Osteoporosis imaging: state of the art and advanced imaging

Quantitative calcium-based assessment of osteoporosis in dual-layer spectral CT

OBJECTIVES

To evaluate a novel calcium-only imaging technique (VCa) with subtracted bone marrow in osteoporosis in dual-layer CT (DLCT) compared to conventional CT images (CI) and dual-energy X-ray absorptiometry (DXA).

MATERIAL AND METHODS

Images of a multi-energy CT phantom with calcium inserts, quantitative CT calibration phantom, and of 55 patients (mean age: 64.6 ± 11.5 years) were acquired on a DLCT to evaluate bone mineral density (BMD). CI, calcium-suppressed images, and VCa were calculated. For investigating the association of VCa and CI with DXA a subsample of 30 patients (<90 days between DXA and CT) was used. Multiple regression analysis was performed to identify further factors improving the prediction of DXA BMD.

RESULTS

The calcium concentrations of the CT phantom inserts were significantly associated with CT numbers from VCa (R2 = 0.94) and from CI (R2 = 0.89-0.92). VCa showed significantly higher CT numbers than CI in the phantom (p ≤ 0.001) and clinical setting (p < 0.001). CT numbers from VCa were significantly associated with CI (R2 = 0.95, p < 0.001) and with DXA (R2 = 0.31, p = 0.007), whereas no significant association between DXA and CI was found. Prediction of DXA BMD based on CT numbers derived from VCa yielded R2 = 0.76 in multiple regression analysis. ROC for the differentiation of normal from pathologic BMD in VCa yielded an AUC of 0.7, and a cut-off value of 126HU (sensitivity: 0.90; specificity: 0.47).

CONCLUSION

VCa images showed better agreement with DXA and known calcium concentrations than CI, and could be used to estimate BMD. A VCa cut-off of 126HU could be used to identify abnormal bone mineral density.

Developing Cut-off Values for Low and Very Low Bone Mineral Density at the Thoracic Spine Using Quantitative Computed Tomography

Osteoporosis is under-diagnosed while detectable by measuring bone mineral density (BMD) using quantitative computer tomography (QCT). Opportunistic screening for low BMD has previously been suggested using lumbar QCT. However, thoracic QCT also possesses this potential to develop upper and lower cut-off values for low thoracic BMD, corresponding to the current cut-offs for lumbar BMD. In participants referred with chest pain, lumbar and thoracic BMD were measured using non-contrast lumbar- and cardiac CT scans. Lumbar BMD cut-off values for very low (< 80 mg/cm3), low (80-120 mg/cm3), and normal BMD (> 120 mg/cm3) were used to assess the corresponding thoracic values. A linear regression enabled identification of new diagnostic thoracic BMD cut-off values. The 177 participants (mean age 61 [range 31-74] years, 51% women) had a lumbar BMD of 121.6 mg/cm3 (95% CI 115.9-127.3) and a thoracic BMD of 137.0 mg/cm3 (95% CI: 131.5-142.5), p < 0.001. Categorization of lumbar BMD revealed 14%, 35%, and 45% in each BMD category. When applied for the thoracic BMD measurements, 25% of participants were reclassified into a lower group. Linear regression predicted a relationship of Thoracic BMD = 0.85 * Lumbar BMD + 33.5, yielding adjusted thoracic cut-off values of < 102 and > 136 mg/cm3. Significant differences in BMD between lumbar and thoracic regions were found, but a linear relationship enabled the development of thoracic upper and lower cut-off values for low BMD in the thoracic spine. As Thoracic CT scans are frequent, these findings will strengthen the utilization of CT images for opportunistic detection of osteoporosis.

The influence of contrast media on calcium-based imaging of the spine in dual-layer CT

This study aimed to evaluate the impact of contrast media application on CT attenuation of the bone using a novel calcium-only imaging technique (VCa) from dual-layer spectral detector CT (DLCT), which enables CT-based bone mineral density measurement unimpeded by soft tissue components. For this, true non-contrast (TNC) and venous phase images (VP) of n = 97 patients were acquired. CT attenuation of the first lumbar vertebra (L1) was measured in TNC-VCa, VP-VCa, and in virtual non-contrast images (VNC). CT attenuation was significantly higher in VP-VCa than in TNC-VCa (p < 0.001), although regression analyses revealed a strong linear association between these measures (R2 = 0.84). A statistical model for the prediction of TNC-VCa CT attenuation was established (TNC-VCa[HU] = - 6.81 + 0.87 × VP-VCa[HU]-0.55 × body weight[kg]) and yielded good agreement between observed and predicted values. Furthermore, a L1 CT attenuation threshold of 293 HU in VP-VCa showed a sensitivity of 90% and a specificity of 96% for detecting osteoporosis. The application of contrast media leads to an overestimation of L1 CT attenuation in VCa. However, CT attenuation values from VP-VCa can be used within CT-based opportunistic osteoporosis screening eighter by applying a separate threshold of 293 HU or by converting measured data to TNC-VCa CT attenuation with the given regression equation.

Reproducibility of CT-based opportunistic vertebral volumetric bone mineral density measurements from an automated segmentation framework

BACKGROUND

To investigate the reproducibility of automated volumetric bone mineral density (vBMD) measurements from routine thoracoabdominal computed tomography (CT) assessed with segmentations by a convolutional neural network and automated correction of contrast phases, on diverse scanners, with scanner-specific asynchronous or scanner-agnostic calibrations.

METHODS

We obtained 679 observations from 278 CT scans in 121 patients (77 males, 63.6%) studied from 04/2019 to 06/2020. Observations consisted of two vBMD measurements from Δdifferent reconstruction kernels (n = 169), Δcontrast phases (n = 133), scan Δsessions (n = 123), Δscanners (n = 63), or Δall of the aforementioned (n = 20), and observations lacking scanner-specific calibration (n = 171). Precision was assessed using root-mean-square error (RMSE) and root-mean-square coefficient of variation (RMSCV). Cross-measurement agreement was assessed using Bland-Altman plots; outliers within 95% confidence interval of the limits of agreement were reviewed.

RESULTS

Repeated measurements from Δdifferent reconstruction kernels were highly precise (RMSE 3.0 mg/cm3; RMSCV 1.3%), even for consecutive scans with different Δcontrast phases (RMSCV 2.9%). Measurements from different Δscan sessions or Δscanners showed decreased precision (RMSCV 4.7% and 4.9%, respectively). Plot-review identified 12 outliers from different scan Δsessions, with signs of hydropic decompensation. Observations with Δall differences showed decreased precision compared to those lacking scanner-specific calibration (RMSCV 5.9 and 3.7, respectively).

CONCLUSION

Automatic vBMD assessment from routine CT is precise across varying setups, when calibrated appropriately. Low precision was found in patients with signs of new or worsening hydropic decompensation, what should be considered an exclusion criterion for both opportunistic and dedicated quantitative CT.

RELEVANCE STATEMENT

Automated CT-based vBMD measurements are precise in various scenarios, including cross-session and cross-scanner settings, and may therefore facilitate opportunistic screening for osteoporosis and surveillance of BMD in patients undergoing routine clinical CT scans.

KEY POINTS

Artificial intelligence-based tools facilitate BMD measurements in routine clinical CT datasets. Automated BMD measurements are highly reproducible in various settings. Reliable, automated opportunistic osteoporosis diagnostics allow for large-scale application.

Correlations among MRI-based cervical and thoracic vertebral bone quality score, CT-based Hounsfield Unit score, and DEXA t-score in assessment of bone mineral density

BACKGROUND

Further optimization of the validated vertebral bone quality (VBQ) score using magnetic resonance imaging (MRI) may expand its clinical utility for bone mineral density (BMD) assessment. This study evaluated the correlations among cervical and thoracic VBQ scores, the validated Hounsfield Unit (HU) measured on computed tomography (CT), and dual-energy x-ray absorptiometry (DEXA) values.

METHODS

We retrieved the medical and radiographic records of 165 patients who underwent synchronous MRI of the cervical and thoracic spine, as well as DEXA and CT imaging of the spine obtained within 1 year of each other between 2015 and 2022. Radiographic data consisted of the MRI-based cervical and thoracic VBQ scores, CT-based HU, and DEXA T-scores of the spine and hip. Patient age, sex, body mass index (BMI), and ethnicity were also obtained.

RESULTS

Mean cervical and thoracic VBQ scores were 3.99 ± 1.68 and 3.82 ± 2.11, respectively. Mean HU and DEXA T-scores of the spine and hip were 135.75 ± 60.36, -1.01 ± 1.15, and -0.47 ± 2.27. All correlations among VBQ, HU, and DEXA were insignificant except for weak correlations between cervical and thoracic VBQ, and cervical VBQ and HU. No correlations were observed between radiographic scores and patient age or BMI. No differences based on ethnicity or sex were observed with respect to cervical or thoracic VBQ, HU, or DEXA.

CONCLUSION

Cervical and thoracic VBQ scores are distinct from Hounsfield Unit and DEXA values. VBQ scoring in the cervical and thoracic spine is not influenced by patient age, ethnicity, sex, or BMI.

Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients

OBJECTIVE

This study aimed to develop and validate a predictive model for osteoporotic vertebral fractures (OVFs) risk by integrating demographic, bone mineral density (BMD), CT imaging, and deep learning radiomics features from CT images.

METHODS

A total of 169 osteoporosis-diagnosed patients from three hospitals were randomly split into OVFs (n = 77) and Non-OVFs (n = 92) groups for training (n = 135) and test (n = 34). Demographic data, BMD, and CT imaging details were collected. Deep transfer learning (DTL) using ResNet-50 and radiomics features were fused, with the best model chosen via logistic regression. Cox proportional hazards models identified clinical factors. Three models were constructed: clinical, radiomics-DTL, and fusion (clinical-radiomics-DTL). Performance was assessed using AUC, C-index, Kaplan-Meier, and calibration curves. The best model was depicted as a nomogram, and clinical utility was evaluated using decision curve analysis (DCA).

RESULTS

BMD, CT values of paravertebral muscles (PVM), and paravertebral muscles' cross-sectional area (CSA) significantly differed between OVFs and Non-OVFs groups (P < 0.05). No significant differences were found between training and test cohort. Multivariate Cox models identified BMD, CT values of PVM, and CSAPS reduction as independent OVFs risk factors (P < 0.05). The fusion model exhibited the highest predictive performance (C-index: 0.839 in training, 0.795 in test). DCA confirmed the nomogram's utility in OVFs risk prediction.

CONCLUSION

This study presents a robust predictive model for OVFs risk, integrating BMD, CT data, and radiomics-DTL features, offering high sensitivity and specificity. The model's visualizations can inform OVFs prevention and treatment strategies.

Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions

BACKGROUND

Forty to fifty percent of women and 13%-22% of men experience an osteoporosis-related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x-Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2-D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed.

PURPOSE

To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.

METHODS

An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD.

RESULTS

Dice scores of accuracy (n = 20), reproducibility (n = 5), and generalizability (n = 12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived reference measure. After 3-digit approximation, each of Pearson and concordance correlation coefficients as well as intraclass correlation coefficient (ICC) between baseline and repeat scans were 0.996 with RMSCV of 0.72%. Results of ULD CT-based bone analysis on 100 participants (age (mean ± SD) 73.6 ± 6.6 years) show that males have significantly greater (p < 0.01) vBMD at the femoral head and trochanteric regions than females, while females have moderately greater vBMD (p = 0.05) at the medial half of the femoral neck than males.

CONCLUSION

Deep learning, combined with shape model and finite element analysis, offers an accurate, reproducible, and generalizable algorithm for automated segmentation of the proximal femur and anatomic femoral subregions using ULD hip CT images. ULD CT-based regional measures of femoral vBMD are accurate and reproducible and demonstrate regional differences between males and females.

Adding Vertebral Bone Quality to the Fusion Risk Score: Does It Improve Predictions of Postoperative Complications?

STUDY DESIGN

Retrospective review of prospectively collected data.

OBJECTIVE

The current study evaluates whether the addition of the Vertebral Bone Quality (VBQ) score to the Fusion Risk Score (FRS) improves its ability to predict perioperative outcomes.

SUMMARY OF BACKGROUND DATA

The FRS was developed to assess preoperative risk in patients undergoing thoracic and lumbar fusions. It includes patient-derived and surgical variables, but it does not include one that directly accounts for bone health. The VBQ score allows assessment of bone quality and has been shown to correlate to DEXA-measured bone mineral density (BMD) scores.

METHODS

The VBQ score was weighted based on a regression model and then added to the FRS (FRS/VBQ). The ability of the two scores to predict the outcomes was then assessed using the area under the curve (AUC).

PATIENT SAMPLE

Patients undergoing elective thoracic and lumbar spinal fusion from January 2019 to June 2020 were included.

OUTCOME MEASURES

The study evaluated various perioperative adverse outcomes, including major and minor adverse events, discharge other than home, extended length of stay, 90-day emergency department visits, 90-day readmission, and 90-day and 2-year reoperation rates.

RESULTS

A total of 353 met the inclusion and exclusion criteria. The FRS/VBQ demonstrated improved predictive ability compared with the FRS alone when evaluating 90-day reoperation. Both scores showed fair predictive ability for any adverse event, major adverse events, minor adverse events, and 2-year reoperation rates, with AUCs ranging from 0.700 to 0.737. Both had poor predictive ability for the other outcomes.

CONCLUSIONS

Adding VBQ to the FRS significantly enhances its predictive accuracy for reoperation rate. This updated risk score provides a more comprehensive understanding of a patient's preoperative risk profile, aiding both patients and physicians in assessing surgical risks and optimizing outcomes through preoperative risk stratification.

LEVEL OF EVIDENCE

3.

Osteoporotic Precise Screening Using Chest Radiography and Artificial Neural Network: The OPSCAN Randomized Controlled Trial

Background Diagnosing osteoporosis is challenging due to its often asymptomatic presentation, which highlights the importance of providing screening for high-risk populations. Purpose To evaluate the effectiveness of dual-energy x-ray absorptiometry (DXA) screening in high-risk patients with osteoporosis identified by an artificial intelligence (AI) model using chest radiographs. Materials and Methods This randomized controlled trial conducted at an academic medical center included participants 40 years of age or older who had undergone chest radiography between January and December 2022 without a history of DXA examination. High-risk participants identified with the AI-enabled chest radiographs were randomly allocated to either a screening group, which was offered fully reimbursed DXA examinations between January and June 2023, or a control group, which received usual care, defined as DXA examination by a physician or patient on their own initiative without AI intervention. A logistic regression was used to test the difference in the primary outcome, new-onset osteoporosis, between the screening and control groups. Results Of the 40 658 enrolled participants, 4912 (12.1%) were identified by the AI model as high risk, with 2456 assigned to the screening group (mean age, 71.8 years ± 11.5 [SD]; 1909 female) and 2456 assigned to the control group (mean age, 72.1 years ± 11.8; 1872 female). A total of 315 of 2456 (12.8%) participants in the screening group underwent fully reimbursed DXA, and 237 of 315 (75.2%) were identified with new-onset osteoporosis. After including DXA results by means of usual care in both screening and control groups, the screening group exhibited higher rates of osteoporosis detection (272 of 2456 [11.1%] vs 27 of 2456 [1.1%]; odds ratio [OR], 11.2 [95% CI: 7.5, 16.7]; P < .001) compared with the control group. The ORs of osteoporosis diagnosis were increased in screening group participants who did not meet formalized criteria for DXA compared with those who did (OR, 23.2 [95% CI: 10.2, 53.1] vs OR, 8.0 [95% CI: 5.0, 12.6]; interactive P = .03). Conclusion Providing DXA screening to a high-risk group identified with AI-enabled chest radiographs can effectively diagnose more patients with osteoporosis. Clinical trial registration no. NCT05721157 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Smith and Rothenberg in this issue.

Coronal plane in opportunistic screening of osteoporosis using computed tomography: comparison with axial and sagittal planes

OBJECTIVE

To compare the coronal plane with axial and sagittal planes in opportunistic screening of osteoporosis using computed tomography (CT).

MATERIALS AND METHODS

A total of 100 patients aged ≥ 50 years who underwent both lumbar spine CT and dual-energy X-ray absorptiometry within 3 months were included. Osteoporosis was diagnosed based on dual-energy X-ray absorptiometry results. The CT number was measured at the center of the vertebral body in coronal, axial, and sagittal planes. To compare the coronal plane with axial and sagittal planes in diagnosing osteoporosis, the areas under the receiver operating characteristic curve (AUC) were compared and intraclass correlation coefficient (ICC) was calculated. The optimal cutoff values were calculated using Youden's index.

RESULTS

The AUC of the coronal plane (0.80; 95% confidence interval [CI], 0.71-0.89) was not significantly different from that of the axial plane (0.78; 95% CI, 0.68-0.87; P = 0.39) and that of the sagittal plane (0.78; 95% CI, 0.69-0.87; P = 0.68). Excellent concordance rates were observed between coronal and axial planes with ICC of 0.95 (95% CI, 0.92-0.96) and between coronal and sagittal planes with ICC of 0.93 (95% CI, 0.85-0.96). The optimal cutoff values for the coronal, axial, and sagittal planes were 110, 112, and 112 HU, respectively.

CONCLUSION

The coronal plane does not significantly differ from axial and sagittal planes in opportunistic screening of osteoporosis. Thus, the coronal plane as well as axial and sagittal planes can be used interchangeably in measuring bone mineral density using CT.

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.

Comparison of predictive value for cage subsidence between MRI-based endplate bone quality and vertebral bone quality scores following transforaminal lumbar interbody fusion: a retrospective propensity-matched study

BACKGROUND CONTEXT

Cage subsidence after lumbar fusion can lead to many adverse outcomes. Low bone mineral density (BMD) is a widely recognized risk factor for cage subsidence. Conventional methods can predict and evaluate BMD, but there are many shortcomings. Recently, MRI-based assessment of bone quality in specific parts of the vertebral body has been proposed, including scores for vertebral bone quality (VBQ) and endplate bone quality (EBQ). However, the predictive accuracy of the two scoring systems for cage subsidence after transforaminal lumbar interbody fusion (TLIF) remains unknown. Therefore, we investigated MRI-based VBQ and EBQ scores for assessing bone quality and compared their predictive value for cage subsidence after TLIF.

PURPOSE

To compare the predictive value between MRI-based VBQ and EBQ scores for cage subsidence after TLIF.

STUDY DESIGN/SETTING

A retrospective case-control study.

PATIENTS SAMPLE

Patients with degenerative lumbar diseases underwent single-level TLIF at our medical center between 2014 and 2020, all of whom had preoperative MRIs available.

OUTCOMES MEASURES

Cage subsidence, disc height, VBQ score, EBQ score, upper and lower vertebral body bone quality (UL-VBQ) score.

METHODS

Data were retrospectively examined for a consecutive sample of 346 patients who underwent TLIF at our medical center between 2014 and 2020. Patients who subsequently experienced cage subsidence or not were matched to each other based on propensity scoring, and the two matched groups (52 patients each) were compared using conditional logistic regression to investigate the association between the potential radiographic factors and cage subsidence. Scores for VBQ and EBQ were assessed for their ability to predict cage subsidence in the matched patients based on the area under the receiver operative characteristic curve (AUC).

RESULTS

Among matched patients, those who suffered cage subsidence had significantly higher VBQ score (3.7 vs 3.1, p<.001) and EBQ score (5.0 vs 4.3, p<.001), and regression linked greater risk of subsidence to higher VBQ score (OR 4.557, 95% CI 1.076-19.291, p=.039) and higher EBQ score (OR 5.396, 95% CI 1.158-25.146, p=.032). A cut-off VBQ score of 3.4 predicted the cage subsidence among matched patients with an AUC of 0.799, sensitivity of 84.6%, and specificity of 69.2%. A cut-off EBQ score of 4.7 predicted subsidence with an AUC of 0.829, sensitivity of 76.9%, and specificity of 82.7%.

CONCLUSION

Higher VBQ and EBQ scores are associated with a greater risk of cage subsidence following TLIF, and EBQ may perform better because of greater specificity.

Optimal scanning parameters of lumbar bone density measured by fast kilovoltage-switching dual-energy computed tomography (DECT)

Background

The technological innovation of fast kilovoltage (KV)-switching dual-energy computed tomography (DECT) has enabled the accurate measurement of vertebral bone density; however, it does not account for the effects of abdominal fat and ribs on the vertebral body. In our study, a European spine phantom (ESP) was used to establish an abdominal phantom for normal weight and obese people, and to explore the best scanning parameters for DECT to measure the bone mineral density (BMD) of the human lumbar spine.

Methods

Revolution CT was used to conduct energy spectrum scanning for each body mode. A total of 20 sets of energy spectrum scans was conducted and each set of conditions was scanned 10 times. The data conformed to a normal distribution, and the differences between the measured and actual values of ESP L1-3 vertebrae were compared using a one-sample t-test, and quantitative data were described byx ¯ ± s . A P value <0.05 was considered statistically significant. Relative error (RE) and root mean square error (RMSE) of BMD measurements were calculated for different scanning conditions in normal and obese populations.

Results

When simulating the upper abdominal condition (L1-2 level, fat area 140 cm2, with rib influence) in a normal weight population, there was no statistical difference (P>0.05) in BMD measurements for each vertebra at 0.8 s/rotation (rot) with different tube currents, the smallest RE at 0.8 s/rot, 190 mA condition, and the smallest RMSE for L1 and 2 vertebral BMD measurements at 190 mA; when simulating the abdominal condition at the L4 level in a normal weight population (fat area of 240 cm2, no rib influence), there were no statistical differences between the measurements at 0.8 s/rot, 190 and 275 mA conditions (P>0.05), and the RE and RMSE in the 190 mA condition was smaller than that in the 275 mA condition. Simulating the upper abdominal condition in the obese population (L1-2 level, fat area 340 cm2, with rib influence), there were no statistical difference between the measurements in the 0.8 s/rot, 315 and 355 mA conditions (P>0.05), the RE and RMSE in the 315 mA condition was less than those in the 355 mA; simulated obese abdominal condition at the L4 level in the population (fat area 450 cm2, no rib influence) resulted in 0.8 s/rot, no statistical difference in measurements between 315 mA (P>0.05), RE in 315 mA conditions were L1: 3.75%, L2: -1.06%, L3: 0.42%, and the RMSE under 315 mA condition were L1: 2.13, L2: 1.21, L3: 1.66.

Conclusions

When using Revolution CT to measure lumbar spine bone density, 0.8 s/rot at 190 mA may be the best scanning parameter for a normal weight population, and 0.8 s/rot at 315 mA may be the best scanning parameter for an obese population.

Association between BMD and coronary artery calcification: an observational and Mendelian randomization study

Observational studies have reported inconsistent associations between bone mineral density (BMD) and coronary artery calcification (CAC). We examined the observational association of BMD with CAC in 2 large population-based studies and evaluated the evidence for a potential causal relation between BMD and CAC using polygenic risk scores (PRS), 1- and 2-sample Mendelian randomization (MR) approaches. Our study populations comprised 1414 individuals (mean age 69.9 yr, 52.0% women) from the Rotterdam Study and 2233 individuals (mean age 56.5 yr, 50.9% women) from the Framingham Heart Study with complete information on CAC and BMD measurements at the total body (TB-), lumbar spine (LS-), and femoral neck (FN-). We used linear regression models to evaluate the observational association between BMD and CAC. Subsequently, we compared the mean CAC across PRSBMD quintile groups at different skeletal sites. In addition, we used the 2-stage least squares regression and the inverse variance weighted (IVW) model as primary methods for 1- and 2-sample MR to test evidence for a potentially causal association. We did not observe robust associations between measured BMD levels and CAC. These results were consistent with a uniform random distribution of mean CAC across PRSBMD quintile groups (P-value > .05). Moreover, neither 1- nor 2-sample MR supported the possible causal association between BMD and CAC. Our results do not support the contention that lower BMD is (causally) associated with an increased CAC risk. These findings suggest that previously reported epidemiological associations of BMD with CAC are likely explained by unmeasured confounders or shared etiology, rather than by causal pathways underlying both osteoporosis and vascular calcification processes.

State of the Art Imaging of Osteoporosis

Osteoporosis is a common disease, particularly prevalent in geriatric populations, which causes significant worldwide morbidity due to increased bone fragility and fracture risk. Currently, the gold-standard modality for diagnosis and evaluation of osteoporosis progression and treatment relies on dual-energy x-ray absorptiometry (DXA), which measures bone mineral density (BMD) and calculates a score based upon standard deviation of measured BMD from the mean. However, other imaging modalities can also be used to evaluate osteoporosis. Here, we review historical as well as current research into development of new imaging modalities that can provide more nuanced or opportunistic analyses of bone quality, turnover, and density that can be helpful in triaging severity and determining treatment success in osteoporosis. We discuss the use of opportunistic computed tomography (CT) scans, as well as the use of quantitative CT to help determine fracture risk and perform more detailed bone quality analysis than would be allowed by DXA . Within magnetic resonance imaging (MRI), new developments include the use of advanced MRI techniques such as quantitative susceptibility mapping (QSM), magnetic resonance spectroscopy, and chemical shift encoding-based water-fat MRI (CSE-MRI) to enable clinicians improved assessment of nonmineralized bone compartments as well as a way to longitudinally assess bone quality without the repeated exposure to ionizing radiation. Within ultrasound, development of quantitative ultrasound shows promise particularly in future low-cost, broadly available screening tools. We focus primarily on historical and recent developments within radiotracer use as applicable to osteoporosis, particularly in the use of hybrid methods such as NaF-PET/CT, wherein patients with osteoporosis show reduced uptake of radiotracers such as NaF. Use of radiotracers may provide clinicians with even earlier detection windows for osteoporosis than would traditional biomarkers. Given the metabolic nature of this disease, current investigation into the role molecular imaging can play in the prediction of this disease as well as in replacing invasive diagnostic procedures shows particular promise.

Vertebral and Femoral Bone Mineral Density (BMD) Assessment with Dual-Energy CT versus DXA Scan in Postmenopausal Females

This study aimed to demonstrate the potential role of dual-energy CT in assessing bone mineral density (BMD) using hydroxyapatite-fat material pairing in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using a HAP-fat material pair. Statistical analysis was performed using the Bland-Altman method to assess the agreement between DXA and DECT HAP-fat measurements. Mean BMD, vertebral, and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP-fat values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) was found for measurements at the femoral neck. In conclusion, we demonstrate the enhanced importance of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

OBJECTIVE

Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis.

METHODS

We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis.

RESULTS

A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7).

CONCLUSIONS

Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

Development of early complications after treatment of trochanteric fractures with an intramedullary sliding hip screw in a geriatric population

PURPOSE

Although trochanteric fractures (TF) are a frequent event in the geriatric population, studies reporting on complication rates associated with surgical treatment are sparse. Thus, this study investigated the relevance of fracture-, implant-, and surgery-associated complications in TF. Furthermore, the role of possible risk factors for the before mentioned complications was investigated.

METHODS

A consecutive series of patients with TF treated by intramedullary nailing with a sliding screw device was evaluated. Data were sampled retrospectively from the hospital patient information system and anonymized at the source. Demographic data and information regarding fracture pattern, the treatment performed, hospital stay, and evaluation of operative and follow-up radiographs were analyzed. Intraoperative problems (i.e., technical problems with the implant, intraoperative fracture) and postoperative complications were investigated.

RESULTS

Postoperative surgical complications were noted in 11.7%. The most frequent surgical problem was a difficult fracture reduction (13%) and intraoperative fracture dislocation (3.6%). The most frequent postoperative complication was intra-hospital mortality (3.6%), delayed/non-union (2.7%), and a cut-out of the lag screw in the femoral head (2.3%). Implant failure (1,4%) was significantly associated with morbid obesity while cut-out (2,3%) correlated with a higher tip-apex distance (TAD). A complex fracture type and a suboptimal screw position significantly increased the cut-out rate to 5% (p = 0.018).

CONCLUSION

Complications after TF treatment occur frequently. While patient-associated variables such as morbid obesity cannot be influenced by the surgeon, correct fracture reduction and implant positioning remain to be of highest importance.

Deep Convolutional Neural Networks Provide Motion Grading for High-Resolution Peripheral Quantitative Computed Tomography of the Scaphoid

In vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) studies on bone characteristics are limited, partly due to the lack of standardized and objective techniques to describe motion artifacts responsible for lower-quality images. This study investigates the ability of such deep-learning techniques to assess image quality in HR-pQCT datasets of human scaphoids. In total, 1451 stacks of 482 scaphoid images from 53 patients, each with up to six follow-ups within one year, and each with one non-displaced fractured and one contralateral intact scaphoid, were independently graded by three observers using a visual grading scale for motion artifacts. A 3D-CNN was used to assess image quality. The accuracy of the 3D-CNN to assess the image quality compared to the mean results of three skilled operators was between 92% and 96%. The 3D-CNN classifier reached an ROC-AUC score of 0.94. The average assessment time for one scaphoid was 2.5 s. This study demonstrates that a deep-learning approach for rating radiological image quality provides objective assessments of motion grading for the scaphoid with a high accuracy and a short assessment time. In the future, such a 3D-CNN approach can be used as a resource-saving and cost-effective tool to classify the image quality of HR-pQCT datasets in a reliable, reproducible and objective way.

Functional Imaging in Musculoskeletal Disorders in Menopause

Menopause-related musculoskeletal (MSK) disorders include osteoporosis, osteoarthritis (OA), sarcopenia and sarco-obesity. This review focuses on the applications of nuclear medicine for the functional imaging of the aforementioned clinical conditions. Bone Scan (BS) with 99mTc-labeled phosphonates, alone or in combination with MRI, can identify "fresh" vertebral collapse due to age-associated osteoporosis and provides quantitative parameters characterized by a good correlation with radiological indices in patients with OA. 18F-NaF PET, particularly when performed by dynamic scan, has given encouraging results for measuring bone turnover in osteoporosis and allows the evaluation of subchondral bone metabolic activity in OA. FDG PET can help discriminate between pathological and nonpathological vertebral fractures, especially by applying appropriate SUV-based thresholds. In OA, it can effectively image inflamed joints and support appropriate clinical management. Preliminary evidences suggest a possible application of FDG in sarco-obesity for the detection and quantification of visceral adipose tissue (VAT). Further studies are needed to better define the role of nuclear medicine in menopause-related MSK disease, especially as regards the possible impact of new radiopharmaceuticals (ie, FAPI and RGD peptides) and recent technological advances (eg, total-body PET/CT scanners).

Correlations among Cervical, Thoracic, and lumbar Hounsfield Unit measurements for assessment of bone mineral density

OBJECTIVE

Bone mineral density assessment using Hounsfield Unit (HU) currently depends upon the availability of computed tomography (CT) of the lumbar spine. The primary aim of this study was to evaluate the associations among HU measurements of the cervical (CHU), thoracic (THU), and lumbar (LHU) spine. The secondary aim of this study was to analyze the influence of patient demographic and anthropometric characteristics on HU measurements.

METHODS

Radiographic records of 165 patients who underwent CT of the cervical, thoracic, and lumbar spine were retrieved. The CHU, THU, and LHU were calculated by obtaining the mean signal intensity from the medullary portions of C3-C7, T8-T12, and L1-L4 vertebral bodies.

RESULTS

Mean CHU, THU, and LHU values were 266.26 ± 88.69, 165.57 ± 55.06, and 166.45 ± 51.38. Significant differences of 100.69, 99.81, and 0.88 were observed between CHU and THU (p <.001), CHU and LHU (p <.001), and THU and LHU (p =.023). Correlations of 0.574, 0.488, and 0.686 were observed between CHU and THU (p <.001), CHU and LHU (p <.001), and THU and LHU (p <.001). No differences in HU based on sex, age, height, weight, or ethnicity were observed. Multivariate regression models demonstrated R2 values of 0.770 - 0.790 (p <.001) in prediction of LHU.

CONCLUSIONS

Hounsfield Unit measurements derived from the cervical and thoracic spine correlate with the validated lumbar Hounsfield Unit. Hounsfield Unit measurements do not vary based on sex, ethnicity, age, height, or weight.

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.

Bone Mineral Density Derived from Cardiac CT Scans: Using Contrast Enhanced Scans for Opportunistic Screening

PURPOSE

Osteoporosis is under-diagnosed and often co-exists with other diseases. Very low bone mineral density (BMD) indicates risk of osteoporosis and opportunistic screening for low BMD in CT-scans has been suggested. In a non-contrast enhanced thoracic CT scan, the scan-field-of-view includes vertebrae enabling BMD estimation. However, many CT scans are obtained by administration of contrast material. If the impact of contrast enhancement on BMD measurements could be quantified, considerably more patients are eligible for screening.

METHODS

This study investigated the impact of intravenous contrast on thoracic BMD measurements in cardiac CT scans pre- and post-contrast, including different contrast trigger levels of 130 and 180 Hounsfield units (HU). BMD was measured using quantitative CT with asynchronous calibration.

RESULTS

In 195 participants undergoing cardiac CT (mean age 57±9 years, 37 % females) contrast increased mean thoracic BMD from 116±33 mg/cm3 (non-enhanced CT) to 130±38 mg/cm3 (contrast-enhanced CT) (p<0.001). Using clinical cut-off values for very low (<80 mg/cm3) and low BMD (<120 mg/cm3) showed that 24 % (47/195 participants) were misclassified when BMD was measured on contrast-enhanced CT-scans. Of the misclassified patients, 6 % (12/195 participants) were categorized as having low BMD despite having very low BMD on the non-enhanced images. Contrast-CT using a higher contrast trigger level showed a significant increase in BMD compared to the lower trigger level (119±32 vs. 135±40 mg/cm3, p<0.01).

CONCLUSION

For patients undergoing cardiac CT, using contrast-enhanced images to assess BMD entails substantial overestimation. Contrast protocol trigger levels also affect BMD measurements. Adjusting for these factors is needed before contrast-enhanced images can be used clinically.

MINI ABSTRACT

Osteoporosis is under-diagnosed. Contrast-enhanced CT made to examine other diseases might be utilized simultaneously for bone mineral density (BMD) screening. These scans, however, likely entails overestimation of BMD due to the effect of contrast. Adjusting for this effect is needed before contrast-enhanced images can be implemented clinically for BMD screening.

Towards Improved Identification of Vertebral Fractures in Routine Computed Tomography (CT) Scans: Development and External Validation of a Machine Learning Algorithm

Vertebral fractures (VFs) are the hallmark of osteoporosis, being one of the most frequent types of fragility fracture and an early sign of the disease. They are associated with significant morbidity and mortality. VFs are incidentally found in one out of five imaging studies, however, more than half of the VFs are not identified nor reported in patient computed tomography (CT) scans. Our study aimed to develop a machine learning algorithm to identify VFs in abdominal/chest CT scans and evaluate its performance. We acquired two independent data sets of routine abdominal/chest CT scans of patients aged 50 years or older: a training set of 1011 scans from a non-interventional, prospective proof-of-concept study at the Universitair Ziekenhuis (UZ) Brussel and a validation set of 2000 subjects from an observational cohort study at the Hospital of Holbaek. Both data sets were externally reevaluated to identify reference standard VF readings using the Genant semiquantitative (SQ) grading. Four independent models have been trained in a cross-validation experiment using the training set and an ensemble of four models has been applied to the external validation set. The validation set contained 15.3% scans with one or more VF (SQ2-3), whereas 663 of 24,930 evaluable vertebrae (2.7%) were fractured (SQ2-3) as per reference standard readings. Comparison of the ensemble model with the reference standard readings in identifying subjects with one or more moderate or severe VF resulted in an area under the receiver operating characteristic curve (AUROC) of 0.88 (95% confidence interval [CI], 0.85-0.90), accuracy of 0.92 (95% CI, 0.91-0.93), kappa of 0.72 (95% CI, 0.67-0.76), sensitivity of 0.81 (95% CI, 0.76-0.85), and specificity of 0.95 (95% CI, 0.93-0.96). We demonstrated that a machine learning algorithm trained for VF detection achieved strong performance on an external validation set. It has the potential to support healthcare professionals with the early identification of VFs and prevention of future fragility fractures. © 2023 UCB S.A. and The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Which Indicator Among Lumbar Vertebral Hounsfield Unit, Vertebral Bone Quality, or Dual-Energy X-Ray Absorptiometry-Measured Bone Mineral Density Is More Efficacious in Predicting Thoracolumbar Fragility Fractures?

OBJECTIVE

Hounsfield units (HU), vertebral bone quality (VBQ), and bone mineral density (BMD) can all serve as predictive indicators for thoracolumbar fragility fractures. This study aims to explore which indicator provides better risk prediction for thoracolumbar fragility fractures.

METHODS

Patients who have received medical attention from The First Affiliated Hospital of Anhui Medical University for thoracolumbar fragility fractures were selected. A total of 78 patients with thoracolumbar fragility fractures were included in the study. To establish a control group, 78 patients with degenerative spinal diseases were matched to the fracture group on the basis of gender, age, and body mass index. The lumbar vertebral HU, the VBQ, and the BMD were obtained for all the 156 patients through computed tomography, magnetic resonance imaging, and dual-energy x-ray absorptiometry (DEXA). The correlations among these parameters were analyzed. The area under curve (AUC) analysis was employed to assess the predictive efficacy and thresholds of lumbar vertebral HU, VBQ, and BMD in relation to the risk of thoracolumbar fragility fractures.

RESULTS

Among the cohort of 156 patients, lumbar vertebral HU exhibited a positive correlation with BMD (p < 0.01). Conversely, VBQ showed a negative correlation with HU, BMD (p < 0.05). HU and BMD displayed a favorable predictive efficacy for thoracolumbar fragility fractures (p < 0.01), with HU (AUC = 0.863) showcasing the highest predictive efficacy, followed by the DEXA-measured BMD (AUC = 0.813). VBQ (AUC = 0.602) ranked lowest among the 3 indicators. The thresholds for predicting thoracolumbar fragility fractures were as follows: HU (88),VBQ (3.37), and BMD (0.81).

CONCLUSION

All 3 of these indicators, HU, VBQ, and BMD, can predict thoracolumbar fragility fractures. Notably, lumbar vertebral HU exhibits the highest predictive efficacy, followed by the BMD obtained through DEXA scanning, with VBQ demonstrating the lowest predictive efficacy.

Computed Tomography Provides Improved Quantification of Trabecular Lumbar Spine Bone Loss Compared to Dual-Energy X-Ray Absorptiometry in Ovariectomized Sheep

Early detection of osteoporosis using advanced imaging is imperative to the successful treatment and prevention of high morbidity fractures in aging patients. In this preclinical study, we aimed to compare dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) to quantify bone mineral density (BMD) changes in the sheep lumbar spine. We also aimed to determine the relationship of BMD to microarchitecture in the same animals as an estimate of imaging modality precision. Osteoporosis was induced in 10 ewes via laparoscopic ovariectomy and administration of high-dose corticosteroids. We performed DXA and QCT imaging to measure areal BMD (aBMD) and trabecular volumetric BMD (Tb.vBMD)/cortical vBMD (Ct.vBMD), respectively, at baseline (before ovariectomy) and at 3, 6, 9, and 12 months after ovariectomy. Iliac crest bone biopsies were collected at each time point for micro-computed tomography (microCT) analysis; bone volume fraction (BV/TV), trabecular number (Tb.N), thickness (Tb.Th), and spacing (Tb.Sp) were reported. aBMD and Tb.vBMD both decreased significantly by 3 and 6 months (p < 0.05) compared with baseline, whereas no changes to Ct.vBMD were observed. Combined (Tb. and Ct.) vBMD was significantly correlated with aBMD at all time points (all p < 0.05). Additionally, greater significant correlations were found between BV/TV and Tb.vBMD at all five time points (R 2 = 0.54, 0.57, 0.66, 0.46, and 0.56, respectively) than with aBMD values (R 2 = 0.23, 0.55, 0.41, 0.20, and 0.19, respectively). The higher correlation of microCT values with QCT than with DXA indicates that QCT provides additional detailed information regarding bone mineral density changes in preclinical settings. Because trabecular bone is susceptible to rapid density loss and structural changes during osteoporosis, QCT can capture these subtle changes more precisely than DXA in a large animal preclinical model. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features

PURPOSE

To assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).

METHODS

A total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework ( https://anduin.bonescreen.de ). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs.

RESULTS

Skewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64-0.76]; malignant fracture group: 0.59 [0.56-0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs.

CONCLUSION

Three-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

A meta-analysis of the value of MRI-based VBQ scores for evaluating osteoporosis

Objective

Osteoporosis is the most common skeletal disease in humans. Early onset of osteoporosis is usually asymptomatic, so early diagnosis is critical. The purpose of this study was to analyze the value of MRI-based VBQ scores for evaluating osteoporosis.

Methods

We searched PubMed, Embase, the Cochrane Library databases, Web of Science, and some Chinese electronic databases for published articles and the ClinicalTrials.gov site for completed but unpublished studies on evaluating the value of MRI-based VBQ scores for evaluating osteoporosis. We calculated the summarized sensitivity, specificity, the ROC curve (AUC) values and their 95% confidence intervals (CIs) using MetaDiSc 1.4 software and STATA.

Results

Our study included 8 studies involving 999 patients of which 660 patients were diagnosed with osteopenia/osteoporosis, and 339 patients were identified as having normal BMD. The pooled sensitivity was 0.809 (95% CI, 0.777-0.838, I 2  = 78.8%), the pooled specificity was 0.640 (95% CI, 0.587-0.691, I 2  = 85.9%), and the pooled AUC was 0.8375.

Conclusion

MRI-based VBQ scores provided high sensitivity and moderate specificity in detecting osteoporosis. Opportunistic use of VBQ scores could be considered, e.g. before lumbar spine surgery.

Prospero registration number

CRD42022377024.

Detection of osteoporotic-related bone changes and prediction of distal radius strength using Raman spectra from excised human cadaver finger bones

While osteoporosis is reliably diagnosed using dual energy X-ray absorptiometry (DXA), screening rates are alarmingly low, contributing to preventable fractures. Raman spectroscopy (RS) can detect biochemical changes that occur in bones transcutaneously and can arguably be more accessible than DXA as a fracture risk assessment. A reasonable approach to translate RS is to interrogate phalangeal bones of human hands, where the soft tissues covering the bone are less likely to hamper transcutaneous measurements. To that end, we set out to first determine whether Raman spectra obtained from phalangeal bones correlate with distal radius fracture strength, which can predict subsequent osteoporotic fractures at the spine and hip. We performed RS upon diaphyseal and epiphyseal regions of exposed proximal phalanges from 12 cadaver forearms classified as healthy (n = 3), osteopenic (n = 4), or osteoporotic (n = 5) based on wrist T-scores measured by DXA. We observed a significant decrease in phosphate to matrix ratio and a significant increase in carbonate substitution in the osteoporotic phalanges relative to healthy and osteopenic phalanges. Multivariate regression models produced wrist T-score estimates with significant correlation to the DXA-measured values (r = 0.79). Furthermore, by accounting for phalangeal RS parameters, body mass index, and age, a multivariate regression significantly predicted distal radius strength measured in a simulated-fall biomechanical test (r = 0.81). These findings demonstrate the feasibility of interrogating the phalanges using RS for bone quality assessment of distant clinical sites of fragility fractures, such as the wrist. Future work will address transcutaneous measurement challenges as another requirement for scale-up and translation.

Influence of demographic factors on the occurrence of motion artefacts in HR-pQCT

The study shows a high incidence of motion artefacts in a central European population and a significant increase of those artefacts with higher age. These findings may impact on the design and conduct of future in vivo HR-pQCT studies or at least help to estimate the potential number of drop outs due to unusable image quality.

PURPOSE

Motion artefacts in high-resolution peripheral quantitative computed tomography (HR-pQCT) are challenging, as they introduce error into the resulting measurement data. The aim of this study was to assess the general occurrence of motion artefacts in healthy distal radius and to evaluate the influence of demographic factors.

METHODS

The retrospective study is based on 525 distal radius second-generation HR-pQCT scans of 95 patients. All stacks were evaluated by two experienced observers and graded according to the visual grading scale recommended by the manufacturer, ranging from grade 1 (no visible motion artefacts) to grade 5 (severe motion artefacts). Correlations between demographic factors and image quality were evaluated using a linear mixed effects model analysis.

RESULTS

The average visual grading was 2.7 (SD ± 0.7). Age and severity of motion artefacts significantly correlated (p = 0.026). Patients aged 65 years or above had an average image quality between grades 1 and 3 in 72.7% of cases, while patients younger than 65 had an average image quality between grades 1 and 3 in 91.9% of cases. Gender, smoking behaviour, and handedness had no significant influence on motion artefacts.

CONCLUSION

This study showed a high incidence of motion artefacts in a representative central European population, but also a significant increase of motion artefacts with higher age. This could impact further study designs by planning for a sufficiently large and if possible a more selective study population to gain a representative amount of high-quality image data.

Assessing bone mineral density in non-weight bearing regions of the body: a texture analysis approach using abdomen and pelvis computed tomography Hounsfield units-a cross-sectional study

Background

Highlighting a gap in comprehending bone microarchitecture's intricacies using dual-energy X-ray absorptiometry (DXA), this study aims to bridge this chasm by analyzing texture in non-weight bearing regions on axial computed tomography (CT) scans. Our goal is to enrich osteoporosis patient management by enhancing bone quality and microarchitecture insights.

Methods

Conducted at Busan Medical Center from March 1, 2013, to August 30, 2022, 1,320 cases (782 patients) were screened. After applying exclusion criteria, 458 samples (296 patients) underwent bone mineral density (BMD) assessment with both CT and DXA. Regions of interest (ROIs) included spine pedicle's maximum trabecular area, sacrum Zone 1, superior/inferior pubic ramus, and femur's greater/lesser trochanters. Texture features (n=45) were extracted from ROIs using gray-level co-occurrence matrices. A regression model predicted BMD, spotlighting the top five influential texture features.

Results

Correlation coefficients ranged from 0.709 (lowest for total femur BMD) to 0.804 (highest for femur intertrochanter BMD). Mean squared error (MSE) values were also provided for lumbar and femur BMD/bone mineral content (BMC) metrics. The most influential texture features included contrast_32, correlation_32_v, and three other metrics.

Conclusions

By melding traditional DXA and CT texture analysis, our approach presents a comprehensive bone health perspective, potentially revolutionizing osteoporosis diagnostics.

Long-term reproducibility of opportunistically assessed vertebral bone mineral density and texture features in routine clinical multi-detector computed tomography using an automated segmentation framework

Background

To investigate reproducibility of texture features and volumetric bone mineral density (vBMD) extracted from trabecular bone in the thoracolumbar spine in routine clinical multi-detector computed tomography (MDCT) data in a single scanner environment.

Methods

Patients who underwent two routine clinical thoraco-abdominal MDCT exams at a single scanner with a time interval of 6 to 26 months (n=203, 131 males; time interval mean, 13 months; median, 12 months) were included in this observational study. Exclusion criteria were metabolic and hematological disorders, bone metastases, use of bone-active medications, and history of osteoporotic vertebral fractures (VFs) or prior diagnosis of osteoporosis. A convolutional neural network (CNN)-based framework was used for automated spine labeling and segmentation (T5-L5), asynchronous Hounsfield unit (HU)-to-BMD calibration, and correction for the intravenous contrast medium phase. Vertebral vBMD and six texture features [varianceglobal, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP)] were extracted for mid- (T5-T8) and lower thoracic (T9-T12), and lumbar vertebrae (L1-L5), respectively. Relative annual changes were calculated in texture features and vBMD for each vertebral level and sorted by sex, and changes were checked for statistical significance (P<0.05) using paired t-tests. Root mean square coefficient of variation (RMSCV) and root mean square error (RMSE) were calculated as measures of variability.

Results

SRE, LRE, RLN, and RP exhibited substantial reproducibility with RMSCV-values below 2%, for both sexes and at all spine levels, while vBMD was less reproducible (RMSCV =11.9-16.2%). Entropy showed highest variability (RMSCV =4.34-7.69%) due to statistically significant increases [range, mean ± standard deviation: (4.40±5.78)% to (8.36±8.66)%, P<0.001]. RMSCV of varianceglobal ranged from 1.60% to 3.03%.

Conclusions

Opportunistic assessment of texture features in a single scanner environment using the presented CNN-based framework yields substantial reproducibility, outperforming vBMD reproducibility. Lowest scan-rescan variability was found for higher-order texture features. Further studies are warranted to determine, whether microarchitectural changes to the trabecular bone may be assessed through texture features.

MRI-based vertebral bone quality score for predicting cage subsidence by assessing bone mineral density following transforaminal lumbar interbody fusion: a retrospective analysis

PURPOSE

This is the first study to evaluate the predictive value of the vertebral bone quality (VBQ) score on cage subsidence after transforaminal lumbar interbody fusion (TLIF) in a Chinese population using the spinal quantitative computed tomography (QCT) as the clinical standard. Meanwhile, the accuracy of the MRI-based VBQ score in bone mineral density (BMD) measurement was verified.

METHODS

We performed a retrospective study of patients who underwent single-level TLIF from 2015 to 2020 with at least 1 year of follow-up. Cage subsidence was measured using postoperative radiographic images based on cage protrusion through the endplates more than 2 mm. The VBQ score was measured on T1-weighted MRI. The results were subjected to statistical analysis.

RESULTS

A total of 283 patients (61.1% of female) were included in the study. The subsidence rate was with 14.1% (n = 40), and the average cage subsidence was 2.3 mm. There was a significant difference in age, sex, VBQ score and spinal QCT between the subsidence group and the no-subsidence group. The multivariable analysis demonstrated that only an increased VBQ score (OR = 2.690, 95% CI 1.312-5.515, p = 0.007) and decreased L1/2 QCT-vBMD (OR = 0.955, 95% CI 0.933-0.977, p < 0.001) were associated with an increased rate of cage subsidence. The VBQ score was found to be moderately correlated with the spinal QCT (r = -0.426, p < 0.001). The VBQ score was shown to significantly predict cage subsidence, with an accuracy of 82.5%.

CONCLUSION

Our findings indicate that the MRI-based VBQ score is a significant predictor of cage subsidence and could be used to assess BMD.

Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography

OBJECTIVE

This study developed a diagnostic tool combining machine learning (ML) segmentation and radiomic texture analysis (RTA) for bone density screening using chest low-dose computed tomography (LDCT).

METHODS

A total of 197 patients who underwent LDCT followed by dual-energy X-ray absorptiometry were analyzed. First, an autosegmentation model was trained using LDCT to delineate the thoracic vertebral body (VB). Second, a two-level classifier was developed using radiomic features extracted from VBs for the hierarchical pairwise classification of each patient's bone status. All the patients were initially classified as either normal or abnormal, and all patients with abnormal bone density were then subdivided into an osteopenia group and an osteoporosis group. The performance of the classifier was evaluated through fivefold cross-validation.

RESULTS

The model for automated VB segmentation achieved a Sorenson-Dice coefficient of 0.87 ± 0.01. Furthermore, the area under the receiver operating characteristic curve scores for the two-level classifier were 0.96 ± 0.01 for detecting abnormal bone density (accuracy = 0.91 ± 0.02; sensitivity = 0.93 ± 0.03; specificity = 0.89 ± 0.03) and 0.98 ± 0.01 for distinguishing osteoporosis (accuracy = 0.94 ± 0.02; sensitivity = 0.95 ± 0.03; specificity = 0.93 ± 0.03). The testing prediction accuracy levels for the first- and second-level classifiers were 0.92 ± 0.04 and 0.94 ± 0.05, respectively. The overall testing prediction accuracy of our method was 0.90 ± 0.05.

CONCLUSION

The combination of ML segmentation and RTA for automated bone density prediction based on LDCT scans is a feasible approach that could be valuable for osteoporosis screening during lung cancer screening.

KEY POINTS

• This study developed an automatic diagnostic tool combining machine learning-based segmentation and radiomic texture analysis for bone density screening using chest low-dose computed tomography. • The developed method enables opportunistic screening without quantitative computed tomography or a dedicated phantom. • The developed method could be integrated into the current clinical workflow and used as an adjunct for opportunistic screening or for patients who are ineligible for screening with dual-energy X-ray absorptiometry.

Automatic Diagnosis of Spinal Disorders on Radiographic Images: Leveraging Existing Unstructured Datasets With Natural Language Processing

STUDY DESIGN

Retrospective study.

OBJECTIVES

Huge amounts of images and medical reports are being generated in radiology departments. While these datasets can potentially be employed to train artificial intelligence tools to detect findings on radiological images, the unstructured nature of the reports limits the accessibility of information. In this study, we tested if natural language processing (NLP) can be useful to generate training data for deep learning models analyzing planar radiographs of the lumbar spine.

METHODS

NLP classifiers based on the Bidirectional Encoder Representations from Transformers (BERT) model able to extract structured information from radiological reports were developed and used to generate annotations for a large set of radiographic images of the lumbar spine (N = 10 287). Deep learning (ResNet-18) models aimed at detecting radiological findings directly from the images were then trained and tested on a set of 204 human-annotated images.

RESULTS

The NLP models had accuracies between 0.88 and 0.98 and specificities between 0.84 and 0.99; 7 out of 12 radiological findings had sensitivity >0.90. The ResNet-18 models showed performances dependent on the specific radiological findings with sensitivities and specificities between 0.53 and 0.93.

CONCLUSIONS

NLP generates valuable data to train deep learning models able to detect radiological findings in spine images. Despite the noisy nature of reports and NLP predictions, this approach effectively mitigates the difficulties associated with the manual annotation of large quantities of data and opens the way to the era of big data for artificial intelligence in musculoskeletal radiology.

Correlation of bone mineral density using the dual energy x-ray absorptiometry and the magnetic resonance imaging of the lumbar spine in Indian patients

Background

Dual-energy x-ray absorptiometry (DEXA) scan is extensively used to diagnose osteoporosis. But surprisingly, osteoporosis remains an underdiagnosed condition with many fragility fracture patients who have failed to undergo DEXA or received concomitant treatment for osteoporosis. Magnetic resonance imaging (MRI) of the lumbar spine is a routine radiological investigation bring done for low back pain. MRI can detect changes in the bone marrow signal intensity on the standard T1-weighted images. This correlation can be explored to measure osteoporosis in elderly and post-menopausal patients. The present study aims to find any correlation of bone mineral density using the DEXA and MRI of the lumbar spine in Indian patients.

Methods

Five regions of interest (ROI) of size 130-180 mm2 were placed in the vertebral body in the mid-sagittal section and parasagittal sections on either side (four in L1-L4 and one outside body) of elderly patients who underwent MRI for back pain. They also underwent a DEXA scan for osteoporosis. Signal to Noise Ratio (SNR) was calculated by dividing the mean signal intensity obtained for each vertebra by the standard deviation of the noise. Similarly, SNR was measured for 24 controls. An MRI-based "M score" was calculated by getting the difference in SNR patients to SNR controls and then dividing it by the control's standard deviation (SD). Correlation between the T score on DEXA and M scores on MRI was found out.

Results

With the M score greater than or equal to 2.82, the sensitivity was 87.5%, and the specificity was 76.5%. M scores negatively correlated with the T score. With the increase in the T score, the M score decreased. The Spearman correlation coefficient for the spine T score was -0.651, with a p-value of <0.001, and the hip T score was -0.428, with a p-value of 0.013.

Conclusion

Our study indicates that MRI investigations are helpful in Osteoporosis assessments. Even though MRI may not replace DEXA, it can give insight into elderly patients who get an MRI routinely for back pain. It may also have a prognostic value.

MRI-based vertebral bone quality score compared to quantitative computed tomography bone mineral density in patients undergoing cervical spinal surgery

PURPOSE

The vertebral bone quality (VBQ) score based on magnetic resonance imaging (MRI) was introduced as a bone quality marker in the lumbar spine. Prior studies showed that it could be utilized as a predictor of osteoporotic fracture or complications after instrumented spine surgery. The objective of this study was to evaluate the correlation between VBQ scores and bone mineral density (BMD) measured by quantitative computer tomography (QCT) in the cervical spine.

METHODS

Preoperative cervical CT and sagittal T1-weighted MRIs from patients undergoing ACDF were retrospectively reviewed and included. The VBQ score in each cervical level was calculated by dividing the signal intensity of the vertebral body by the signal intensity of the cerebrospinal fluid on midsagittal T1-weighted MRI images and correlated with QCT measurements of the C2-T1 vertebral bodies. A total of 102 patients (37.3% female) were included.

RESULTS

VBQ values of C2-T1 vertebrae strongly correlated with each other. C2 showed the highest VBQ value [Median (range) 2.33 (1.33, 4.23)] and T1 showed the lowest VBQ value [Median (range) 1.64 (0.81, 3.88)]. There was significant weak to moderate negative correlations between and VBQ Scores for all levels [C2: p < 0.001; C3: p < 0.001; C4: p < 0.001; C5: p < 0.004; C6: p < 0.001; C7: p < 0.025; T1: p < 0.001].

CONCLUSION

Our results indicate that cervical VBQ scores may be insufficient in the estimation of BMDs, which might limit their clinical application. Additional studies are recommended to determine the utility of VBQ and QCT BMD to evaluate their potential use as bone status markers.

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

OBJECTIVES

We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners.

METHODS

We enrolled 402 patients who underwent noncontrast CT examinations, including L1-L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMDDL and TBSDL were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMDDL and BMD, and TBSDL and TBS. The diagnostic performance of BMDDL for osteopenia/osteoporosis and that of TBSDL for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis.

RESULTS

BMDDL and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBSDL and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMDDL for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBSDL for identifying patients with bone microarchitecture impairment were 73% for all values.

CONCLUSIONS

The BMDDL and TBSDL derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.

The impact of motion induced artifacts in the evaluation of HR-pQCT scans of the scaphoid bone: an assessment of inter- and intraobserver variability and quantitative parameters

Background

In-vivo high-resolution peripheral quantitative computed tomography (HR-pQCT) has high potential in scaphoid bone pathologies' scientific and clinical fields. The manufacturer's visual grading scale (VGS) classifies motion artifacts and divides scans into five quality grades ranging from grade 1 (good quality) to grade 5 (poor quality). This prospective study aimed to investigate the feasibility of the VGS and the influence of image quality on bone density and microarchitecture parameters for the scaphoid bone.

Methods

Within one year, twenty-two patients with scaphoid fractures received up to six scans of their fractured and contralateral wrist (each consisting of three stacks) using second-generation HR-pQCT (total 256 scans). Three experienced observers graded each stack following the visual grading system, and inter- and intraobserver variability were assessed. The contralateral uninjured scaphoids were then compared pairwise within each patient to high-quality grade 1 scans to determine the influence of image quality on density and microarchitecture parameters.

Results

Inter- and intraobserver variability among the three observers significantly revealed fair to moderate agreement, P<0.001 and P<0.05, respectively. Bone volume (BV) fraction tended to increase with poorer image quality but did not exceed four percent. Trabecular bone mineral density (Tb.BMD) decreased with poorer image quality but did not exceed five percent. Trabecular number and trabecular thickness significantly increased by 15.5% and 6.8% at grade five (P<0.001), respectively, and trabecular separation significantly decreased by 13.7% at grade five (P<0.001).

Conclusions

This study revealed a considerable influence of motion on bone morphometry parameters of the scaphoid. Therefore, high image quality must be a central point in studies focusing on the histomorphometry of small objects. The high inter- and intraobserver variability limit the VGS. Future research may focus on other grading systems or automated techniques leading to more consistent and reproducible results. Currently, the use of microarchitectural analysis should be limited to cases without motion artefacts or, at most low graded motion artefacts.

Identification of Bone Mineral Density Deficit Using L1 Trabecular Attenuation by Opportunistic Multidetector CT Scan in Adult Patients

BACKGROUND

Multidetector computer tomography (CT) has been used to diagnose pathologies such as osteoporosis via opportunistic screening, where the assessment of the bone structure and the measurement of bone mineral density (BMD) are of great relevance.

PURPOSE

To construct reference BMD values based on the measurement of the attenuation of the L1 vertebral body by multidetector CT scan (in the soft tissue and bone windows) in adult patients and to establish normative ranges by sex and age of BMD values.

MATERIALS AND METHODS

A retrospective cross-sectional study of 5080 patients who underwent multidetector CT scan between January and December 2021. Adult patients (≥18 years) with non-contrast multidetector CT scan of the abdomen or thorax-abdomen at a voltage 120 kV. The attenuation of the L1 vertebral body in Hounsfield units (HU) in both windows were compared using the Mann-Whitney U-test with α = 0.05. Additionally, the quartiles of the BMD were constructed (in both windows) grouped by sex and age.

RESULTS

Only 454 (51.30 ± 15.89 years, 243 women) patients met the inclusion criteria. There is no difference in BMD values between windows (soft tissue: 163.90 ± 57.13, bone: 161.86 ± 55.80, p = 0.625), mean L1 attenuation decreased linearly with age at a rate of 2 HU per year, and the presence of BMD deficit among patients was high; 152 of 454 (33.48%) patients presented BMD values suggestive of osteoporosis, and of these, approximately half 70 of 454 (15.42%) corresponded to patients with BMD values suggestive of a high risk of osteoporotic fracture.

CONCLUSIONS

From clinical practice, the bone mineral density (BMD) of a patient in either window below the first quartile for age- and sex-matched peers suggests a deficit in BMD that cannot be ignored and requires clinical management that enables identification of the etiology, its evolution, and the consequences of this alteration.

What Is the Diagnostic Performance of Conventional Radiographs and Clinical Reassessment Compared With HR-pQCT Scaphoid Fracture Diagnosis?

BACKGROUND

Conventional radiographs and clinical reassessment are considered guides in managing clinically suspected scaphoid fractures. This is a unique study as it assessed the value of conventional radiographs and clinical reassessment in a cohort of patients, all of whom underwent additional imaging, regardless of the outcome of conventional radiographs and clinical reassessment.

QUESTIONS/PURPOSES

(1) What is the diagnostic performance of conventional radiographs in patients with a clinically suspected scaphoid fracture compared with high-resolution peripheral quantitative CT (HR-pQCT)? (2) What is the diagnostic performance of clinical reassessment in patients with a clinically suspected scaphoid fracture compared with HR-pQCT? (3) What is the diagnostic performance of conventional radiographs and clinical reassessment combined compared with HR-pQCT?

METHODS

Between December 2017 and October 2018, 162 patients with a clinically suspected scaphoid fracture presented to the emergency department (ED). Forty-six patients were excluded and another 25 were not willing or able to participate, which resulted in 91 included patients. All patients underwent conventional radiography in the ED and clinical reassessment 7 to 14 days later, together with CT and HR-pQCT. The diagnostic performance characteristics and accuracy of conventional radiographs and clinical reassessment were compared with those of HR-pQCT for the diagnosis of fractures since this was proven to be superior to CT scaphoid fracture detection. The cohort included 45 men and 46 women with a median (IQR) age of 52 years (29 to 67). Twenty-four patients with a median age of 44 years (35 to 65) were diagnosed with a scaphoid fracture on HR-pQCT.

RESULTS

When compared with HR-pQCT, conventional radiographs alone had a sensitivity of 67% (95% CI 45% to 84%), specificity of 85% (95% CI 74% to 93%), positive predictive value (PPV) of 62% (95% CI 46% to 75%), negative predictive value (NPV) of 88% (95% CI 80% to 93%), and a positive and negative likelihood ratio (LR) of 4.5 (95% CI 2.4 to 8.5) and 0.4 (95% CI 0.2 to 0.7), respectively. Compared with HR-pQCT, clinical reassessment alone resulted in a sensitivity of 58% (95% CI 37% to 78%), specificity of 42% (95% CI 30% to 54%), PPV of 26% (95% CI 19% to 35%), NPV of 74% (95% CI 62% to 83%), as well as a positive and negative LR of 1.0 (95% CI 0.7 to 1.5) and 1.0 (95% CI 0.6 to 1.7), respectively. Combining clinical examination with conventional radiography produced a sensitivity of 50% (95% CI 29% to 71%), specificity of 91% (95% CI 82% to 97%), PPV of 67% (95% CI 46% to 83%), NPV of 84% (95% CI 77% to 88%), as well as a positive and negative LR of 5.6 (95% CI 2.4 to 13.2) and 0.6 (95% CI 0.4 to 0.8), respectively.

CONCLUSION

The accuracy of conventional radiographs (80% compared with HR-pQCT) and clinical reassessment (46% compared with HR-pQCT) indicate that the value of clinical reassessment is limited in diagnosing scaphoid fractures and cannot be considered directive in managing scaphoid fractures. The combination of conventional radiographs and clinical reassessment does not increase the accuracy of these diagnostic tests compared with the accuracy of conventional radiographs alone and is therefore also limited in diagnosing scaphoid fractures.

LEVEL OF EVIDENCE

Level II, diagnostic study.

Correlation of Lisfranc Injuries With Regional Bone Density

Lisfranc injuries present a challenge due to their ubiquity though frequent missed diagnoses. A paucity of data exists associating the contribution of bone density to injury type. This investigation compares the regional bone density between Lisfranc injury types using computed-tomography (CT)-derived Hounsfield units. A retrospective chart review identified patients with gross ligamentous and avulsion-type Lisfranc injuries determined by CT examination of the second metatarsal base and medial cuneiform. Regional bone density was assessed by averaging the Hounsfield units of the first metatarsal base, navicular, cuboid, calcaneus, and talus between 2 reviewers. Density was compared between injury type, isolated concomitant forefoot, and mid/hindfoot fractures. One hundred thirty-four patients were separated into avulsion (n = 85) and ligamentous (n = 49) groups. No statistically significant difference in patient body mass index, age, smoking status, or Quenu and Kuss injury pattern was observed between groups. The regional bone density of the cuboid (p = .03) and talus (p = .04) was greater in the ligamentous group. Lower extremity concomitant mid/hindfoot fracture patients exhibited greater regional bone density in the ligamentous group in all assessed bones (p ≤ .04) except the calcaneus. Ligamentous injuries of the Lisfranc complex are associated with increased regional bone density among patients sustaining concomitant mid/hindfoot fractures. This study expands the utility of regional bone density analysis in foot and ankle trauma.

The impact of obesity on the accuracy of DXA BMD for DXA-equivalent BMD estimation

INTRODUCTION

As the radiomics technique using texture features in CT is adopted for accessing DXA-equivalent bone mineral density (BMD), this study aims to compare BMD by DXA and predicted BMD to investigate the impact of obesity and central obesity in general patients.

MATERIALS AND METHODS

A total of 710 cases (621 patients) obtained from May 6, 2012, to June 30, 2021, were used in the study. We focused both their abdomen & pelvis CT's first lumbar vertebrae axial cuts to predict estimated BMD and bone mineral content (BMC). In each patient's CT, we extracted the largest trabecular region of the L1 vertebral body as a region of interest (ROI) using the gray-level co-occurrence matrices (GLCM) technique, and linear regression was applied to predict the indices. Cases were divided by central obesity/overall obesity and normal group by body mass index (BMI), waist circumference (WC), or index of central obesity (ICO) standard.

RESULTS

The coefficients were all above 0.73, respectively. P-values from ICO were over 0.05 when the measures were Hip BMD and Hip BMC. In contrast, those from ICO were 0.0131 and 0.0351 when the measures were L1 BMD and L1 BMC, respectively, which show a difference between the two groups.

CONCLUSIONS

The CT HU texture analysis method was an effective and economical method for measuring estimated BMD and BMC and evaluating the impact of obesity. We found that central obesity especially exerted an effect on the disturbance of the clinical BMD measurements since groups were significantly different under the ICO standard.

Radiographic Cortical Thickness Index Predicts Fragility Fracture in Gaucher Disease

Background Patients with Gaucher disease (GD) have a high risk of fragility fractures. Routine evaluation of bone involvement in these patients includes radiography and repeated dual-energy x-ray absorptiometry (DXA). However, osteonecrosis and bone fracture may affect the accuracy of DXA. Purpose To assess the utility of DXA and radiographic femoral cortical thickness measurements as predictors of fragility fracture in patients with GD with long-term follow-up (up to 30 years). Materials and Methods Patients with GD age 16 years and older with a detailed medical history, at least one bone image (DXA and/or radiographs), and minimum 2 years follow-up were retrospectively identified using three merged UK-based registries (Gaucherite study, enrollment 2015-2017; Clinical Bone Registry, enrollment 2003-2006; and Mortality Registry, enrollment 1993-2019). Cortical thickness index (CTI) and canal-to-calcar ratio (CCR) were measured by two independent observers, and inter- and intraobserver reliability was calculated. The fracture-predictive value of DXA, CTI, CCR, and cutoff values were calculated using receiver operating characteristic curves. Statistical differences were assessed using univariable and multivariable analysis. Results Bone imaging in 247 patients (123 men, 124 women; baseline median age, 39 years; IQR, 27-50 years) was reviewed. The median follow-up period was 11 years (IQR, 7-19 years; range, 2-30 years). Thirty-five patients had fractures before or at first bone imaging, 23 patients had fractures after first bone imaging, and 189 patients remained fracture-free. Inter- and intraobserver reproducibility for CTI/CCR measurements was substantial (range, 0.96-0.98). In the 212 patients with no baseline fracture, CTI (cutoff, ≤0.50) predicted future fractures with higher sensitivity and specificity (area under the receiver operating characteristic curve [AUC], 0.96; 95% CI: 0.84, 0.99; sensitivity, 92%; specificity, 96%) than DXA T-score at total hip (AUC, 0.78; 95% CI: 0.51, 0.91; sensitivity, 64%; specificity, 93%), femoral neck (AUC, 0.73; 95% CI: 0.50, 0.86; sensitivity, 64%; specificity, 73%), lumbar spine (AUC, 0.69; 95% CI: 0.49, 0.82; sensitivity, 57%; specificity, 63%), and forearm (AUC, 0.78; 95% CI: 0.59, 0.89; sensitivity, 70%; specificity, 70%). Conclusion Radiographic cortical thickness index of 0.50 or less was a reliable independent predictor of fracture risk in Gaucher disease. Clinical trial registration no. NCT03240653 © RSNA, 2022 Supplemental material is available for this article.

Vertebral Bone Quality Score Independently Predicts Proximal Junctional Kyphosis and/or Failure After Adult Spinal Deformity Surgery

BACKGROUND

Proximal junctional kyphosis (PJK) and proximal junctional failure (PJF) can be catastrophic complications associated with adult spinal deformity (ASD) surgery. These complications are markedly influenced by osteoporosis, leading to additional vertebral fracture and pedicle screw loosening. The MRI-based vertebral bone quality score (VBQ) is a newly developed tool that can be used to assess bone quality.

OBJECTIVE

To investigate the utility of the VBQ score in predicting PJK and/or PJF (PJF/PJK) after ASD correction.

METHODS

We conducted a retrospective chart review to identify patients age ≥50 years who had received ASD surgery of 5 or more thoracolumbar levels. Demographic, spinopelvic parameters, and procedure-related variables were collected. Each patient's VBQ score was calculated using preoperative T1-weighted MRI. Univariate analysis and multivariate logistic regression were performed to determine potential risk factors of PJK/PJF. Receiver operating characteristic analysis and area-under-the-curve values were generated for prediction of PJK/PJF.

RESULTS

A total of 116 patients were included (mean age, 64.1 ± 6.8 years). Among them, 34 patients (29.3%) developed PJK/PJF. Mean VBQ scores were 3.13 ± 0.46 for patients with PJK/PJF and 2.46 ± 0.49 for patients without, which was significantly different between the 2 groups ( P < .001). On multivariate analysis, VBQ score was the only significant predictor of PJK/PJF (odds ratio = 1.745, 95% CI = 1.558-1.953, P < .001), with a predictive accuracy of 94.3%.

CONCLUSION

In patients undergoing ASD correction, higher VBQ was independently associated with PJK/PJF occurrence. Measurement of VBQ score on preoperative MRI may be a useful adjunct to ASD surgery planning.

Opportunistic osteoporosis screening using chest CT with artificial intelligence

Osteoporosis has a high incidence and a low detection rate. If it is not detected in time, it will cause osteoporotic fracture and other serious consequences. This study showed that the attenuation values of vertebrae on chest CT could be used for opportunistic screening of osteoporosis. This will be beneficial to improve the detection rate of osteoporosis and reduce the incidence of adverse events caused by osteoporosis.

INTRODUCTION

To explore the value of the attenuation values of all thoracic vertebrae and the first lumbar vertebra measured by artificial intelligence on non-enhanced chest CT to do osteoporosis screening.

METHODS

On base of images of chest CT, using artificial intelligence (AI) to measure the attenuation values (HU) of all thoracic and the first vertebrae of patients who underwent CT examination for lung cancer screening and dual-energy X-ray absorptiometry (DXA) examination during the same period. The patients were divided into three groups: normal group, osteopenia group, and osteoporosis group according to the results of DXA. Clinical baseline data and attenuation values were compared among the three groups. The correlation between attenuation values and BMD values was analyzed, and the predictive ability and diagnostic efficacy of attenuation values of thoracic and first lumbar vertebrae on osteopenia or osteoporosis risk were further evaluated.

RESULTS

CT values of each thoracic vertebrae and the first lumbar vertebrae decreased with age, especially in menopausal women and presented high predictive ability and diagnostic efficacy for osteopenia or osteoporosis. After clinical data correction, with every 10 HU increase of CT values, the risk of osteopenia or osteoporosis decreased by 32 ~ 44% and 61 ~ 80%, respectively. And the combined diagnostic efficacy of all thoracic vertebrae was higher than that of a single vertebra. The AUC of recognizing osteopenia or osteoporosis from normal group was 0.831and 0.972, respectively.

CONCLUSIONS

The routine chest CT with AI is of great value in opportunistic screening for osteopenia or osteoporosis, which can quickly screen the population at high risk of osteoporosis without increasing radiation dose, thus reducing the incidence of osteoporotic fracture.

The feasibility study of XACT imaging for characterizing osteoporosis

BACKGROUND

Osteoporosis is a progressive bone disease that is characterized by a decrease in bone mass and the deterioration in bone microarchitecture, which might be related to age and space travel. An unmet need exists for the development of novel imaging technologies to characterize osteoporosis.

PURPOSE

The purpose of our study is to investigate the feasibility of X-ray-induced acoustic computed tomography (XACT) imaging for osteoporosis detection.

METHODS

An in-house simulation workflow was developed to assess the ability of XACT for osteoporosis detection. To evaluate this simulation workflow, a three-dimensional digital bone phantom for XACT imaging was created by a series of two-dimensional micro-computed tomography (micro-CT) slices of normal and osteoporotic bones in mice. In XACT imaging, the initial acoustic pressure rise caused by the X-ray induce acoustic (XA) effect is proportional to bone density. First, region growing was deployed for image segmentation of different materials inside the bone. Then k-wave simulations were deployed to model XA wave propagation, attenuation, and detection. Finally, the time-varying pressure signals detected at each transducer location were used to reconstruct the XACT image with a time-reversal reconstruction algorithm.

RESULTS

Through the simulated XACT images, cortical porosity has been calculated, and XA signal spectra slopes have been analyzed for the detection of osteoporosis. The results have demonstrated that osteoporotic bones have lower bone mineral density and higher spectra slopes. These findings from XACT images were in good agreement with porosity calculation from micro-CT images.

CONCLUSION

This work explores the feasibility of using XACT imaging as a new imaging tool for Osteoporosis detection. Considering that acoustic signals are generated by X-ray absorption, XACT imaging can be combined with traditional X-ray imaging that holds potential for clinical management of osteoporosis and other bone diseases.

Association between obstructive sleep apnea syndrome and bone mineral density in adult orthodontic populations

OBJECTIVE

To determine the association between obstructive sleep apnea syndrome (OSAS) and predicted bone mineral density (BMD) in adults presenting for orthodontic treatment.

METHODS

This retrospective cross-sectional study included 38 adults divided into OSAS and non-OSAS groups. Using pre-treatment CBCT images, radiographic density (RD) of left and right lateral regions of the 1st cervical vertebrae and dens of the 2nd cervical vertebrae were measured as an indicator for BMD.

RESULTS

When controlling for age, sex, and BMI, the mean RD was significantly lower in the OSAS group compared to the non-OSAS group (left CV1: 36.69 ± 84.50 vs. 81.67 ± 93.25 Hounsfield Units [HU], respectively, p = 0.031; right CV1: 30.59 ± 81.18 vs. 74.26 ± 91.81 HU, p = 0.045; dens: 159.25 ± 115.96 vs. 223.94 ± 106.09 HU, p = 0.038).

CONCLUSION

Adults with OSAS have lower values for predicted BMD than those without OSAS.