8% vs. 5.0 ± 4.6%; p=0.53). In multivariable linear regression models adjusted for sex, age and baseline BMD, PDFF values of the PSM increased significantly over 6 months in the VCF group (2.4 ± 2.8% vs. -1.0 ± 2.3%, p<0.001), while T2* values of the PSM showed a significant decrease (p ≤ 0.01). ROC analyses identified a PDFF increase of 0.2% in the PSM as the optimal cutoff value to distinguish between patients with and without VCF (AUC 0.86, 95% CI [0.74-0.98], p<0.001).

Conclusion

Longitudinal PDFF-based assessment of the PSM composition may be a useful indicator for the prediction of the development of vertebral compression fractures.

Undetected low bone mineral density in patients undergoing lumbar fusion surgery-prevalence and risk factors

Background

Sufficient bone quality is a prerequisite for low complication rates and satisfactory outcomes in lumbar fusion surgery (LFS). Low bone mineral density (BMD), including osteoporosis and osteopenia, is linked to adverse postoperative outcomes. Despite reports of a high prevalence of undiagnosed osteoporosis, it is uncertain which risk factors should guide preoperative BMD screening in LFS.

Methods

This secondary cross-sectional analysis of a prospective institutional database at an academic spine center included adult patients undergoing LFS for degenerative conditions between 2014 and 2023. Opportunistic quantitative CT (qCT) at the L1/2 level was performed before surgery, and demographic and medical history data were extracted. Descriptive and comparative statistics, univariable and multivariable logistic regression were performed to determine risk factors for present and undiagnosed osteoporosis.

Results

Of the 675 patients screened, 578 (54% female) were included after excluding those with preoperative lumbar CT scans not suitable for qCT. The median age was 65 years (IQR 58-72), and the median BMI of 28.9 kg/m2 (IQR 25.2-32.9). Osteoporosis was identified in 182 patients (31%), with 114 previously diagnosed and 68 newly detected via preoperative qCT. Undiagnosed osteoporosis was found in 12% of all patients and 37% of those with osteoporosis. Osteopenia was present in 199 patients (34%), leading to an overall impaired bone quality prevalence of 66%. Multivariable analysis revealed that age and female sex were independent risk factors for osteoporosis, while undiagnosed cases were more common in males, patients with higher BMI, and older individuals.

Conclusions

This study found a high prevalence of abnormal BMD in LFS patients, with a significant proportion of undiagnosed osteoporosis. While osteoporosis was more common in females, male patients with osteoporosis were more frequently undiagnosed. Spine surgeons must remain vigilant about metabolic bone disease in LFS patients to ensure preoperative optimization and prevent complications.

Cervicothoracic volumetric bone mineral density assessed by opportunistic QCT may be a reliable marker for osteoporosis in adults

This study aimed to validate the correlation between volumetric bone mineral density in the cervicothoracic and lumbar spine using measurements from opportunistic CT scans. The bone density assessment proved feasible, allowing us to propose optimal cut-off values for diagnosing osteoporosis and predicting vertebral fractures in the cervical and thoracic spine.

OBJECTIVES

To investigate the performance of cervicothoracic volumetric bone mineral density (vBMD), obtained through opportunistic quantitative computed tomography (QCT), in discriminating patients with/without osteoporosis and with/without vertebral fractures (VFs), using lumbar vBMD as the reference.

METHODS

Three hundred twenty-five patients (65.3 ± 19.2 years, 140 women) with routine non-contrast or contrast-enhanced multi-detector CT (MDCT) scans were included. Trabecular vBMD was automatically extracted from each vertebra using a convolutional neural network (CNN)-based framework (SpineQ software v1.0) with asynchronous calibration and contrast phase correction. The correlations of vBMD between each vertebra spanning C2-T12 and the averaged lumbar spine (L1-L3, or L4 and L5) vBMD values were analyzed, considering fracture status and degeneration. Vertebra-specific linear regression equations were used to approximate lumbar vBMD at the cervicothoracic spine.

RESULTS

Cervicothoracic vBMD correlated well with lumbar vBMD (r = 0.79), with significant improvement after excluding degenerated vertebrae (p < 0.05; r = 0.89), except for C7-T3 and T9. Cervical (AUC = 0.94) and thoracic vBMD (AUC = 0.97) showed strong discriminatory ability for osteoporosis (vBMD < 80 mg/cm3). Excluding degenerated vertebrae at the cervical spine increased the AUC to 0.97. Cervical and thoracic vBMD (AUC = 0.74, AUC = 0.72) were comparable to lumbar vBMD (AUC = 0.72) in differentiating patients with and without prevalent VFs. Trabecular vBMD < 190 mg/cm3 for the cervical spine and < 100 mg/cm3 for the thoracic spine were potential indicators of osteoporosis, similar to < 80 mg/cm3 at the lumbar spine.

CONCLUSION

Cervicothoracic vBMD may allow for determination of osteoporosis and prediction of VFs.

Photon-counting CT Spectral Localizer Radiographs for Lumbar Areal Bone Mineral Density Quantification: A Clinical Study on Accuracy, Reliability, and Diagnostic Performance for Osteoporosis

RATIONALE AND OBJECTIVES

To explore the accuracy, reliability, and diagnostic performance of photon-counting CT (PCCT) spectral localizer radiographs (SLRs) for quantifying lumbar areal bone mineral density (BMD) and detecting osteoporosis (T-score ≤-2.5).

MATERIALS AND METHODS

This prospective study recruited consecutive participants from April to July 2024. Participants each underwent a dual-energy X-ray absorptiometry (DXA) examination serving as the gold-standard reference for aBMD (aBMDDXA) and a PCCT scan to obtain SLR. The SLRs were reconstructed into hydroxyapatite (HA) and water maps. Lumbar vertebrae (L1 to L4) and soft tissue were blindly and semiautomatically segmented on HA and water maps to calculate aBMDSLR. The agreement and relative absolute error (RAE) between aBMDSLR and aBMDDXA were calculated. Factors that might influence the RAE were evaluated. Using DXA results as the reference, the diagnostic performance of PCCT-SLRs for osteoporosis was assessed.

RESULTS

A total of 159 participants (88 females) with a median age of 66 years (interquartile range [IQR], 55-72 years) were included. The median (IQR) aBMDDXA and aBMDSLR values were 1.095 (0.936-1.261) g/cm2 and 1.086 (0.932-1.255) g/cm2, respectively. There was excellent agreement between the two methods (mean bias=-0.57%). The median (IQR) RAE was 2.65% (1.23-4.07%). The RAE was unaffected by age, body mass index, aBMD, sex, tube voltage, or tube current. The sensitivity and specificity of PCCT-SLRs for osteoporosis diagnosis were 92.31% (12/13) and 98.63% (144/146), respectively.

CONCLUSION

The PCCT-SLR is an accurate and reliable approach for lumbar aBMD quantification in humans, with high diagnostic performance for osteoporosis.

Assessment of trabecular bone Hounsfield units in the lumbar spine for osteoporosis evaluation in individuals aged 65 and above: a review

Osteoporosis is a prevalent condition that significantly increases fracture risk, particularly in the elderly population. Despite its widespread occurrence, osteoporosis is often underdiagnosed and inadequately managed. Traditional diagnostic methods, such as dual-energy X-ray absorptiometry (DXA), have limitations in terms of accessibility and accuracy, necessitating exploration of alternative diagnostic approaches.This review aims to evaluate the diagnostic potential of Hounsfield Unit (HU) values derived from abdominal computed tomography (CT) scans, specifically focusing on the trabecular bone of the lumbar spine, for osteoporosis assessment in individuals aged 65 and older. The review seeks to assess the sensitivity, specificity, and overall diagnostic performance of HU values in distinguishing between normal bone density, osteopenia, and osteoporosis, and to identify areas for further investigation to establish standardized diagnostic criteria.This review compiles existing studies on the use of HU values from abdominal CT scans for osteoporosis diagnosis. It examines the relationship between HU values and DXA T-scores, analyzes optimal HU thresholds for classifying bone density categories, and explores the potential of CT scans as a viable alternative to DXA.The findings indicate that HU values from abdominal CT scans show strong correlations with DXA T-scores, suggesting a promising diagnostic tool for assessing bone density and quality. HU values have demonstrated the ability to differentiate between osteopenia, osteoporosis, and normal bone density, with varying sensitivity and specificity depending on the established HU threshold. CT scans are identified as a scalable, cost-effective alternative to DXA, with the added benefit of utilizing routine abdominal CT scans, which are often conducted for other clinical reasons, thereby reducing additional costs and radiation exposure.HU values derived from abdominal CT scans represent a promising approach for osteoporosis screening, offering a potential solution for routine, cost-effective, and accurate diagnosis, especially in older adults. However, there is a need for standardized HU thresholds and further research to refine diagnostic criteria and enhance the accuracy of osteoporosis detection. Establishing standardized guidelines would improve diagnostic consistency and facilitate early intervention, potentially improving patient outcomes and reducing healthcare burdens.

Enhancing fracture risk indication: The impact of bone load index and muscle fat infiltration on vertebral compression fracture

PURPOSE

This study aims to identify risk factors for vertebral compression fracture and enhance the ability to indicate fracture risk.

METHODS

A retrospective collection of clinical and imaging data was conducted for patients with vertebral compression fractures and control subjects who underwent quantitative computed tomography scans. Stepwise logistic regression analysis was employed to identify variables associated with fractures, constructing both unadjusted model and adjusted model.

RESULTS

Compared with the non-fracture group, the fracture group showed significant differences in weight, body mass index (BMI), bone mineral density (BMD), vertebral cross-sectional area, paraspinal muscle area and right psoas major muscle fat area (all P < 0.05). Adjusted characteristics analyzed by stepwise logistic regression indicated that bone load index (BLI) (OR = 3.19, P = 0.041), paraspinal muscle fat infiltration (PMFI) (OR = 2.27, P = 0.039), and right psoas major muscle fat infiltration (RPMFI) (OR = 1.08, P = 0.005) were independent risk factors for vertebral fractures. Interaction analysis revealed a positive interaction between BLI and PMFI (OR = 1.95, P = 0.008) as well as RPMFI (OR = 1.53, P = 0.045). Compared with the unadjusted model, the diagnostic performance of the adjusted model was significantly improved (training set IDI: 19.5 %, validation set IDI: 18.4 %, P < 0.001). Correlation analysis demonstrated significant associations between BMD (r = -0.353, P = 0.002), BLI (r = 0.631, P < 0.001), PMFI (r = 0.412, P < 0.001), RPMFI (r = 0.513, P < 0.001), and the degree of vertebral compression.

CONCLUSION

Under conditions of bone maladaptive loading and muscle degeneration, vertebral bodies may become more susceptible to external forces, increasing the risk of vertebral compression fracture.

Radiomics models based on thoracic and upper lumbar spine in chest LDCT to predict low bone mineral density

This study aims to develop and validate different radiomics models based on thoracic and upper lumbar spine in chest low-dose computed tomography (LDCT) to predict low bone mineral density (BMD) using quantitative computed tomography (QCT) as standard of reference. A total of 905 participants underwent chest LDCT and paired QCT BMD examination were retrospectively included from August 2018 and June 2019. The patients with low BMD (n = 388) and the normal (n = 517) were randomly divided into a training set (n = 622) and a validation set (n = 283). Radiomics features (RFs) were extracted from the single and consecutive vertebrae in chest LDCT images to construct the single vertebra RFs models, mixed RFs models and Radscore models, respectively. The performance of these models was evaluated by the area under the curve (AUC) of receiver operator characteristic curve, using QCT as standard of reference. The Radscore models, mixed RFs models, and single vertebra RFs models yielded the AUC values ranging from 0.809 to 0.906, 0.792 to 0.883, and 0.731 to 0.884 for predicting low BMD in the validation set, respectively. For predicting low BMD, the Radscore model of L1-L2 vertebrae yielded the highest AUC of 0.906, and of T1-T3 yielded the lowest AUC of 0.809 (P < 0.05), respectively. However, there was no significant difference among the AUC values of three Radscore models constructed on the vertebrae of T4-T6 (AUC = 0.855), T7-T9 (AUC = 0.845), and T10-T12 (AUC = 0.871) for predicting low BMD in the validation set (P > 0.1). The Radscore model of L1-L2 have potential to serve as an important tool for predicting and screening low BMD from normal in chest LDCT images.

Correlation Between Osteoporosis and Endplate Damage in Degenerative Disc Disease Patients: A Study Based on Phantom-Less Quantitative Computed Tomography and Total Endplate Scores

BACKGROUND

Osteoporosis and degenerative disc disease (DDD) are prevalent in the elderly population. Damage to the vertebral endplate, which impairs nutrient supply to the disc, serves as both a significant initiator and a hallmark of DDD. This study was aimed to explore the association between osteoporosis and endplate damage.

METHODS

This retrospective study included 205 patients with DDD who were treated at Tianjin Hospital from January 2019 to May 2023. We collected data on age, sex, body mass index, phantom-less quantitative computed tomography (PL-QCT) values, and total endplate scores (TEPS). The average PL-QCT value of L1-L4 and TEPS were used to represent volumetric bone mineral density (BMD) and the degree of endplate damage, respectively. Based on the average PL-QCT value of L1 and L2, patients were divided into 3 groups: normal group (BMD > 120 mg/cm3), osteopenic group (80 mg/cm3 ≤ BMD ≤ 120 mg/cm3), and osteoporosis group (BMD < 80 mg/cm3). Multiple linear regression models were used to identify independent factors associated with endplate damage.

RESULTS

The overall TEPS (4.3 ± 1.3 vs. 5.0 ± 1.0 vs. 5.9 ± 1.5, P < 0.01) and segment (L1/2-L4/5) TEPS (P < 0.05) in each group showed significant difference (R = -0.5), increasing in order from normal group to osteoporosis group. A significant negative correlation was found between TEPS and PL-QCT values in overall and each segment (P < 0.001). The PL-QCT values and age (P < 0.05) were independent factors influencing endplate damage. There were significant differences in the average number of TEPS ≥7 segments per patient among the 3 groups, with 1.16, 0.41, and 0.2 segments/person from osteoporosis group to normal group.

CONCLUSIONS

Our study showed a significant positive correlation between osteoporosis and endplate damage. Attention is warranted for patients with osteopenia to prevent progression to osteoporosis, potentially leading to exacerbated DDD. The management of patients with both DDD and osteoporosis necessitates comprehensive treatment strategies that address both the BMD and endplate aspects of these conditions.

Diagnostic Accuracy of Dual-Energy CT-Derived Metrics for the Prediction of Osteoporosis-Associated Fractures

RATIONALE AND OBJECTIVES

This study aimed to compare the diagnostic value of dual-energy CT (DECT)-based volumetric material decomposition with that of Hounsfield units (HU)-based values and cortical thickness ratio for predicting the 2-year risk of osteoporosis-associated fractures.

METHODS

The L1 vertebrae of 111 patients (55 men, 56 women; median age, 62 years) who underwent DECT between 01/2015 and 12/2018 were retrospectively analyzed. For phantomless bone mineral density (BMD) assessment, a specialized DECT postprocessing software employing material decomposition was utilized. The digital records of all patients were monitored for two years after the DECT scans to track the incidence of osteoporotic fractures. Diagnostic accuracy parameters were calculated for all metrics using receiver-operating characteristic (ROC) and precision-recall (PR) curves. Logistic regression models were used to determine associations of various predictive metrics with the occurrence of osteoporotic fractures.

RESULTS

Patients who sustained one or more osteoporosis-associated fractures in a 2-year interval were significantly older (median age 74.5 years [IQR 57-83 years]) compared those without such fractures (median age 50.5 years [IQR 38.5-69.5 years]). According to logistic regression models, DECT-derived BMD was the sole predictive parameter significantly associated with osteoporotic fracture occurrence across all age groups. ROC and PR curve analyses confirmed the highest diagnostic accuracy for DECT-based BMD, with an area under the curve (AUC) of 0.95 [95% CI: 0.89-0.98] for the ROC curve and an AUC of 0.96 [95% CI: 0.85-0.99] for the PR curve.

CONCLUSION

The diagnostic performance of DECT-based BMD in predicting the 2-year risk of osteoporotic fractures is greater than that of HU-based metrics and the cortical thickness ratio. DECT-based BMD values are highly valuable in identifying patients at risk for osteoporotic fractures.

Lumbar Magnetic Resonance Imaging Shows Sex-Specific Alterations During Musculoskeletal Aging-A Radio-Anatomic Investigation Involving 202 Individuals

Background/Objectives: Musculoskeletal aging can clinically hardly be distinguished from degenerative disease, especially if symptoms are nonspecific, like lower back pain and reduced physical resilience. However, age-related changes are considered to be physiological until they cause osteoporotic fractures or sarcopenia-related restrictions. This radio-anatomic investigation examines whether findings in lumbar magnetic resonance imaging (MRI) mirror age- and sex-related musculoskeletal differences that help to identify the onset of sarcopenia. Methods: Lumbar MRI investigations from 101 women and 101 men were retrospectively evaluated for vertebral and muscular cross-sectional diameter sizes and T2-signal intensities ("T2-brightness") in axial sections in the L5-level. The results were correlated with the individual's age to find specific alterations that were indicative of sarcopenia or attributable to the aging process. Results: In women (average age 62.6 (34-85) years), musculoskeletal cross-sectional area sizes and diameters were significantly smaller (p < 0.00001) compared to those in men (average age 57.0 (21-90) years). The most pronounced structural age-related change was the increasing mean posterior paravertebral muscle brightness (MPPVB), which exceeded the mean vertebral brightness (MVB) earlier and to a greater extent in women than in men (p < 0.00001). The brightness difference (∆MVB - MPPVB) was found to indicate (pre-)sarcopenia at values below 25. Conclusions: Significant age-related deterioration in muscle quantity and quality was more obvious in women, correlated with the onset of menopause, and progressed to lower levels during aging.

Prediction of Bone Mineral Density based on Computer Tomography Images Using Deep Learning Model

INTRODUCTION

The problem of population aging is intensifying worldwide. Osteoporosis has become an important cause affecting the health status of older populations. However, the diagnosis of osteoporosis and people's understanding of it are seriously insufficient. We aim to develop a deep learning model to automatically measure bone mineral density (BMD) and improve the diagnostic rate of osteoporosis.

METHODS

The images of 801 subjects with 2,080 vertebral bodies who underwent chest or abdominal paired computer tomography (CT) and quantitative computer tomography (QCT) scanning was retrieved from June 2020 to January 2022. The BMD of T11-L4 vertebral bodies was measured by QCT. Developing a multistage deep learning-based model to simulate the segmentation of the vertebral body and predict BMD. The subjects were randomly divided into training dataset, validation dataset and test dataset. Analyze the fitting effect between the BMD measured by the model and the standard BMD by QCT. Accuracy, precision, recall and f1-score were used to analyze the diagnostic performance according to categorization criterion measured by QCT.

RESULTS

410 males (51.2%) and 391 females (48.8%) were included in this study. Among them, there were 154 (19.2%) males and 118 (14.7%) females aged 23-44; 182 (22.7%) males and 205 (25.6%) females aged 45-64; 74 (9.2%) males and 68 (8.5%) females aged 65-84. The number of vertebral bodies in the training dataset, the validation dataset, and the test dataset was 1433, 243, 404, respectively. In each dataset, the BMD of males and females decreases with age. There was a significant correlation between the BMD measured by the model and QCT, with the coefficient of determination (R2) 0.95-0.97. The diagnostic accuracy based on the model in the three datasets was 0.88, 0.91, and 0.91, respectively.

CONCLUSION

The proposed multistage deep learning-based model can achieve automatic measurement of vertebral BMD and performed well in the prediction of osteoporosis.

Dark-field radiography for the detection of bone microstructure changes in osteoporotic human lumbar spine specimens

BACKGROUND

Dark-field radiography imaging exploits the wave character of x-rays to measure small-angle scattering on material interfaces, providing structural information with low radiation exposure. We explored the potential of dark-field imaging of bone microstructure to improve the assessment of bone strength in osteoporosis.

METHODS

We prospectively examined 14 osteoporotic/osteopenic and 21 non-osteoporotic/osteopenic human cadaveric vertebrae (L2-L4) with a clinical dark-field radiography system, micro-computed tomography (CT), and spectral CT. Dark-field images were obtained in both vertical and horizontal sample positions. Bone microstructural parameters (trabecular number, Tb.N; trabecular thickness, Tb.Th; bone volume fraction, BV/TV; degree of anisotropy, DA) were measured using standard ex vivo micro-CT, while hydroxyapatite density was measured using spectral CT. Correlations were assessed using Spearman rank correlation coefficients.

RESULTS

The measured dark-field signal was lower in osteoporotic/osteopenic vertebrae (vertical position, 0.23 ± 0.05 versus 0.29 ± 0.04, p < 0.001; horizontal position, 0.28 ± 0.06 versus 0.34 ± 0.04, p = 0.003). The dark-field signal from the vertical position correlated significantly with Tb.N (ρ = 0.46, p = 0.005), BV/TV (ρ = 0.45, p = 0.007), DA (ρ = -0.43, p = 0.010), and hydroxyapatite density (ρ = 0.53, p = 0.010). The calculated ratio of vertical/horizontal dark-field signal correlated significantly with Tb.N (ρ = 0.43, p = 0.011), BV/TV (ρ = 0.36, p = 0.032), DA (ρ = -0.51, p = 0.002), and hydroxyapatite density (ρ = 0.42, p = 0.049).

CONCLUSION

Dark-field radiography is a feasible modality for drawing conclusions on bone microarchitecture in human cadaveric vertebral bone.

RELEVANCE STATEMENT

Gaining knowledge of the microarchitecture of bone contributes crucially to predicting bone strength in osteoporosis. This novel radiographic approach based on dark-field x-rays provides insights into bone microstructure at a lower radiation exposure than that of CT modalities.

KEY POINTS

Dark-field radiography can give information on bone microstructure with low radiation exposure. The dark-field signal correlated positively with bone microstructure parameters. Dark-field signal correlated negatively with the degree of anisotropy. Dark-field radiography helps to determine the directionality of trabecular loss.

Age- and Sex-Related Volumetric Density Differences in Trabecular and Cortical Bone of the Proximal Femur in Healthy Population

BACKGROUND

There are age- and sex-related increases in the prevalence of osteoporosis. Bone densitometry based on dual energy X-ray absorptiometry (DXA) is the gold standard for the assessment of bone mineral density (BMD). Three-dimensional (3D) analysis of the proximal femur (3D-DXA) allows discrimination between cortical and trabecular compartments, and it has shown a good correlation with computed tomography. We aimed to assess age- and sex-related volumetric density differences in trabecular and cortical bone using 3D-DXA and determine the reference intervals for integral volumetric (v)BMD within the Argentine population.

METHODS

Healthy female and male adult subjects (N=1,354) from Argentina were included. Hip BMD was measured using DXA, and 3D analysis was performed using 3D-Shaper software. The integral vBMD, cortical surface BMD, and trabecular vBMD (trab vBMD) were measured.

RESULTS

The study population included 73.9% women (N=1,001) and 26.13% men (N=353). We found a significant decrease in integral vBMD between 20 and 90 years in both sexes (women, -23.1%; men, -16.6%). Bone loss indicated in the integral vBMD results was mainly due to a decrease in trabecular bone in both sexes (women, -33.4%; men, -27.7%). The age-related loss of cortical bone density was less and was limited to the female population, without no age-related differences in men. Moreover, 3D-DXA allowed us to propose reference intervals for integral vBMD.

CONCLUSIONS

We found age- and sex-related bone loss between 20 and 90 years in an Argentine cohort via integral vBMD measurements using 3D-DXA, mainly due to decreases in trabecular bone in both sexes. The age-related loss of cortical bone density was less and was limited to the female population.

Opportunistic Computed Tomography: A Novel Opportunity for Osteoporosis Screening

STUDY DESIGN

Retrospective review.

OBJECTIVE

To use opportunistic computed tomography (CT) screening to determine the prevalence of osteoporosis (OP) in patients presenting with spinal fractures and the rate of identification and treatment at our institution.

BACKGROUND

OP remains a highly underdiagnosed and undertreated disease. Opportunistic abdominopelvic CT scans offer a feasible, accessible, and cost-effective screening tool for OP.

METHODS

Retrospective review of 519 patients presenting as trauma activation to the emergency department of a Level 1 Trauma Center after a spinal fracture. Patients were excluded if under the age of 18 or lacking a CT scan upon arrival in the emergency department. Hounsfield Units (HU) were measured at the L1 vertebral level on CT scans to determine bone density levels. Values of ≤100 HU were considered osteoporotic, whereas 101-150 HU were osteopenic.

RESULTS

A total of 424 patients were included. The average HU was 204.8 ± 74.3 HU. Of the patients, 16.7% were diagnosed as osteopenic and 9.9% as osteoporotic. The mean age was 65 ± 14 years for osteopenic patients and 77 ± 11 years for osteoporotic. A statistically significant inverse relationship was found between age and bone density. Of the patients, 42.5% with low bone density HU measurements had a previously documented history of OP/osteopenia. There was a statistically significant association between females and low bone density. Patients injured in a fall were statistically significantly more likely to have lower bone densities than those in motor vehicle accidents. Of the osteoporotic patients, 9.5% were treated by our institution's fragility fracture team.

CONCLUSIONS

Our study shows that among a cohort of patients with spinal fractures, 58% of patients with radiographic signs of OP are currently undiagnosed, resulting in a low treatment rate of OP. Increasing and standardizing the use of opportunistic CT scans would allow an increase in the diagnosis and treatment of OP in patients with spinal fractures. Further, opportunistic CT scans could also be useful for a broader orthopedic population at high risk of fragility fractures.

LEVEL OF EVIDENCE

Level II-therapeutic.

Using QCT for the prediction of spontaneous age- and gender-specific thoracolumbar vertebral fractures and accompanying distant vertebral fractures

PURPOSE

To investigate the value and age- and gender-specific threshold values of bone mineral density (BMD) by quantitative computed tomography (QCT) for the prediction of spontaneous thoracolumbar vertebral fractures and thoracolumbar junction fractures accompanying distant vertebral fractures.

METHODS

Among the 556 patients included, 68 patients had thoracolumbar vertebral fractures (12 patients with distant vertebral fractures, 56 patients without distant vertebral fractures) and 488 patients had no vertebral fractures. All patients were grouped by gender and age. According to the principle of Youden index, the threshold values were calculated from receiver operating characteristic (ROC) curves.

RESULTS

The threshold values for predicting thoracolumbar vertebral fractures were 89.8 mg/cm3 for all subjects, 90.1 mg/cm3 for men, and 88.6 mg/cm3 for women. The threshold values for men aged < 60 years old and ≥ 60 years old were 117.4 mg/cm3 and 87.5 mg/cm3, respectively. The threshold values for women aged < 60 years old and ≥ 60 years old were 88.6 and 68.4 mg/cm3, respectively. The threshold value for predicting spontaneous thoracolumbar junction fractures with distant vertebral fractures was 62.7 mg/cm3.

CONCLUSIONS

QCT provides a good ability to predict age- and gender-specific spontaneous thoracolumbar vertebral fractures, and to further predict spontaneous thoracolumbar junction fractures with distant vertebral fractures.

Feasibility study of opportunistic osteoporosis screening on chest CT using a multi-feature fusion DCNN model

A multi-feature fusion DCNN model for automated evaluation of lumbar vertebrae L1 on chest combined with clinical information and radiomics permits estimation of volumetric bone mineral density for evaluation of osteoporosis.

PURPOSE

To develop a multi-feature deep learning model based on chest CT, combined with clinical information and radiomics to explore the feasibility in screening for osteoporosis based on estimation of volumetric bone mineral density.

METHODS

The chest CT images of 1048 health check subjects were retrospectively collected as the master dataset, and the images of 637 subjects obtained from a different CT scanner were used for the external validation cohort. The subjects were divided into three categories according to the quantitative CT (QCT) examination, namely, normal group, osteopenia group, and osteoporosis group. Firstly, a deep learning-based segmentation model was constructed. Then, classification models were established and selected, and then, an optimal model to build bone density value prediction regression model was chosen.

RESULTS

The DSC value was 0.951 ± 0.030 in the testing dataset and 0.947 ± 0.060 in the external validation cohort. The multi-feature fusion model based on the lumbar 1 vertebra had the best performance in the diagnosis. The area under the curve (AUC) of diagnosing normal, osteopenia, and osteoporosis was 0.992, 0.973, and 0.989. The mean absolute errors (MAEs) of the bone density prediction regression model in the test set and external testing dataset are 8.20 mg/cm3 and 9.23 mg/cm3, respectively, and the root mean square errors (RMSEs) are 10.25 mg/cm3 and 11.91 mg/cm3, respectively. The R-squared values are 0.942 and 0.923, respectively. The Pearson correlation coefficients are 0.972 and 0.965.

CONCLUSION

The multi-feature fusion DCNN model based on only the lumbar 1 vertebrae and clinical variables can perform bone density three-classification diagnosis and estimate volumetric bone mineral density. If confirmed in independent populations, this automated opportunistic chest CT evaluation can help clinical screening of large-sample populations to identify subjects at high risk of osteoporotic fracture.

Multi-site phantomless bone mineral density from clinical quantitative computed tomography in males

Volumetric bone mineral density (vBMD) is commonly assessed using QCT. Although standard vBMD calculation methods require phantom rods that may not be available, internal-reference phantomless (IPL) and direct measurements of Hounsfield units (HU) can be used to calculate vBMD in their absence. Yet, neither approach has been systemically assessed across skeletal sites, and HU need further validation as a vBMD proxy. This study evaluated the accuracy of phantomless methods, including IPL and regression-based phantomless (RPL) calibration using HU to calculate vBMD, compared to phantom-based (PB) methods. vBMD from QCT scans of 100 male post-mortem human subjects (PMHS) was calculated using site-specific PB calibration at multiple skeletal sites throughout the body. A development sample of 50/100 PMHS was used to determine site-specific reference material density for IPL calibration and RPL equations. Reference densities and equations from the development sample were used to calculate IPL and RPL vBMD on the remaining 50/100 PMHS for method validation. PB and IPL/RPL vBMD were not significantly different (p > .05). Univariate regressions between PB and IPL/RPL vBMD were universally significant (p < 0.05), except for IPL Rad-30 (p = 0.078), with a percent difference across all sites of 6.97% ± 5.95% and 5.22% ± 4.59% between PB and IPL/RPL vBMD, respectively. As vBMD increased, there were weaker relationships and larger differences between PB vBMD and IPL/RPL vBMD. IPL and RPL vBMD had strong relationships with PB vBMD across sites (R2 = 97.99, R2 = 99.17%, respectively), but larger residual differences were found for IPL vBMD. As the accuracy of IPL/RPL vBMD varied between sites, phantomless methods should be site-specific to provide values more comparable to PB vBMD. Overall, this study suggests that RPL calibration may better represent PB vBMD compared to IPL calibration, increases the utility of opportunistic QCT, and provides insight into bone quality and fracture risk.

Opportunistic use of lumbar computed tomography and magnetic resonance imaging for osteoporosis screening

Compared with the healthy patients, patients with osteoporosis had a lower Hounsfield unit (HU) value and a higher vertebral bone quality (VBQ) score. Both the HU value and VBQ score can simply distinguish patients with osteoporosis (OP), with a cutoff value of HU value < 97.06 and VBQ score > 3.08.

INTRODUCTION

The purpose of this study is to determine whether the opportunistic use of computed tomography (CT) or magnetic resonance imaging (MRI) is effective for identifying spine surgical patients with OP.

METHODS

We retrospectively evaluated 109 lumbar spine surgery patients who received lumbar quantitative CT (QCT) and MRI. Using the area under the curve, the CT-based HU value and MRI-based VBQ score were calculated. Then, based on the QCT results, receiver operating characteristic (ROC) curves were constructed to determine the diagnostic performance of the HU value and VBQ score.

RESULTS

The HU value was significantly lower in the OP group, and the VBQ score was significantly higher in the OP group. Using the area under the curve, the diagnostic performance of the HU value and VBQ score for OP were 0.959 and 0.880, respectively. The diagnostic threshold values determined with optimal sensitivity and specificity were an HU value of 97.06 and a VBQ score of 3.08.

CONCLUSION

Opportunistic use of CT and MRI can simply distinguish patients with OP, which are expected to be potential alternatives to T-score for the osteoporosis screening.

Effect of fully automatic classification model from different tube voltage images on bone density screening: A self-controlled study

PURPOSE

To develop two bone status prediction models combining deep learning and radiomics based on standard-dose chest computed tomography (SDCT) and low-dose chest computed tomography (LDCT), and to evaluate the effect of tube voltage on reproducibility of radiomics features and predictive efficacy of these models.

METHODS

A total of 1508 patients were enrolled in this retrospective study. LDCT was conducted using 80 kVp, tube current ranging from 100 to 475 mA. On the other hand, SDCT was performed using 120 kVp, tube current ranging from 100 to 520 mA. We developed an automatic thoracic vertebral cancellous bone (TVCB) segmentation model. Subsequently, 1184 features were extracted and two classifiers were developed based on LDCT and SDCT images. Based on the diagnostic results of quantitative computed tomography examination, the first-level classifier was initially developed to distinguish normal or abnormal BMD (including osteoporosis and osteopenia), while the second-level classifier was employed to identify osteoporosis or osteopenia. The Dice coefficient was used to evaluate the performance of the automated segmentation model. The Concordance Correlation Coefficients (CCC) of radiomics features were calculated between LDCT and SDCT, and the performance of these models was evaluated.

RESULTS

Our automated segmentation model achieved a Dice coefficient of 0.98 ± 0.01 and 0.97 ± 0.02 in LDCT and SDCT, respectively. Alterations in tube voltage decreased the reproducibility of the extracted radiomic features, with 85.05 % of the radiomic features exhibiting low reproducibility (CCC < 0.75). The area under the curve (AUC) using LDCT-based and SDCT-based models was 0.97 ± 0.01 and 0.94 ± 0.02, respectively. Nonetheless, cross-validation with independent test sets of different tube voltage scans suggests that variations in tube voltage can impair the diagnostic efficacy of the model. Consequently, radiomics models are not universally applicable to images of varying tube voltages. In clinical settings, ensuring consistency between the tube voltage of the image used for model development and that of the acquired patient image is critical.

CONCLUSIONS

Automatic bone status prediction models, utilizing either LDCT or SDCT images, enable accurate assessment of bone status. Tube voltage impacts reproducibility of features and predictive efficacy of models. It is necessary to account for tube voltage variation during the image acquisition.

Opportunistic CT-Based Hounsfield Units Strongly Correlate with Biomechanical CT Measurements in the Thoracolumbar Spine

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

Hounsfield units (HUs) are known to correlate with clinical outcomes, but no study has evaluated how they correlate with biomechanical computed tomography (BCT) and dual-energy x-ray absorptiometry (DXA) measurements.

SUMMARY OF BACKGROUND

Low bone mineral density (BMD) represents a major risk factor for fracture and poor outcomes following spine surgery. DXA can provide regional BMD measurements but has limitations. Opportunistic HUs provide targeted BMD estimates; however, they are not formally accepted for diagnosing osteoporosis in current guidelines. More recently, BCT analysis has emerged as a new modality endorsed by the International Society for Clinical Densitometry for assessing bone strength.

METHODS

Consecutive cases from 2017 to 2022 at a single institution were reviewed for patients who underwent BCT in the thoracolumbar spine. BCT-measured vertebral strength, trabecular BMD, and the corresponding American College of Radiology Classification were recorded. DXA studies within three months of the BCT were reviewed. Pearson Correlation Coefficients were calculated, and receiver-operating characteristic curves were constructed to assess the predictive capacity of HUs. Threshold analysis was performed to identify optimal HU values for identifying osteoporosis and low BMD.

RESULTS

Correlation analysis of 114 cases revealed a strong relationship between HUs and BCT vertebral strength ( r =0.69; P <0.0001; R2 =0.47) and trabecular BMD ( r =0.76; P <0.0001; R2 =0.58). However, DXA poorly correlated with opportunistic HUs and BCT measurements. HUs accurately predicted osteoporosis and low BMD (Osteoporosis: C =0.95, 95% CI 0.89-1.00; Low BMD: C =0.87, 95% CI 0.79-0.96). Threshold analysis revealed that 106 and 122 HUs represent optimal thresholds for detecting osteoporosis and low BMD.

CONCLUSION

Opportunistic HUs strongly correlated with BCT-based measures, while neither correlated strongly with DXA-based BMD measures in the thoracolumbar spine. HUs are easy to perform at no additional cost and provide accurate BMD estimates at noninstrumented vertebral levels across all American College of Radiology-designated BMD categories.

Comparison of QCT and DEXA findings for lumbar vertebra in young adults and the elderly

BACKGROUND

The use of dual-energy X-ray absorptiometry (DEXA) and quantitative computed tomography (QCT) methods are important for the diagnosis and follow-up of osteoporosis, and are used especially in cases to determine the degree of osteoporosis and the risk of fracture, monitoring the effectiveness of the treatment applied.

PURPOSE

To compare the parameters measured using the DEXA method from the lumbar (L1-L4) vertebrae and the Hounsfield unit (HU) values measured with QCT at the same levels among young adults and the elderly.

MATERIAL AND METHODS

The study included 155 patients (age range = 26-93 years). A total of 57 (36.8%) patients (age range = 26-64 years) were defined as the first group, and 98 (63.2%) patients (aged ≥65 years) were defined as the second group. T-test and correlation analysis were performed to compare bone mineral density (BMD), T score, and HU values measured using DEXA and QCT.

RESULTS

A statistically significant difference was found between T score, lumbar total BMD, and HU values according to age and sex (P < 0.05). When the values measured from lumbar vertebrae were compared using both DEXA and CT, a high correlation was found between them.

CONCLUSION

In the study, it was observed that QCT attenuation measurements of the lumbar spine measured between different age groups provided reliable results in terms of BMD scanning, as in DEXA. It should be noted that QCT has a longer imaging time and higher radiation dose compared to DEXA, and unnecessary scans should be avoided.

For older women, the majority of hip fragility fractures and radiographic vertebral fragility fractures occur among the densitometrically osteoporotic population: a literature analysis

It has been frequently cited that 'the majority of fragility fractures (FF) occur at non-osteoporotic bone mineral density (BMD)'. For the reports with T-score measured around the time of a hip fracture, we conducted a systematic literature search in December 2022, and resulted in 10 studies with five for Caucasian women and five for East Asian women. Femoral neck (FN) T-score was reported in five Caucasian studies and three East Asian studies, three of five Caucasian studies had a mean T-score ≤-2.5, and one study had the majority of their patients measuring a mean T-score ≤-2.5. All three East Asian studies reported a mean FN T-score ≤-2.7. Total hip T-score was reported in two Caucasian studies and three East Asian studies, the two Caucasian studies both had a mean T-score ≤-2.5, and all three East Asian studies had a mean T-score ≤-2.6. A new literature search conducted in April 2024 results in additional three studies, with results being consistent with the data described above. A trend was noted that 'younger' patients suffer from hip fractures at a 'higher' T-score. For the highly cited articles where the notion the majority of FF occur at non-osteoporotic BMD was derived from, authors reported prospective epidemiological studies where BMD was not measured at the timepoint of hip fracture, instead, BMD was measured at the study baseline. These epidemiological studies suggest that >50% of hip fractures likely occur in women with an osteoporotic FN or hip T-score. However, a pattern was seen that older men suffer from hip fracture at a notably higher T-score than older women. For the cases of radiographic vertebral FF, despite varying criteria being used to classify these FFs, the majority of female patients had spine densitometric osteoporosis. Literature shows, compared with the cases of hip fracture, distal forearm fracture occurs at a 'younger' age and 'higher' BMD, suggesting distal forearm fracture is more likely associated with a 'higher' trauma energy level.

Correlation between vertebral bone mineral density and multi-level virtual non-calcium imaging parameters from dual-layer spectral detector computed tomography

Background

Virtual non-calcium (VNCa) imaging based on dual-energy computed tomography (CT) plays an increasingly important role in diagnosing spinal diseases. However, the utility of VNCa technology in the measurement of vertebral bone mineral density (BMD) is limited, especially the VNCa CT value at multiple calcium suppression levels and the slope of VNCa curve. This retrospective cross-sectional study aimed to explore the correlation between vertebral BMD and new VNCa parameters from dual-layer spectral detector CT.

Methods

The dual-layer spectral detector CT and quantitative CT (QCT) data of 4 hydroxyapatite (HAP) inserts and 667 vertebrae of 234 patients (132 male and 102 female) who visited a university teaching hospital between April and May 2023 were retrospectively analyzed. The BMD values of 3 vertebrae (T12, L1, and L2) and inserts were measured using QCT, defined as QCT-BMD. The VNCa CT values and the slope λ of the VNCa attenuation curve of vertebrae and inserts were recorded. The correlations between VNCa parameters (VNCa CT value, slope λ) and QCT-BMD were analyzed.

Results

For the vertebrae, the correlation coefficient ranged from -0.904 to 0.712 (all P<0.05). As the calcium suppression index (CaSI) increased, the correlation degree exhibited a decrease first and then increased, with the best correlation (r=-0.904, P<0.001) observed at the index of 25%. In contrast, the correlation coefficient for the inserts remained relatively stable (r=-0.899 to -1, all P<0.05). For the vertebrae, the values of 3 slopes λ (λ1, λ2, and λ3) derived from the VNCa attenuation curve were 6.50±1.99, 3.75±1.15, and 2.04±0.62, respectively. Regarding the inserts, the λ1, λ2, and λ3 values were 11.56 [interquartile range (IQR): 2.40-22.62], 6.68 (IQR: 1.39-13.49), and 3.63 (IQR: 0.75-7.8), respectively. For the vertebrae, all 3 correlation coefficients between 3 slopes λ and QCT-BMD were 0.956 (all P<0.05). For the inserts, the 3 correlation coefficients were 0.996, 0.998, and 1 (all P<0.05), respectively.

Conclusions

A promising correlation was detected between VNCa CT parameters and QCT-BMD in vertebrae, warranting further investigation to explore the possibility of VNCa imaging to assess BMD.

CT image-based biomarkers for opportunistic screening of osteoporotic fractures: a systematic review and meta-analysis

The use of opportunistic computed tomography (CT) image-based biomarkers may be a low-cost strategy for screening older individuals at high risk for osteoporotic fractures and populations that are not sufficiently targeted. This review aimed to assess the discriminative ability of image-based biomarkers derived from existing clinical routine CT scans for hip, vertebral, and major osteoporotic fracture prediction. A systematic search in PubMed MEDLINE, Embase, Cochrane, and Web of Science was conducted from the earliest indexing date until July 2023. The evaluation of study quality was carried out using a modified Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2) checklist. The primary outcome of interest was the area under the curve (AUC) and its corresponding 95% confidence intervals (CIs) obtained for four main categories of biomarkers: areal bone mineral density (BMD), image attenuation, volumetric BMD, and finite element (FE)-derived biomarkers. The meta-analyses were performed using random effects models. Sixty-one studies were included in this review, among which 35 were synthesized in a meta-analysis and the remaining articles were qualitatively synthesized. In comparison to the pooled AUC of areal BMD (0.73 [95% CI 0.71-0.75]), the pooled AUC values for predicting osteoporotic fractures for FE-derived parameters (0.77 [95% CI 0.72-0.81]; p < 0.01) and volumetric BMD (0.76 [95% CI 0.71-0.81]; p < 0.01) were significantly higher, but there was no significant difference with the pooled AUC for image attenuation (0.73 [95% CI 0.66-0.79]; p = 0.93). Compared to areal BMD, volumetric BMD and FE-derived parameters may provide a significant improvement in the discrimination of osteoporotic fractures using opportunistic CT assessments.

Comparisons of Hounsfield units and volumetric bone density in discriminating vertebral fractures on lumbar CT scans

OBJECTIVES

To compare the performance of areal Hounsfield units (aHUs), volumetric Hounsfield units (vHUs), and volumetric bone mineral density (vBMD) by quantitative CT (QCT) in discriminating vertebral fractures (VFs) risk.

METHODS

We retrospectively included CT scans of the lumbar spine 101 VFs cases (60 women, mean age: 64 ± 4 years; 41 men, mean age: 73 ± 10 years) and sex- and age-matched 101 control subjects (60 women, mean age: 64 ± 4 years; 41 men, mean age: 72 ± 7 years). In order to assess the discriminatory capability of aHU, vHU, and vBMD measurements at the L1 and L2 levels in identifying VFs, we conducted binary logistic regression and receiver operating characteristic (ROC) curve analyses in men and women. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

RESULTS

In both men and women with and without VFs, aHU, vHU, and vBMD were highly correlated with each other (r2 from 0.832 to 0.957, all P < .001). There was a statistically significant difference in aHU, vHU, and vBMD between subjects with and without VFs (P < .001). When age, gender, and BMI were taken into account as covariances and adjusted simultaneously, odds ratios (ORs) for aHU, vHU, and vBMD values, which represent the risk of VFs, were significant (P < .001). Compared with aHU and vHU, vBMD was more strongly associated with VF risk (vBMD: OR, 6.29; 95% CI, 3.83-10.35 vs vHU: OR, 3.64; 95% CI, 2.43-5.46 vs aHU: OR, 2.56; 95% CI, 1.79-3.67). In both men and women, further, vBMD had higher values for AUC, sensitivity, specificity, PPV, and NPV compared to vHU, with vHU in turn surpassing aHU. The area under the receiver operating characteristic curve (AUC) for discriminating VFs using the average aHU, vHU, and vBMD of 2 vertebrae was 0.72, 0.77, and 0.87 in men and 0.76, 0.79, and 0.86 in women. In both men and women, there exist statistically significant differences in the AUC when employing the 3 measurements-namely, aHU, vHU, and vBMD-to discriminate fractures (P < .05).

CONCLUSIONS

The QCT-measured vBMD is more associated with acute VFs than vHU and aHU values of the lumbar spine. Although the use of vHU and aHU values for the diagnosis of osteoporosis and discriminating fracture risk is limited to scanner- and imaging protocol-specific, they have great potential for opportunistic osteoporosis screening, particularly vHU.

ADVANCES IN KNOWLEDGE

The novelty of this study presents a comparison of the VF discriminative capabilities among aHU, vHU, and vBMD. The vHU values introduced in this study demonstrate a greater capacity to discriminate fractures compared to aHU, presenting an improved clinical choice. Although its discriminatory capability is slightly lower than that of vBMD, it is more convenient to measure and does not require specialized software.

Longitudinal MR-based proton-density fat fraction (PDFF) and T2* for the assessment of associations between bone marrow changes and myelotoxic chemotherapy

OBJECTIVES

MR imaging-based proton density fat fraction (PDFF) and T2* imaging has shown to be useful for the evaluation of degenerative changes in the spine. Therefore, the aim of this study was to investigate the influence of myelotoxic chemotherapy on the PDFF and T2* of the thoracolumbar spine in comparison to changes in bone mineral density (BMD).

METHODS

In this study, 19 patients were included who had received myelotoxic chemotherapy (MC) and had received a MR imaging scan of the thoracolumbar vertebrates before and after the MC. Every patient was matched for age, sex, and time between the MRI scans to two controls without MC. All patients underwent 3-T MR imaging including the thoracolumbar spine comprising chemical shift encoding-based water-fat imaging to extract PDFF and T2* maps. Moreover, trabecular BMD values were determined before and after chemotherapy. Longitudinal changes in PDFF and T2* were evaluated and compared to changes in BMD.

RESULTS

Absolute mean differences of PDFF values between scans before and after MC were at 8.7% (p = 0.01) and at -0.5% (p = 0.57) in the control group, resulting in significantly higher changes in PDFF in patients with MC (p = 0.008). BMD and T2* values neither showed significant changes in patients with nor in those without myelotoxic chemotherapy (p = 0.15 and p = 0.47). There was an inverse, yet non-significant correlation between changes in PDFF and BMD found in patients with myelotoxic chemotherapy (r = -0.41, p = 0.12).

CONCLUSION

Therefore, PDFF could be a useful non-invasive biomarker in order to detect changes in the bone marrow in patients receiving myelotoxic therapy.

CLINICAL RELEVANCE STATEMENT

Using PDFF as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment may help enable more targeted countermeasures at commencing states of bone marrow degradation and reduce risks of possible fragility fractures.

KEY POINTS

Quantifying changes in bone marrow fat fraction, as well as T2* caused by myelotoxic pharmaceuticals using proton density fat fraction, is feasible. Proton density fat fraction could potentially be established as a non-invasive biomarker for early bone marrow changes in oncologic patients undergoing myelotoxic treatment.

Effectiveness of opportunistic osteoporosis screening on chest CT using the DCNN model

OBJECTIVE

To develop and evaluate a deep learning model based on chest CT that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images, and explore the feasibility and effectiveness of the model based on the lumbar 1 vertebral body alone.

MATERIALS AND METHODS

The chest CT images of 1048 health check subjects from January 2021 to June were retrospectively collected as the internal dataset (the segmentation model: 548 for training, 100 for tuning and 400 for test. The classification model: 530 for training, 100 for validation and 418 for test set). The subjects were divided into three categories according to the quantitative CT measurements, namely, normal, osteopenia and osteoporosis. First, a deep learning-based segmentation model was constructed, and the dice similarity coefficient(DSC) was used to compare the consistency between the model and manual labelling. Then, two classification models were established, namely, (i) model 1 (fusion feature construction of lumbar vertebral bodies 1 and 2) and (ii) model 2 (feature construction of lumbar 1 alone). Receiver operating characteristic curves were used to evaluate the diagnostic efficacy of the models, and the Delong test was used to compare the areas under the curve.

RESULTS

When the number of images in the training set was 300, the DSC value was 0.951 ± 0.030 in the test set. The results showed that the model 1 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.990, 0.952 and 0.980; the model 2 diagnosing normal, osteopenia and osteoporosis achieved an AUC of 0.983, 0.940 and 0.978. The Delong test showed that there was no significant difference in area under the curve (AUC) values between the osteopenia group and osteoporosis group (P = 0.210, 0.546), while the AUC value of normal model 2 was higher than that of model 1 (0.990 vs. 0.983, P = 0.033).

CONCLUSION

This study proposed a chest CT deep learning model that achieves favorable performance on opportunistic osteoporosis screening using the lumbar 1 + lumbar 2 vertebral bodies fusion feature images. We further constructed the comparable model based on the lumbar 1 vertebra alone which can shorten the scan length, reduce the radiation dose received by patients, and reduce the training cost of technologists.

Incident fractures of the distal radius: Dual-energy CT-derived metrics for opportunistic risk stratification

BACKGROUND

Dual-energy CT (DECT)-derived bone mineral density (BMD) of the distal radius and other CT-derived metrics related to bone health have been suggested for opportunistic osteoporosis screening and risk evaluation for sustaining distal radius fractures (DRFs).

METHODS

The distal radius of patients who underwent DECT between 01/2016 and 08/2021 was retrospectively analyzed. Cortical Hounsfield Unit (HU), trabecular HU, cortical thickness, and DECT-based BMD were acquired from a non-fractured, metaphyseal area in all examinations. Receiver-operating characteristic (ROC) analysis was conducted to determine the area under the curve (AUC) values for predicting DRFs based on DECT-derived BMD, HU values, and cortical thickness. Logistic regression models were then employed to assess the associations of these parameters with the occurrence of DRFs.

RESULTS

In this study, 263 patients (median age: 52 years; interquartile range: 36-64; 132 women; 192 fractures) were included. ROC curve analysis revealed a higher area under the curve (AUC) value for DECT-derived BMD compared to cortical HU, trabecular HU, and cortical thickness (0.91 vs. 0.61, 0.64, and 0.69, respectively; p <.001). Logistic regression models confirmed the association between lower DECT-derived BMD and the occurrence of DRFs (Odds Ratio, 0.83; p <.001); however, no influence was observed for cortical HU, trabecular HU, or cortical thickness.

CONCLUSIONS

DECT can be used to assess the BMD of the distal radius without dedicated equipment such as calibration phantoms to increase the detection rates of osteoporosis and stratify the individual risk to sustain DRFs. In contrast, assessing HU-based values and cortical thickness does not provide clinical benefit.

Opportunistic screening for osteoporosis using enhanced images based on dual-energy computed tomography material decomposition: a comparison with quantitative computed tomography

Background

Many patients with malignant tumors require chemotherapy and radiation therapy, which can result in a decline in physical function and potentially influence bone mineral density (BMD). Furthermore, these treatments necessitate enhanced computed tomography (CT) scans for determining disease staging or treatment outcomes, and opportunistic screening with available imaging data is beneficial for patients at high risk for osteoporosis if existing imaging data can be used. The study aimed to investigate the feasibility of opportunistic screening for osteoporosis using enhanced CT based on a dual-energy CT (DECT) material decomposition technique.

Methods

We prospectively enrolled 346 consecutive patients who underwent abdominal unenhanced and triphasic contrast-enhanced CT (arterial, portal venous, and delayed phases) between June 2021 and June 2022. The BMD, and the density of hydroxyapatite (HAP) on HAP-iodine images and calcium (Ca) on Ca-iodine images were measured on the L1-L3 vertebral bodies. The iodine intake was recorded. Pearson analysis was conducted to assess the correlation between iodine intake and the density values in three phases and the correlation between BMD and the densities of HAP and Ca. Furthermore, linear regression was employed for quantitative evaluation. Bland-Altman analysis was used to evaluate the agreement between calculated BMD derived from DECT (BMD-DECT) and reference BMD derived from quantitative CT (BMD-QCT). Receiver operating characteristic (ROC) analysis was applied to assess the diagnostic efficacy.

Results

The HAP and Ca density of the L1-L3 vertebral bodies did not differ significantly among the three phases of contrast-enhanced CT (F=0.001-0.049; P>0.05). Significant positive correlations were found between HAP, Ca densities, and BMD (HAP-BMD: r=0.9472, R2=0.8973; Ca-BMD: r=0.9470, R2=0.8968; all P<0.001). Bland-Altman plots showed high agreement between BMD-DECT and BMD-QCT. The area under the curve (AUC) using HAP and Ca measurements was 0.963 [95% confidence interval (CI): 0.937-0.980] and 0.964 (95% CI: 0.939-0.981), respectively, for diagnosing osteoporosis and was 0.951 (95% CI: 0.917-0.973) and 0.950 (95% CI: 0.916-0.973), respectively, for diagnosing osteopenia.

Conclusions

The HAP and Ca density measurements generated through the material decomposition technique in DECT have good diagnostic performances in assessing BMD, which offers a new perspective for opportunistic screening of osteoporosis on contrast-enhanced CT.

Quantitative computed tomography has higher sensitivity detecting critical bone mineral density compared to dual-energy X-ray absorptiometry in postmenopausal women and elderly men with osteoporotic fractures: a real-life study

INTRODUCTION

Dual-energy X-ray absorptiometry (DXA) is considered the gold standard for the diagnosis of osteoporosis and assessment of fracture risk despite proven limitations. Quantitative computed tomography (QCT) is regarded as a sensitive method for diagnosis and follow-up. Pathologic fractures are classified as the main clinical manifestation of osteoporosis. The objective of the study was to compare DXA and QCT to determine their sensitivity and discriminatory power.

MATERIALS AND METHODS

Patients aged 50 years and older were included who had DXA of the lumbar spine and femur and additional QCT of the lumbar spine within 365 days. Fractures and bone mineral density (BMD) were retrospectively examined. BMD measurements were analyzed for the detection of osteoporotic fractures. Sensitivity and receiver operating characteristic curve were used for calculations. As an indication for a second radiological examination was given, the results were compared with control groups receiving exclusively DXA or QCT for diagnosis or follow-up.

RESULTS

Overall, BMD measurements of 404 subjects were analyzed. DXA detected 15 (13.2%) patients having pathologic fractures (n = 114) with normal bone density, 66 (57.9%) with osteopenia, and 33 (28.9%) with osteoporosis. QCT categorized no patients having pathologic fractures with healthy bone density, 14 (12.3%) with osteopenia, and 100 (87.7%) with osteoporosis. T-score DXA, trabecular BMD QCT, and cortical BMD QCT correlated weakly. Trabecular BMD QCT and cortical BMD QCT classified osteoporosis with decreased bone mineral density (AUC 0.680; 95% CI 0.618-0.743 and AUC 0.617; 95% CI 0.553-0.682, respectively). T-score DXA could not predict prevalent pathologic fractures. In control groups, each consisting of 50 patients, DXA and QCT were significant classifiers to predict prevalent pathologic fractures.

CONCLUSION

Our results support that volumetric measurements by QCT in preselected subjects represent a more sensitive method for the diagnosis of osteoporosis and prediction of fractures compared to DXA.

A study on whether deep learning models based on CT images for bone density classification and prediction can be used for opportunistic osteoporosis screening

This study utilized deep learning to classify osteoporosis and predict bone density using opportunistic CT scans and independently tested the models on data from different hospitals and equipment. Results showed high accuracy and strong correlation with QCT results, showing promise for expanding osteoporosis screening and reducing unnecessary radiation and costs.

PURPOSE

To explore the feasibility of using deep learning to establish a model for osteoporosis classification and bone density value prediction based on opportunistic CT scans and to verify its generalization and diagnostic ability using an independent test set.

METHODS

A total of 1219 cases of opportunistic CT scans were included in this study, with QCT results as the reference standard. The training set: test set: independent test set ratio was 703: 176: 340, and the independent test set data of 340 cases were from 3 different hospitals and 4 different CT scanners. The VB-Net structure automatic segmentation model was used to segment the trabecular bone, and DenseNet was used to establish a three-classification model and bone density value prediction regression model. The performance parameters of the models were calculated and evaluated.

RESULTS

The ROC curves showed that the mean AUCs of the three-category classification model for categorizing cases into "normal," "osteopenia," and "osteoporosis" for the training set, test set, and independent test set were 0.999, 0.970, and 0.933, respectively. The F1 score, accuracy, precision, recall, precision, and specificity of the test set were 0.903, 0.909, 0.899, 0.908, and 0.956, respectively, and those of the independent test set were 0.798, 0.815, 0.792, 0.81, and 0.899, respectively. The MAEs of the bone density prediction regression model in the training set, test set, and independent test set were 3.15, 6.303, and 10.257, respectively, and the RMSEs were 4.127, 8.561, and 13.507, respectively. The R-squared values were 0.991, 0.962, and 0.878, respectively. The Pearson correlation coefficients were 0.996, 0.981, and 0.94, respectively, and the p values were all < 0.001. The predicted values and bone density values were highly positively correlated, and there was a significant linear relationship.

CONCLUSION

Using deep learning neural networks to process opportunistic CT scan images of the body can accurately predict bone density values and perform bone density three-classification diagnosis, which can reduce the radiation risk, economic consumption, and time consumption brought by specialized bone density measurement, expand the scope of osteoporosis screening, and have broad application prospects.

Based on HbA1c Analysis: Bone Mineral Density and Osteoporosis Risk in Postmenopausal Female with T2DM

INTRODUCTION

This study aims to investigate association between glycosylated hemoglobin (HbA1c) with bone mineral density (BMD) and osteoporosis-risk in postmenopausal female with type 2 diabetes mellitus (T2DM).

METHODOLOGY

HbA1c values, BMD of L3 vertebra and basic clinical data of 152 postmenopausal females with T2DM and 326 postmenopausal females without T2DM were retrospectively analyzed. The propensity score matching was used to match the T2DM and the non-T2DM group at a ratio of 1:1. Restricted cubic spline (RCS) analysis and piecewise linear regression were used to evaluate the relationship between HbA1c and BMD. Univariable and multivariable logistic regression were utilized to evaluate the effect of HbA1c on the risk of osteoporosis in matched diabetes population.

RESULTS

After matching, the BMD (66.60 (46.58, 93.23) vs. 63.50 (36.70, 83.33), P < 0.05), HbA1c value (7.50 (6.72, 8.80) vs 5.30 (5.14, 5.50), P  <  0.05) in the T2DM group were significantly higher than that of non-T2DM group. We found a nonlinear relation between HbA1c value and BMD, which showing a U-shaped curve with the cutoff value around 7.5 % (Poverall < 0.001, Pnonliearity < 0.05). The prevalence of osteoporosis in T2DM group was similar to that in controls (64.9 % vs 73.6 %, P = 0.102). Age-adjusted HbA1c value was not risk factor of osteoporosis in postmenopausal females with T2DM.

CONCLUSION

In postmenopausal females with T2DM, high BMD and similar risk of osteoporosis were confirmed; HbA1c was a contributing factor to BMD when values exceed 7.5 %. However, HbA1c does not seem to be associated with osteoporosis risk.

Quantitative CT Evaluation of Bone Mineral Density in the Thoracic Spine on 18F-Fluorocholine PET/CT Imaging in Patients With Primary Hyperparathyroidism

INTRODUCTION

Measurement of bone mineral density (BMD) with quantitative CT (QCT) carries several advantages over other densitometric techniques, including superior assessment of the spine. As most QCT studies evaluated the lumbar spine, measurements of the thoracic spine are limited. We performed QCT analysis of the thoracic spine in a cohort of patients with primary hyperparathyroidism.

MATERIALS AND METHODS

This study was a retrospective QCT analysis of the thoracic spine on 18F-fluorocholine PET/CT scans in patients with primary hyperparathyroidism patients between March 2018 and December 2022. Correlations between QCT-derived BMD or Hounsfield units (HU) and demographic data, laboratory parameters, results from histopathological examination after parathyroidectomy and results of DXA imaging were analyzed, when available.

RESULTS

In 189 patients, mean QCT-derived BMD at the thoracic spine was 85.6 mg/cm3. Results from recent DXA were available in 122 patients. Mean thoracic QCT-derived BMD and HU were significantly correlated with DXA-derived BMD in lumbar spine, total hip and femoral neck and with the lowest T-score at DXA imaging. Only weak correlations were found with BMI or 18F-fluorocholine uptake, while no significant correlations were found with adenoma weight, PTH or calcium levels.

CONCLUSION

Our study confirms correlation between QCT-derived BMD in the thoracic spine with age and DXA-derived BMD measurements within a population of patients with primary hyperparathyroidism. Establishment of reference BMD values for individual thoracic vertebrae, may allow direct osteoporosis classification on thoracic CT imaging.

Vertebral Fracture Risk Thresholds from Phantom-Less Quantitative Computed Tomography-Based Finite Element Modeling Correlate to Phantom-Based Outcomes

INTRODUCTION

Osteoporosis indicates weakened bones and heightened fracture susceptibility due to diminished bone quality. Dual-energy x-ray absorptiometry is unable to assess bone strength. Volumetric bone mineral density (vBMD) from quantitative computed tomography (QCT) has been used to establish guidelines as equivalent measurements for osteoporosis. QCT-based finite element analysis (FEA) has been implemented using calibration phantoms to establish bone strength thresholds based on the established vBMD. The primary aim was to validate vertebral failure load thresholds using a phantom-less approach with previously established thresholds, advancing a phantom-free approach for fracture risk prediction.

METHODOLOGY

A controlled cohort of 108 subjects (68 females) was used to validate sex-specific vertebral fracture load thresholds for normal, osteopenic, and osteoporotic subjects, obtained using a QCT/FEA-based phantom-less calibration approach and two material equations.

RESULTS

There were strong prediction correlations between the phantom-less and phantom-based methods (R2: 0.95 and 0.97 for males, and R2: 0.96 and 0.98 for females) based on the two equations. Bland Altman plots and paired t-tests showed no significant differences between methods. Predictions for bone strengths and thresholds using the phantom-less method matched those obtained using the phantom calibration and those previously established, with ≤4500 N (fragile) and ≥6000 N (normal) bone strength in females, and ≤6500 N (fragile) and ≥8500 N (normal) bone strength in males.

CONCLUSION

Phantom-less QCT-based FEA can allow for prospective and retrospective studies evaluating incidental vertebral fracture risk along the spine and their association with spine curvature and/or fracture etiology. The findings of this study further supported the application of phantom-less QCT-based FEA modeling to predict vertebral strength, aiding in identifying individuals prone to fractures. This reinforces the rationale for adopting this method as a comprehensive approach in predicting and managing fracture risk.

Different cervical vertebral bone quality scores for bone mineral density assessment for the patients with cervical degenerative disease undergoing ACCF/ACDF: computed tomography and magnetic resonance imaging-based study

BACKGROUND

Bone mineral density (BMD) is important for the outcome of cervical spine surgery. As the gold standard of assessing BMD, dual-energy X-ray absorptiometry scans are often not ordered or go unreviewed in patients' charts. As the supplement, MRI-based vertebral bone quality (VBQ) was found to accurately predict osteopenia/osteoporosis and postoperative complications in lumbar spine. However, discussion of the efficiency of VBQ in cervical spine is lacking. And measurement methods of VBQ in cervical spine are diverse and not universally acknowledged like lumbar spine. We aimed to compare the predictive performance of three kinds of different Cervical-VBQ (C-VBQ) scores for bone mineral density assessment in patients undergoing cervical spine surgery. HU value of cervical spine was set as a reference.

METHODS

Adult patients receiving cervical spine surgery for degenerative diseases were retrospectively included between Jan 2015 and Dec 2022 in our hospital. The VBQ scores and HU value were measured from preoperative MRI and CT. The correlation between HU value/C-VBQs (named C-VBQ1/2/3 according to different calculating methods) and DEXA T-score was analyzed using univariate linear correlation and Pearson's correlation. We evaluated the predictive performance of those two parameters and achieved the most appropriate cutoff value by comparing the receiver operating characteristic (ROC) curves.

RESULTS

106 patients (34 patients with T ≥ - 1.0 vs 72 patients with T < - 1.0) were included (mean age: 51.95 ± 10.94, 48 men). According to Pearson correlation analysis, C-VBQ1/2/3 and HU value were all significantly correlated to DEXA T-score (Correlation Coefficient (r): C-VBQ1: - 0.393, C-VBQ2: - 0.368, C-VBQ3: - 0.395, HU value: 0.417, p < 0.001). The area under the ROC curve (AUC) was calculated (C-VBQ1: 0.717, C-VBQ2: 0.717, C-VBQ3: 0.727, HU value: 0.746). The AUC of the combination of C-VBQ3 and HU value was 0.786. At last, the most appropriate cutoff value was determined (C-VBQ1: 3.175, C-VBQ2: 3.005, C-VBQ3: 2.99, HU value: 299.85 HU).

CONCLUSIONS

Different MRI-based C-VBQ scores could all be potential and alternative tools for opportunistically screening patients with osteopenia and osteoporosis before cervical spine surgery. Among them, C-VBQ calculated in ASIC2-C7/SIT1-CSF performed better. We advised patients with C-VBQ higher than cutoff value to accept further BMD examination.

Evaluation of Bone Mineral Density in Lung Transplant Recipients by Chest Computed Tomography

INTRODUCTION

Lung transplantation (LT) recipients are at risk of bone mineral density (BMD) loss. Pre- and post-LT BMD loss has been reported in some cross-sectional studies; however, there are limited studies regarding the serial BMD change in LT recipients. The aim of this study was to investigate the serial BMD changes and the clinical characteristics associated with BMD decline.

METHODS

This was a single-center, retrospective observational study. BMD was serially measured in thoracic vertebral bodies (Th4, 7, 10) using computed tomography (CT) before and 3 and 12 months after LT. The frequency of osteoporosis and factors associated with pre-LT osteoporosis and post-LT BMD loss were evaluated. The frequency of post-LT compression fracture and its associated factors were also analyzed.

RESULTS

This study included 128 adult LT recipients. LT recipients had decreased BMD (151.8 ± 42.2 mg/mL) before LT compared with age-, sex-, and smoking index-matched controls (176.2 ± 35.7 mg/mL). The diagnosis of COPD was associated with pre-LT osteoporosis. LT recipients experience further BMD decline after transplantation, and the percentage of recipients classified as exhibiting osteoporosis increased from 20% at baseline to 43% at 12 months. Recipients who had been taking no or small doses of glucocorticoids before LT had rapid BMD loss after LT. Early bisphosphonate use (within 3 months) after LT attenuated BMD loss and decreased new-onset compression fracture.

CONCLUSION

LT recipients are at high risk for BMD loss and compression fracture after LT. Early bisphosphonate use may decrease BMD loss and compression fracture.

Screw Insertional Torque Measurement in Spine Surgery: Correlation With Bone Mineral Density and Hounsfield Unit

OBJECTIVE

Achieving successful fusion during spine surgery is dependent on rigid pedicle screw fixation. To assess fixation strength, the insertional torque can be measured during intraoperative screw fixation. This study aimed to explore the technical feasibility of measuring the insertional torque of a pedicle screw, while investigating its relationship with bone density.

METHODS

Thoraco-lumbar screw fixation fusion surgery was performed on 53 patients (mean age, 65.5 ± 9.8 years). The insertional torque of 284 screws was measured at the point passing through the pedicle using a calibrated torque wrench, with a specially designed connector to the spine screw system. The Hounsfield units (HU) value was determined by assessing the trabecular portion of the index vertebral body on sagittal computed tomography images. We analyzed the relationship between the measured insertional torque and the following bone strength parameters: bone mineral density (BMD) and HU of the vertebral body.

RESULTS

The mean insertion torque was 105.55 ± 58.08 N∙cm and T-score value (BMD) was -1.14 ± 1.49. Mean HU value was 136.37 ± 57.59. Screw insertion torque was positively correlated with BMD and HU in whole patients. However, in cases of osteopenia, all variables showed very weak correlations with insertional torque. In patients with osteoporosis, there was no statistically significant correlation between BMD and torque strength; HU showed a significant correlation.

CONCLUSION

The insertional torque of screw fixation significantly correlated with bone density (BMD and HU). HU measurements showed greater clinical significance than did BMD values in patients with osteoporosis.

Automated vertebral bone mineral density measurement with phantomless internal calibration in chest LDCT scans using deep learning

OBJECTIVE

To develop and evaluate a fully automated method based on deep learning and phantomless internal calibration for bone mineral density (BMD) measurement and opportunistic low BMD (osteopenia and osteoporosis) screening using chest low-dose CT (LDCT) scans.

METHODS

A total of 1175 individuals were enrolled in this study, who underwent both chest LDCT and BMD examinations with quantitative computed tomography (QCT), by two different CT scanners (Siemens and GE). Two convolutional neural network (CNN) models were employed for vertebral body segmentation and labeling, respectively. A histogram technique was applied for vertebral BMD calculation using paraspinal muscle and surrounding fat as references. 195 cases (by Siemens scanner) as fitting cohort were used to build the calibration function. 698 cases as validation cohort I (VCI, by Siemens scanner) and 282 cases as validation cohort II (VCII, by GE scanner) were performed to evaluate the performance of the proposed method, with QCT as the standard for analysis.

RESULTS

The average BMDs from the proposed method were strongly correlated with QCT (in VCI: r = 0.896, in VCII: r = 0.956, p < 0.001). Bland-Altman analysis showed a small mean difference of 1.1 mg/cm3, and large interindividual differences as seen by wide 95% limits of agreement (-29.9 to +32.0 mg/cm3) in VCI. The proposed method measured BMDs were higher than QCT measured BMDs in VCII (mean difference = 15.3 mg/cm3, p < 0.001). Osteoporosis and low BMD were diagnosed by proposed method with AUCs of 0.876 and 0.903 in VCI, 0.731 and 0.794 in VCII, respectively. The AUCs of the proposed method were increased to over 0.920 in both VCI and VCII after adjusting the cut-off.

CONCLUSION

Without manual selection of the region of interest of body tissues, the proposed method based on deep learning and phantomless internal calibration has the potential for preliminary screening of patients with low BMD using chest LDCT scans. However, the agreement between the proposed method and QCT is insufficient to allow them to be used interchangeably in BMD measurement.

ADVANCES IN KNOWLEDGE

This study proposed an automated vertebral BMD measurement method based on deep learning and phantomless internal calibration with paraspinal muscle and fat as reference.