Opportunistic Screening Using Low-Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study

Prevalence rate of primary osteoporosis in China: a meta-analysis

BACKGROUND

Primary osteoporosis (POP) is recognized as a "silent disease" and often ignored. This meta-analysis aimed to determine the prevalence of POP in the Chinese population over the past 20 years to raise awareness of the disease's epidemiology, which is hoped to help prevent and treat the condition better.

METHODS

Eight English and three Chinese language databases were searched systematically from January 2002 to December 2023. Relevant data were analysed using Stata 16.0. Meta-regression and subgroup analyses were performed to explore causes of heterogeneity. A funnel plot was further drawn in combination with Egger and Begg tests to determine publication bias.

RESULTS

A total of 45 studies (241,813 participants) were included. The meta-analysis revealed that the overall prevalence of POP in the Chinese population was 18.2% (95% CI: 14.7-21.7%), showing a positive correlation with age. Specifically, prevalence rates were 23.4% (18.3-28.5%) in women and 11.5% (9.1-13.9%) in men. A notable increase was observed within the span of 20 years (16.9% before 2010 and 20.3% in 2011-2020). Notably, regional variations were observed, with southern China reporting a lower prevalence of 16.4% compared to 20.2% in northern China. Meta-regression suggested that sample size significantly influenced the estimation of point prevalence (P = 0.037).

CONCLUSIONS

Over the past two decades, there has been an increase in the prevalence of POP within the Chinese population. The growing prevalence of older individuals and women further highlights the urgency for tailored disease prevention and control measures.

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

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

PURPOSE/INTRODUCTION

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

METHOD

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

RESULTS

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

CONCLUSION

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Menopause modified the association of blood pressure with osteoporosis among gender: a large-scale cross-sectional study

Purpose

This study aimed to assess the potential association between blood pressure and osteoporosis in a rural population with limited resources. Existing evidence on this association is limited, particularly in such settings.

Methods

Data from 7,689 participants in the Henan Rural Cohort study were analyzed. Four blood pressure indicators [systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and pulse pressure (PP)] were measured. The logistic regression model and restricted cubic spline plots were used to assess the relationship between blood pressure indicators and osteoporosis prevalence.

Results

Positive trends were noted between blood pressure indicators and osteoporosis prevalence in the entire group and women (P trend < 0.05 for SBP, MAP, and PP). Women with higher SBP and PP exhibited elevated odds of osteoporosis compared with those with the lowest SBP and PP (ORs ranging from 1.15 to 1.5 for SBP and 1.06 to 1.83 for PP). No such associations were found in men. These relationships were only evident in postmenopausal women. Dose-response analysis confirmed these findings. Excluding participants taking hypertension medication did not alter the results.

Conclusion

In resource-limited settings, higher SBP and PP are associated with the increased prevalence of osteoporosis in women, potentially influenced by menopause-related factors. This indicates that potential gender-based differences and social inequalities may affect bone health.

Clinical trial registration

The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699) http://www.chictr.org.cn/showproj.aspx?proj=11375.

Visual scoring of osteoporosis on low-dose CT in lung cancer screening population

OBJECTIVES

The risk factors for lung cancer screening eligibility, age as well as smoking history, are also present for osteoporosis. This study aims to develop a visual scoring system to identify osteoporosis that can be applied to low-dose CT scans obtained for lung cancer screening.

MATERIALS AND METHODS

We retrospectively reviewed 1000 prospectively enrolled participants in the lung cancer screening program at the Mount Sinai Hospital. Optimal window width and level settings for the visual assessment were chosen based on a previously described approach. Visual scoring of osteoporosis and automated measurement using dedicated software were compared. Inter-reader agreement was conducted using six readers with different levels of experience who independently visually assessed 30 CT scans.

RESULTS

Based on previously validated formulas for choosing window and level settings, we chose osteoporosis settings of Width = 230 and Level = 80. Of the 1000 participants, automated measurement was successfully performed on 774 (77.4 %). Among these, 138 (17.8 %) had osteoporosis. There was a significant correlation between the automated measurement and the visual score categories for osteoporosis (Kendall's Tau = -0.64, p < 0.0001; Spearman's rho = -0.77, p < 0.0001). We also found substantial to excellent inter-reader agreement on the osteoporosis classification among the 6 radiologists (Fleiss κ = 0.91).

CONCLUSIONS

Our study shows that a simple approach of applying specific window width and level settings to already reconstructed sagittal images obtained in the context of low-dose CT screening for lung cancer is highly feasible and useful in identifying osteoporosis.

Fully automated deep learning system for osteoporosis screening using chest computed tomography images

Background

Osteoporosis, a disease stemming from bone metabolism irregularities, affects approximately 200 million people worldwide. Timely detection of osteoporosis is pivotal in grappling with this public health challenge. Deep learning (DL), emerging as a promising methodology in the field of medical imaging, holds considerable potential for the assessment of bone mineral density (BMD). This study aimed to propose an automated DL framework for BMD assessment that integrates localization, segmentation, and ternary classification using various dominant convolutional neural networks (CNNs).

Methods

In this retrospective study, a cohort of 2,274 patients underwent chest computed tomography (CT) was enrolled from January 2022 to June 2023 for the development of the integrated DL system. The study unfolded in 2 phases. Initially, 1,025 patients were selected based on specific criteria to develop an automated segmentation model, utilizing 2 VB-Net networks. Subsequently, a distinct cohort of 902 patients was employed for the development and testing of classification models for BMD assessment. Then, 3 distinct DL network architectures, specifically DenseNet, ResNet-18, and ResNet-50, were applied to formulate the 3-classification BMD assessment model. The performance of both phases was evaluated using an independent test set consisting of 347 individuals. Segmentation performance was evaluated using the Dice similarity coefficient; classification performance was appraised using the receiver operating characteristic (ROC) curve. Furthermore, metrics such as the area under the curve (AUC), accuracy, and precision were meticulously calculated.

Results

In the first stage, the automatic segmentation model demonstrated excellent segmentation performance, with mean Dice surpassing 0.93 in the independent test set. In the second stage, both the DenseNet and ResNet-18 demonstrated excellent diagnostic performance in detecting bone status. For osteoporosis, and osteopenia, the AUCs were as follows: DenseNet achieved 0.94 [95% confidence interval (CI): 0.91-0.97], and 0.91 (95% CI: 0.87-0.94), respectively; ResNet-18 attained 0.96 (95% CI: 0.92-0.98), and 0.91 (95% CI: 0.87-0.94), respectively. However, the ResNet-50 model exhibited suboptimal diagnostic performance for osteopenia, with an AUC value of only 0.76 (95% CI: 0.69-0.80). Alterations in tube voltage had a more pronounced impact on the performance of the DenseNet. In the independent test set with tube voltage at 100 kVp images, the accuracy and precision of DenseNet decreased on average by approximately 14.29% and 18.82%, respectively, whereas the accuracy and precision of ResNet-18 decreased by about 8.33% and 7.14%, respectively.

Conclusions

The state-of-the-art DL framework model offers an effective and efficient approach for opportunistic osteoporosis screening using chest CT, without incurring additional costs or radiation exposure.

Differences in Vertebral Morphology and bone Mineral Density between Grade 1 Vertebral Fracture and Non-Fractured Participants in the Chinese Population

PURPOSE

To investigate the difference in vertebral morphology and bone mineral density (BMD) between grade 1 VFs and non-fractured participants in the Chinese population to shed light on the clinical significance of grade 1 VFs from various perspectives.

METHODS

This retrospective cohort study included patients who received a chest low-dose computed tomography (LDCT) scan for health examination and visited the First Affiliated Hospital of Zhengzhou University, Henan, China, from October 2019 to August 2022. Data were analyzed from March 2023 to July 2023. The main outcome of this study was the difference in morphological parameters and BMD between grade 1 VFs and non-fractured participants. The prevalence of grade 1 VFs in China populations was calculated. The difference in BMD of three fracture types in the Grade 1 group was also evaluated.

RESULTS

A total of 3652 participants (1799 males, 54.85 ± 9.02 years, range, 40-92 years; 1853 females, 56.00 ± 9.08 years, range, 40-93 years) were included. The prevalence of grade 2 and 3 increase with age. The prevalence of grade 1 VFs gradually increases ≤ 50y to 60-69y group, but there is a decrease in the ≥ 70 years male group (6.6%) and a rise in the female group (25.5%). There was no significant statistical difference observed in vertebral shape indices (VSI) and BMD between the Grade 1 group and the no-fractured group aged < 50 years old except the wedge index in male. The biconcavity index did not differ between the non-fractured group and the Grade 1 group in men aged 50-59 years, whereas a significant statistical difference was observed in women. Additionally, the results of BMD were consistent with these findings. For the 40-59 years age group, there were significant differences between the compression deformity group and the other groups.

CONCLUSIONS

The grade 1 group had higher VSI and lower BMD than the non-fractured group, suggesting an association between the Grade 1 group and osteoporosis in individuals aged over 50 for women and over 60 for men. Different fracture types have significant variations in BMD among middle-aged people. The prevalence of grade 1 VFs exhibits an age-related increase in both genders, with opposite trends observed between older males and females. We suggested VSI can aid physicians in the diagnosis of grade 1 VFs.

Cost-Effectiveness of Artificial Intelligence-Based Opportunistic Compression Fracture Screening of Existing Radiographs

PURPOSE

Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software.

METHODS

A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices.

RESULTS

In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened.

CONCLUSIONS

Artificial intelligence-based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective.

Reference Values for Paravertebral Muscle Size and Myosteatosis in Chinese Adults, a Nationwide Multicenter Study

RATIONALE AND OBJECTIVES

The paravertebral muscles, characterized by their susceptibility to severe size loss and fat infiltration in old age, lack established reference values for age-related variations in muscle parameters. This study aims to fill this gap by establishing reference values for paravertebral muscles in a Chinese adult population.

MATERIALS AND METHODS

This cross-sectional study utilized the baseline data from the prospective cohort China Action on Spine and Hip (CASH). A total of 4305 community-dwelling participants aged 21-80 years in China were recruited between 2013 and 2017. Pregnant women, individuals with metal implants, limited mobility or diseases/conditions (spinal tumor, infection, etc.) affecting lumbar vertebra were excluded from the study. Psoas and paraspinal muscles were measured in quantitative computed tomography (QCT) images at the L3 and L5 levels using Osirix software. Age-related reference values for muscle area, density, and fat fraction were constructed as percentile charts using the lambda-mu-sigma (LMS) method.

RESULTS

The paravertebral muscles exhibited an age-related decline in muscle area and density, coupled with an increase in muscle fat fraction. Between the ages of 25 and 75, the reductions in psoas and paraspinal muscle cross-sectional area at the L3 level were - 0.47%/yr and - 0.53%/yr in men, and - 0.19%/yr and - 0.23%/yr in women, respectively. Notably, accelerated muscle loss was observed during menopause and postmenopause in women (45-75 years) and intermittently during middle and old age in men (35-55 and 60-75 years). Besides, the age-related decreases in PSMA, PMA, and PSMD and the increases in PSMFF were more pronounced in L5 than in L3 CONCLUSION: This study shows distinct patterns of accelerated muscle loss were identified in menopausal and postmenopausal women and in middle-aged and old men. The findings contribute valuable information for future investigations on paravertebral muscle loss and myosteatosis.

DeepmdQCT: A multitask network with domain invariant features and comprehensive attention mechanism for quantitative computer tomography diagnosis of osteoporosis

In the medical field, the application of machine learning technology in the automatic diagnosis and monitoring of osteoporosis often faces challenges related to domain adaptation in drug therapy research. The existing neural networks used for the diagnosis of osteoporosis may experience a decrease in model performance when applied to new data domains due to changes in radiation dose and equipment. To address this issue, in this study, we propose a new method for multi domain diagnostic and quantitative computed tomography (QCT) images, called DeepmdQCT. This method adopts a domain invariant feature strategy and integrates a comprehensive attention mechanism to guide the fusion of global and local features, effectively improving the diagnostic performance of multi domain CT images. We conducted experimental evaluations on a self-created OQCT dataset, and the results showed that for dose domain images, the average accuracy reached 91%, while for device domain images, the accuracy reached 90.5%. our method successfully estimated bone density values, with a fit of 0.95 to the gold standard. Our method not only achieved high accuracy in CT images in the dose and equipment fields, but also successfully estimated key bone density values, which is crucial for evaluating the effectiveness of osteoporosis drug treatment. In addition, we validated the effectiveness of our architecture in feature extraction using three publicly available datasets. We also encourage the application of the DeepmdQCT method to a wider range of medical image analysis fields to improve the performance of multi-domain images.

Burden of comorbidities: Osteoporotic vertebral fracture during non-small cell lung cancer - the BONE study

INTRODUCTION

Immunotherapy and targeted therapy have extended life expectancy in non-small cell lung cancer (NSCLC) patients, shifting it into a chronic condition with comorbidities, including osteoporosis. This study aims to evaluate the prevalence and incidence of osteoporotic vertebral fracture (OPVF) during NSCLC follow-up, identify risk factors of OPVF, and determine the impact on overall survival (OS).

METHODS

We performed a longitudinal single-center retrospective cohort study involving patients with histologically proven NSCLC of any stage. Chest-abdomen-pelvis computed tomography (CAP CT) at diagnosis and during follow-up were double-blind reviewed to determine OPVF site, count, type, time to incident OPVF, and trabecular volumetric bone density (TVBD). An institutional expert committee adjudicated discrepancies. Binary logistic regression was used to predict the occurrence of incident OPVF. OS was calculated using the Kaplan-Meier method.

RESULTS

We included 289 patients with a median follow-up of 29.7 months. OPVF prevalence was 10.7% at inclusion and 23.2% at the end of follow-up. Cumulative incidence was 12.5%, with an incidence rate of 4 per 100 patient-years. Median time to incident OPVF was 13 months (IQR: 6.7-21.2). Seven of the 36 patients with incident OPVF received denosumab or bisphosphonates. In multivariable analysis, independent risk factors for incident OPVF were BMI < 19 kg/m2 (OR: 5.62, 95%CI 1.84-17.20, p = 0.002), lower TVBD (OR: 0.982 per HU, 95%CI 0.97-0.99, p = 0.001) and corticosteroid use (OR: 4.77, 95%CI: 1.76-12.89, p = 0.001). OPVF was not significantly associated with OS.

CONCLUSIONS

Osteoporosis should be screened for in NSCLC patients. Thoracic oncologists must broaden the use of steroid-induced osteoporosis recommendations.

Association between bone mineral density and coronary plaque burden in patients with coronary artery disease: a cross-sectional study using quantitative computed tomography

PURPOSE

To evaluate the association between osteoporosis and coronary calcification and coronary plaque burden in patients with atherosclerosis and coronary artery disease (CAD).

METHODS

This study included 290 men and 177 postmenopausal women with angiography-confirmed atherosclerosis or CAD who underwent chest multidetector row computed tomography covering L1-L2 between September 2020 and October 2021. Quantitative computed tomography was used to measure the lumbar vertebra's bone mineral density (BMD). The coronary artery calcium score (CACS) and total coronary plaque burden were quantified using the Agatston and modified Gensini scores, respectively. Associations between BMD and CACS and modified Gensini scores were assessed using multivariate regression analysis. Lasso regression was used in model selection.

RESULTS

In men, BMD was inversely associated with CACS [ β  = -0.24; 95% confidence interval (CI), -0.35 to -0.13; P  < 0.001) and coronary artery calcification (CAC) presence [odds ratio (OR) = 0.71; 95% CI, 0.52-0.96; P  = 0.03) in the unadjusted model. After adjusting for age, modified Gensini score, prior percutaneous coronary intervention and hypertension, BMD was inversely associated with CACS ( β  = -0.11; 95% CI, -0.22 to -0.01; P  = 0.04). In postmenopausal women, BMD was inversely associated with CACS ( β  = -0.24; 95% CI, -0.39 to 0.10; P  < 0.001) and CAC presence (OR = 0.66; 95% CI, 0.47-0.92; P  = 0.01) in the unadjusted model but no other models ( P  > 0.05). In both sexes, BMD did not correlate with the modified Gensini score or CAD prevalence (all P  > 0.05).

CONCLUSION

In patients with coronary atherosclerosis and CAD, BMD of the lumbar vertebra correlated inversely with CACS in men but not postmenopausal women. Additionally, BMD did not correlate with the modified Gensini score in both sexes.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Assessment of whole-body and regional body fat using abdominal quantitative computed tomography in Chinese women and men

BACKGROUND

Being overweight or obese has become a serious public health concern, and accurate assessment of body composition is particularly important. More precise indicators of body fat composition include visceral adipose tissue (VAT) mass and total body fat percentage (TBF%). Study objectives included examining the relationships between abdominal fat mass, measured by quantitative computed tomography (QCT), and the whole-body and regional fat masses, measured by dual energy X-ray absorptiometry (DXA), as well as to derive equations for the prediction of TBF% using data obtained from multiple QCT slices.

METHODS

Whole-body and regional fat percentage were quantified using DXA in Chinese males (n = 68) and females (n = 71) between the ages of 24 and 88. All the participants also underwent abdominal QCT measurement, and their VAT mass and visceral fat volume (VFV) were assessed using QCT and DXA, respectively.

RESULTS

DXA-derived TBF% closely correlated with QCT abdominal fat percentage (r = 0.89-0.93 in men and 0.76-0.88 in women). Stepwise regression showed that single-slice QCT data were the best predictors of DXA-derived TBF%, DXA android fat percentage and DXA gynoid fat percentage. Cross-validation analysis showed that TBF% and android fat percentage could be accurately predicted using QCT data in both sexes. There were close correlations between QCT-derived and DXA-derived VFV (r = 0.97 in men and 0.93 in women).

CONCLUSION

Clinicians can assess the TBF% and android and gynoid fat percentages of Chinese women and men by analysing existing abdominal CT-derived data using the QCT technique.

Novel Calcium-Binding Peptide from Bovine Bone Collagen Hydrolysates and Its Potential Pro-Osteogenic Activity via Calcium-Sensing Receptor (CaSR)

SCOPE

This paper aims to explore the osteogenic activity and potential mechanism of the peptide-calcium chelate, and provides a theoretical basis for peptide-calcium chelates as functional foods to prevent or improve osteoporosis.

METHODS AND RESULTS

In this research, a novel peptide (Phe-Gly-Leu, FGL) with a high calcium-binding capacity is screened from bovine bone collagen hydrolysates (CPs), calcium binding sites of which mainly included carbonyl, amino and carboxyl groups. The FGL-Ca significantly enhances the osteogenic activity of MC3T3-E1 cells (survival rate, differentiation, and mineralization). The results of calcium fluorescence labeling and molecular docking show that FGL-Ca may activate calcium-sensing receptor (CaSR), leading to an increase in intracellular calcium concentration, then enhancing osteogenic activity of MC3T3-E1 cells. Further research found that FGL-Ca significantly promotes the mRNA and protein expression levels of CaSR, transforming growth factor β (TGF-β1), TGF-β-type II receptor (TβRII), Smad2, Smad3, osteocalcin (OCN), alkaline phosphatase (ALP), osteoprotegrin (OPG), and collagen type I (COLI). Subsequently, in the signal pathway intervention experiment, the expression levels of genes and proteins related to the TGF-β1/Smad2/3 signaling pathway that are promoted by FGL-Ca are found to decrease.

CONCLUSIONS

These results suggest that FGL-Ca may activate CaSR, increase intracellular calcium concentration, and activate TGF-β1/Smad2/3 signaling pathway, which may be one of the potential mechanisms for enhancing osteogenic activity.

Association between high-density lipoprotein cholesterol and lumbar bone mineral density in Chinese: a large cross-sectional study

BACKGROUND

The association between lipid and bone metabolism, particularly the role of high-density lipoprotein cholesterol (HDL-C) in regulating bone mineral density (BMD), is of significant interest. Despite numerous studies, findings on this relationship remain inconclusive, especially since evidence from large, sexually diverse Chinese populations is sparse. This study, therefore, investigates the correlation between HDL-C and lumbar BMD in people of different genders using extensive population-based data from physical examinations conducted in China.

METHODS

Data from a cross-sectional survey involving 20,351 individuals aged > = 20 years drawn from medical records of health check-ups at the Health Management Centre of the Henan Provincial People's Hospital formed the basis of this study. The primary objective was to determine the correlation between HDL-C levels and lumbar BMD across genders. The analysis methodology included demographic data analysis, one-way ANOVA, subgroup analyses, multifactorial regression equations, smoothed curve fitting, and threshold and saturation effect analyses.

RESULTS

Multifactorial regression analysis revealed a significant inverse relationship between HDL-C levels and lumbar BMD in both sexes, controlling for potential confounders (Male: β = -8.77, 95% CI -11.65 to -5.88, P < 0.001; Female: β = -4.77, 95% CI -8.63 to -0.90, P = 0.015). Subgroup and threshold saturation effect analyses indicated a stronger association in males, showing that increased HDL-C correlates with reduced lumbar BMD irrespective of age and body mass index (BMI). The most significant effect was observed in males with BMI > 28 kg/m2 and HDL-C > 1.45 mmol/L and in females with a BMI between 24 and 28 kg/m2.

CONCLUSION

Elevated HDL-C is associated with decreased bone mass, particularly in obese males. These findings indicate that individuals with high HDL-C levels should receive careful clinical monitoring to mitigate osteoporosis risk.

TRIAL REGISTRATION

The research protocol received ethics approval from the Ethics Committee at Beijing Jishuitan Hospital, in conformity with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are a contribution of the China Health Quantitative CT Big Data Research team, registered at clinicaltrials.gov (code: NCT03699228).

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

INTRODUCTION

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Fracture risk prediction in old Chinese people-a narrative review

With aging, the burden of osteoporotic fracture (OF) increases substantially, while China is expected to carry the greatest part in the future. The risk of fracture varies greatly across racial groups and geographic regions, and systematically organized evidence on the potential predictors for fracture risk is needed for Chinese. This review briefly introduces the epidemiology of OF and expands on the predictors and predictive tools for the risk of OF, as well as the challenges for their potential translation in the old Chinese population. There are regional differences of fracture incidence among China. The fracture incidences in Hong Kong and Taiwan have decreased in recent years, while it is still increasing in mainland China. Although the application of dual-energy X-ray absorptiometry (DXA) is limited among old Chinese in the mainland, bone mineral density (BMD) by DXA has a predictive value similar to that worldwide. Other non-DXA modalities, especially heel QUS, are helpful in assessing bone health. The fracture risk assessment tool (FRAX) has a good discrimination ability for OFs, especially the FRAX with BMD. And some clinical factors have added value to FRAX, which has been verified in old Chinese. In addition, although the application of the osteoporosis self-assessment tool for Asians (OSTA) in Chinese needs further validation, it may help identify high-risk populations in areas with limited resources. Moreover, the translation use of the muscle quality and genetic or serum biomarkers in fracture prediction needs further works. More applicable and targeted fracture risk predictors and tools are still needed for the old Chinese population.

Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images

Background

The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and their patterns.

Methods

The retrospective cohort comprised patients who visited our hospital from 2017-2018; the prospective cohort (control group) was consecutively recruited from the same hospital from February-June 2023. All patients had blunt chest trauma and underwent CT. Volumetric BMD of L1 vertebra was measured by using an AI software. Analyses were done by using BMD categorized as osteoporosis (<80 mg/cm3), osteopenia (80-120 mg/cm3), or normal (>120 mg/cm3). Pearson's χ2, Fisher's exact, or Kruskal-Wallis tests and Bonferroni correction were used for comparisons. Negative binomial, and logistic regression analyses were used to assess the associations and impacts of BMD status. Sensitivity analyses were also performed.

Findings

The retrospective cohort included 2,076 eligible patients, of whom 954 (46%) had normal BMD, 806 (38.8%) had osteopenia, and 316 (15.2%) had osteoporosis. After sex- and age-adjustment, osteoporosis was significantly associated with higher rib fracture rates, and a higher likelihood of fractures in ribs 4-7. Furthermore, both the osteopenia and osteoporosis groups demonstrated a significantly higher number of fractured ribs and fracture sites on ribs, with a higher likelihood of fractures in ribs 1-3, as well as flail chest. The prospective cohort included 205 eligible patients, of whom 92 (44.9%) had normal BMD, 74 (36.1%) had osteopenia, and 39 (19.0%) had osteoporosis. The findings observed within this cohort were in concurrence with those in the retrospective cohort.

Interpretation

Traumatic rib fractures are associated with decreased BMD. CT-AI can help to identify individuals who have decreased BMD and a greater rib fracture rate, along with their fracture patterns.

Biomechanical and Biological Assessment of Polyglycelrolsebacate-Coupled Implant with Shape Memory Effect for Treating Osteoporotic Fractures

Poly(glycerol sebacate) is a biocompatible elastomer that has gained increasing attention as a potential biomaterial for tissue engineering applications. In particular, PGS is capable of providing shape memory effects and allows for a free form, which can remember the original shape and obtain a temporary shape under melting point and then can recover its original shape at body temperature. Because these properties can easily produce customized shapes, PGS is being coupled with implants to offer improved fixation and maintenance of implants for fractures of osteoporosis bone. Herein, this study fabricated the OP implant with a PGS membrane and investigated the potential of this coupling. Material properties were characterized and compared with various PGS membranes to assess features such as control of curing temperature, curing time, and washing time. Based on the ISO 10993-5 standard, in vitro cell culture studies with C2C12 cells confirmed that the OP implant coupled with PGS membrane showed biocompatibility and biomechanical experiments indicated significantly increased pullout strength and maintenance. It is believed that this multifunctional OP implant will be useful for bone tissue engineering applications.

The burden of fracture in China from 1990 to 2019

PURPOSE

Osteoporosis is one of the most common clinical problems among the elderly population. China is one of the countries most threatened by osteoporosis and fragility fracture, because of its large population and aging population trends during recent decades. We aimed to estimate the disease burden of fracture from 1990 to 2019 in China.

METHODS

We performed a secondary analysis of fractures using detailed information for China from the Global Burden of Disease Study 2019. Fracture incidence and prevalence, rate of years lost to disability from fractures, and term secular trends in China from 1990 to 2019 were compared by sex, age, cause, and nature of fracture.

RESULTS

The numbers for incidence and prevalence of fracture and years lived with disability (YLDs) from fractures in China increased from 12.54 million, 28.35 million, and 1.71 million in 1990 to 21.27 million, 67.85 million, and 3.79 million in 2019, respectively, increases of 70%, 139%, and 122%, respectively. In 2019, falls was the leading cause of fractures, with an age-standardized incidence rate (ASIR) of 762 per 100 000 (95% uncertainty interval [UI] 629-906), an age-standardized prevalence rate (ASPR) of 1863 per 100 000 (95% UI 1663-2094), and an age-standardized YLD rate (ASYR) of 103 per 100 000 (95% UI 69-147). Fall-associated deaths and disability-adjusted life-years (DALYs) from low bone mineral density increased greatly during the most recent three decades. Fracture of patella, tibia or fibula, and ankle were the most frequent fracture types, with an ASYR of 116 per 100 000 (95% UI 75-169). Hip fracture had more incident cases in adults ≥ 60 years old, and was more frequent for females.

CONCLUSIONS

The burden from fractures has increased significantly since 1990 in China. Falls and road injuries are the main causes of the increase. The fall-associated health burden from osteoporosis needs to be prioritized, with longer-term commitment to its reduction required.

The age-related characteristics in bone microarchitecture, osteoclast distribution pattern, functional and transcriptomic alterations of BMSCs in mice

Deteriorated age-related bone loss is the hallmarks of skeletal aging. However, how the aging of bone marrow mesenchymal stem cells (BMSCs) and osteoclasts are linked to the bone microstructure degeneration is not yet very clear. In this study, the characteristics of age-related bone loss, distribution patterns of osteoclasts, functional and transcriptomic alterations of BMSCs, hub genes responsible for BMSCs senescence, were analyzed. Our study revealed an age-related declined trends in trabecular and cortical bones of femur, tibia and lumbar vertebra in mice, which was accompanied by a shift from the trabecular to cortical bones in osteoclasts. Additionally, middle-aged or aged mice exhibited remarkably reduced dynamic bone formation capacities, along with reversed osteogenic-adipogenic differentiation potentials in BMSCs. Finally, transcriptomic analysis indicated that aging-related signaling pathways were significantly activated in BMSCs from aged mice (e.g., cellular senescence, p53 signaling pathway, etc.). Also, weighted correlation network analysis (WGCNA) and venn diagram analysis based on our RNA-Seq data and GSE35956 dataset revealed the critical role of PTPN1 in BMSCs senescence. Targeted inhibition of PTP1B with AAV-Ptpn1-RNAi dramatically postponed age-related bone loss in middle-aged mice. Collectively, our study has uncovered the age-dependent cellular characteristics in BMSCs and osteoclasts underlying progressive bone loss with advancing age.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria

RATIONALE AND OBJECTIVES

Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool.

MATERIALS AND METHODS

We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV).

RESULTS

Our best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%.

CONCLUSION

Our model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Artificial Intelligence Applications for Osteoporosis Classification Using Computed Tomography

Osteoporosis, marked by low bone mineral density (BMD) and a high fracture risk, is a major health issue. Recent progress in medical imaging, especially CT scans, offers new ways of diagnosing and assessing osteoporosis. This review examines the use of AI analysis of CT scans to stratify BMD and diagnose osteoporosis. By summarizing the relevant studies, we aimed to assess the effectiveness, constraints, and potential impact of AI-based osteoporosis classification (severity) via CT. A systematic search of electronic databases (PubMed, MEDLINE, Web of Science, ClinicalTrials.gov) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 39 articles were retrieved from the databases, and the key findings were compiled and summarized, including the regions analyzed, the type of CT imaging, and their efficacy in predicting BMD compared with conventional DXA studies. Important considerations and limitations are also discussed. The overall reported accuracy, sensitivity, and specificity of AI in classifying osteoporosis using CT images ranged from 61.8% to 99.4%, 41.0% to 100.0%, and 31.0% to 100.0% respectively, with areas under the curve (AUCs) ranging from 0.582 to 0.994. While additional research is necessary to validate the clinical efficacy and reproducibility of these AI tools before incorporating them into routine clinical practice, these studies demonstrate the promising potential of using CT to opportunistically predict and classify osteoporosis without the need for DEXA.

Cost-Effectiveness of Osteoporosis Opportunistic Screening Using Computed Tomography in China

OBJECTIVES

Underutilization and insufficient availability of dual-energy X-ray absorptiometry (DXA) in diagnosing osteoporosis in China could be changed by adopting unindicated quantitative computed tomography. We aimed to assess the cost-effectiveness of quantitative computed tomography (QCT) as a screening tool for osteoporosis in China.

METHODS

A Markov microsimulation model was developed to assess the long-term costs and quality-adjusted life-years (QALYs) saved associated with 2 examinations as opportunistic screening for osteoporosis in a general population without prior histories of fracture. The diagnostic performance of both examinations was incorporated into the model. In lifetime modeling, opportunistically screened people may face the risk of experiencing hip, vertebral, and wrist fractures depending on their osteoporosis, age, and sex. Model parameters were informed by published literature.

RESULTS

The base-case result showed that QCT was associated with higher costs ($6054 vs $5883) and higher benefits (10.081 vs 10.071 QALYs) in comparison with DXA, making QCT a cost-effective option for opportunistic screening (incremental cost-effectiveness ratio of US $16 430/QALY). Screening with QCT led to fewer fractures over the lifetime simulation: for every 10 000 people screened, 129 fractures (32 hip, 78 vertebral, and 19 wrist fractures) could be avoided because of the early initiation of antiosteoporotic treatment.

CONCLUSIONS

Using QCT to screen people for osteoporosis is more cost-effective than standard practice in China, where access to DXA is minimal. This finding could support opportunistic osteoporosis screening using QCT in other countries with similar status.

Association between total cholesterol and lumbar bone density in Chinese: a study of physical examination data from 2018 to 2023

BACKGROUND

The impact of total cholesterol (TC) on lumbar bone mineral density (BMD) is a topic of interest. However, empirical evidence on this association from demographic surveys conducted in China is lacking. Therefore, this study aimed to examine the relationship between serum TC and lumbar BMD in a sample of 20,544 Chinese adults between the ages of 20 and 80 years over a period of 5 years, from February 2018 to February 2023. Thus, we investigated the effect of serum TC level on lumbar BMD and its relationship with bone reduction in a Chinese adult population.

METHODS

This cross-sectional study used data obtained from the Department of Health Management at Henan Provincial People's Hospital between February 2018 and February 2023. The aim of this study was to examine the correlation between serum TC and lumbar BMD in individuals of different sexes. The research methodology encompassed population description, analysis of stratification, single-factor and multiple-equation regression analyses, smooth curve fitting, and analysis of threshold and saturation effects. The R and EmpowerStats software packages were used for statistical analysis.

RESULTS

After adjusting for confounding variables, a multiple linear regression model revealed a significant correlation between TC and lumbar BMD in men. In subgroup analysis, serum TC was found to have a positive association with lumbar BMD in men, specifically those aged 45 years or older, with a body mass index (BMI) ranging from 24 to 28 kg/m2. A U-shaped correlation arose between serum TC and lumbar BMD was detected in women of different ages and BMI, the inflection point was 4.27 mmol/L for women aged ≥ 45 years and 4.35 mmol/L for women with a BMI of ≥ 28 kg/m2.

CONCLUSION

In this study, Chinese adults aged 20-80 years displayed different effects of serum TC on lumbar BMD in sex-specific populations. Therefore, monitoring BMI and serum TC levels in women of different ages could prevent osteoporosis and osteopenia.

TRIAL REGISTRATION

The research protocol was approved by the Ethics Committee of Beijing Jishuitan Hospital, in accordance with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are part of the China Health Quantitative CT Big Data Research team, which has been registered at clinicaltrials.gov (code: NCT03699228).

Feasibility analysis of low-dose CT with asynchronous quantitative computed tomography to assess vBMD

BACKGROUND

To explore the feasibility of low-dose computed tomography (LDCT) with asynchronous quantitative computed tomography (asynchronous QCT) for assessing the volumetric bone mineral density (vBMD).

METHODS

416 women patients, categorized into 4 groups, were included and underwent chest CT examinations combined with asynchronous QCT, and CT scanning dose protocols (LDCT or CDCT) were self-determined by the participants. Radiation dose estimations were retrieved from patient protocols, including volume CT dose index (CTDIvol) and dose-length-product (DLP), and then calculated effective dose (ED). Delimiting ED by 1.0 mSv, chest CT examinations were categorized into 2 groups, LDCT group and CDCT group. vBMD of T12-L2 was obtained by transferring the LDCT and CDCT images to the QCT workstation, without extra radiation.

RESULTS

There was no difference of vBMD among 4 age groups in LDCT group (P = 0.965), and no difference in CDCT group (P = 0.988). In LDCT group and CDCT group, vBMD was not correlated to mAs, CTDIvol and DLP (P > 0.05), respectively. Between LDCT group and CDCT group, there was no difference of vBMD (P ≥ 0.480), while differences of mAs, CTDIvol and DLP.

CONCLUSION

There was no difference of vBMD between LDCT group and CDCT group and vBMD was not correlated to mAs. While screening for diseases such as lung cancer and mediastinal lesions, LDCT combined with asynchronous QCT can be also used to assess vBMD simultaneously with no extra imaging equipment, patient visit time, radiation dose and no additional economic cost.

CT and MR for bone mineral density and trabecular bone score assessment in osteoporosis evaluation

Dual energy X-ray absorptiometry (DXA) is widely used modality for measuring bone mineral density (BMD). DXA is used to measure the quantitative areal BMD of bone, but has the disadvantage of not reflecting the bone architecture. To compensate for this disadvantage, trabecular bone score (TBS), a qualitative parameter of trabecular microarchitecture, is used. Meanwhile, there have been recent attempts to diagnose osteoporosis using the Hounsfield unit (HU) from CT and MR-based proton density fat fraction (PDFF) measurements. In our study, we aimed to find out the correlation between HU/PDFF and BMD/TBS, and whether osteoporosis can be diagnosed through HU/PDFF. Our study revealed that the HU value showed a moderate to good positive correlation with BMD and TBS. PDFF showed a fair negative correlation with BMD and TBS. In diagnosing osteopenia and osteoporosis, the HU value showed good performance, whereas the PDFF showed fair performance. In conclusion, both HU values and PDFF can play a role in predicting BMD and TBS. Both HU values and PDFF can be used to predict osteoporosis; further, CT is expected to show better results.

Smoking index and COPD duration as potential risk factors for development of osteoporosis in patients with non-small cell lung cancer - A retrospective case control study evaluated by CT Hounsfield unit

Objective

To investigate the effect of smoking index (calculated as number of cigarettes per day × smoking years) and chronic obstructive pulmonary disease (COPD) duration on osteoporosis (OP)evaluated by opportunistic chest CT in patients with non-small cell lung cancer (NSCLC).

Methods

A total of 101 patients diagnosed with NSCLC were included in our cohort study. Among them, 50 patients with a history of smoking and COPD were assigned to the experimental group, while 51 patients without a history of smoking and COPD were assigned to the control group. Hounsfield unit (HU) value was measured by conventional chest CT to investigate the bone mineral density; and the mean values of axial HU value in the upper, middle and lower parts of T4, T7, T10 and L1 vertebral bodies were measured as the study variables.

Results

There were no significant differences in gender, age, body mass index, type of lung cancer, clinical stage of lung cancer and comorbidities between the two groups (P = 0.938，P = 0.158，P = 0.722,P = 0.596,P = 0.813,P = 0.655). The overall mean HU values of T4, T7, T10, L1 in the experimental group were 116.60 ± 30.67, 110.56 ± 30.03, 109.18 (96.85-122.95), 94.63 (85.20-104.12) and 106.86 ± 22.26, respectively, which were significantly lower than those in the control group (189.55 ± 34.57, 174.54 ± 35.30, 172.73 (156.33-199.50), 158.20 (141.60-179.40) and 177.50 ± 33.49) (P ＜0.05). And in the experimental group, smoking index and COPD duration were significantly and negatively correlated with HU values (r = -0.627, -0.542, P ＜0.05, respectively).

Conclusion

Patients with NSCLC who have a history of smoking and COPD exhibit a notably lower HU value compared to the control groups. Additionally, it has been observed that the smoking index and duration of COPD may be influential factors affecting bone mineral density in NSCLC patients.

Using Artificial Intelligence to Diagnose Osteoporotic Vertebral Fractures on Plain Radiographs

Osteoporotic vertebral fracture (OVF) is a risk factor for morbidity and mortality in elderly population, and accurate diagnosis is important for improving treatment outcomes. OVF diagnosis suffers from high misdiagnosis and underdiagnosis rates, as well as high workload. Deep learning methods applied to plain radiographs, a simple, fast, and inexpensive examination, might solve this problem. We developed and validated a deep-learning-based vertebral fracture diagnostic system using area loss ratio, which assisted a multitasking network to perform skeletal position detection and segmentation and identify and grade vertebral fractures. As the training set and internal validation set, we used 11,397 plain radiographs from six community centers in Shanghai. For the external validation set, 1276 participants were recruited from the outpatient clinic of the Shanghai Sixth People's Hospital (1276 plain radiographs). Radiologists performed all X-ray images and used the Genant semiquantitative tool for fracture diagnosis and grading as the ground truth data. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to evaluate diagnostic performance. The AI_OVF_SH system demonstrated high accuracy and computational speed in skeletal position detection and segmentation. In the internal validation set, the accuracy, sensitivity, and specificity with the AI_OVF_SH model were 97.41%, 84.08%, and 97.25%, respectively, for all fractures. The sensitivity and specificity for moderate fractures were 88.55% and 99.74%, respectively, and for severe fractures, they were 92.30% and 99.92%. In the external validation set, the accuracy, sensitivity, and specificity for all fractures were 96.85%, 83.35%, and 94.70%, respectively. For moderate fractures, the sensitivity and specificity were 85.61% and 99.85%, respectively, and 93.46% and 99.92% for severe fractures. Therefore, the AI_OVF_SH system is an efficient tool to assist radiologists and clinicians to improve the diagnosing of vertebral fractures. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Characteristics of MRI‑based vertebral bone quality scores in elderly patients with vertebral fragility fractures

OBJECTIVE

To explore the characteristics of vertebral bone quality (VBQ) scores in patients with vertebral fragility fractures, including VBQ score and single-level VBQ score, and evaluate their effectiveness as predictors.

METHODS

The VBQ scores were measured using T1-weighted MRI images. VBQ scores were compared in patients with different times of previous fragility fractures. In addition, patients with fractures were matched for age and sex with patients without fractures, and VBQ scores were compared between the two groups. Finally, the predictive efficiency of VBQ scores for vertebral fragility fractures was analyzed by the receiver-operator curve (ROC).

RESULTS

The average VBQ score and single-level VBQ score in patients with fractures were 3.48 ± 0.56 and 3.60 ± 0.60 and no difference among patients with different times of previous fractures. As for the age- and sex-matched patients, fracture patients had higher VBQ scores (VBQ score: 3.48 ± 0.56 vs. 2.88 ± 0.40, p < 0.001; single-level VBQ score: 3.60 ± 0.60 vs. 2.95 ± 0.44, p < 0.001). The AUCs using the VBQ score and single-level VBQ score to predict fragility fractures were 0.815 and 0.817, respectively. The optimal thresholds of the VBQ score and single-level VBQ score for predicting fragility fractures were 3.22 and 3.16, respectively.

CONCLUSION

MRI‑based VBQ scores are important predictors of vertebral fragility fracture but have no predictive value for the recurrence of fractures in patients with a history of fragility fractures. The VBQ score of 3.22 and single-level VBQ score of 3.16 are optimal thresholds that can be used when using lumbar MRI scans to identify individuals at high risk for fragility fractures.

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.

The Effects of n-3 PUFA Supplementation on Bone Metabolism Markers and Body Bone Mineral Density in Adults: A Systematic Review and Meta-Analysis of RCTs

Supplemental n-3 polyunsaturated fatty acids (PUFA) on bone metabolism have yielded inconsistent results. This study aimed to examine the effects of n-3 PUFA supplementation on bone metabolism markers and bone mineral density through a meta-analysis of randomized controlled trials. A systematic literature search was conducted using the PubMed, Web of Science, and EBSCO databases, updated to 1 March 2023. The intervention effects were measured as standard mean differences (SMD) and mean differences (MD). Additionally, n-3 PUFA with the untreated control, placebo control, or lower-dose n-3 PUFA supplements were compared, respectively. Further, 19 randomized controlled trials (RCTs) (22 comparisons, n = 2546) showed that n-3 PUFA supplementation significantly increased blood n-3 PUFA (SMD: 2.612; 95% CI: 1.649 to 3.575). However, no significant effects were found on BMD, CTx-1, NTx-1, BAP, serum calcium, 25(OH)D, PTH, CRP, and IL-6. Subgroup analyses showed significant increases in femoral neck BMD in females (0.01, 95% CI: 0.01 to 0.02), people aged <60 years (0.01, 95% CI: 0.01 to 0.01), and those people in Eastern countries (0.02, 95% CI: 0.02 to 0.03), and for 25(OH)D in people aged ≥60 years (0.43, 95% CI: 0.11 to 0.74), treated with n-3 PUFA only (0.36, 95% CI: 0.06 to 0.66), and in studies lasting ≤6 months (0.29, 95% CI: 0.11 to 0.47). NTx-1 decreased in both genders (-9.66, 95% CI: -15.60 to -3.71), and serum calcium reduction was found in studies lasting >6 months (-0.19, 95% CI: -0.37 to -0.01). The present study demonstrated that n-3 PUFA supplementation might not have a significant effect on bone mineral density or bone metabolism markers, but have some potential benefits for younger postmenopausal subjects in the short term. Therefore, additional high-quality, long-term randomized controlled trials (RCTs) are warranted to fully elucidate the potential benefits of n-3 PUFA supplementation, as well as the combined supplementation of n-3 PUFA, on bone health.

Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening

BACKGROUND

Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology.

METHODS

The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model.

RESULTS

A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively.

CONCLUSIONS

It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.

Addressing gaps in osteoporosis screening in kuwait using opportunistic quantitative computer tomography (QCT): a retrospective study

Osteoporosis is a common skeletal disorder which is underdiagnosed and undertreated. Consequent fragility fractures are associated with high morbidity and mortality. Prevention of these fractures is possible by timely osteoporosis screening followed by timely therapeutic interventions when needed. Utilizing all available modalities such as bone density measurements on preexisting CT scans could help narrow the diagnostic gap.

PURPOSE

To demonstrate the feasibility and clinical utility of opportunistic osteoporosis screening in Kuwait using QCT, aiming to increase screening rates in a country with a relatively high prevalence of osteoporosis and an alarming trend of increasing incidence of fractures.

METHODS

At a tertiary referral center, all abdominal CT scans performed on females ≥60 years old between 12/2020 and 12/2021 were retrospectively utilized for asynchronous QCT acquisition. The average volumetric bone mineral density (vBMD) was calculated, and rates of osteoporosis (vBMD < 80 mg/cm³ calcium hydroxyapatite) and osteopenia (80-120 mg/cm³) were determined. CT images were reviewed to assess for the presence of vertebral fractures. For each patient, the electronic health record was reviewed for any previous DXA scans.

RESULTS

vBMD was calculated in 305 females ≥60 years old (mean [SD] 71 [8.7], range 60-93). Low bone mass was detected in 258 patients (84.6%); 148 (48.5%) met criteria for osteopenia and 110 (36.1%) for osteoporosis. Osteoporotic vertebral fractures were observed in 64 (21.0%) study participants. Only 73 patients (23.9% of total) had a previous DXA documented in the reviewed health records. For 231 patients who were ≥65 years old, who would routinely qualify for a screening DXA, only 63 (27.3%) had a documented DXA available.

CONCLUSION

vBMD measurements obtained by opportunistic QCT had comparable rates of osteopenia and osteoporosis detection to those previously reported using DXA in a similar population in Kuwait. These findings suggest that opportunistic QCT on preexisting CT scans can be effectively utilized to narrow gaps in osteoporosis screening.

Distribution of bone voids in the thoracolumbar spine in Chinese adults with and without osteoporosis: A cross-sectional multi-center study based on 464 vertebrae

Bone void is a novel intuitive morphological indicator to assess bone quality but its use in vertebrae has not been described. This cross-sectional and multi-center study aimed to investigate the distribution of bone voids in the thoracolumbar spine in Chinese adults based on quantitative computed tomography (QCT). A bone void was defined as a trabecular net region with extremely low bone mineral density (BMD) (<40 mg/cm³), detected by an algorithm based on phantom-less technology. A total of 464 vertebrae from 152 patients (51.8 ± 13.4 years old) were included. The vertebral trabecular bone was divided into eight sections based on the middle sagittal, coronal, and horizontal planes. Bone void of the whole vertebra and each section were compared between healthy, osteopenia, and osteoporosis groups and between spine levels. Receiver operator characteristic (ROC) curves were plotted and optimum cutoff points of void volume between the groups were obtained. The total void volumes of the whole vertebra were 124.3 ± 221.5 mm³, 1256.7 ± 928.7 mm^3, and 5624.6 ± 3217.7 mm³ in healthy, osteopenia, and osteoporosis groups, respectively. The detection rate of vertebrae with bone voids was higher and the normalized void volume was larger in the lumbar than in thoracic vertebrae. L3 presented the largest void (2165.0 ± 3396.0 mm³), while T12 had the smallest void (448.9 ± 699.4 mm³). The bone void was mainly located in the superior-posterior-right section (40.8 %). Additionally, bone void correlated positively with age and increased rapidly after 55 years. The most significant void volume increase was found in the inferior-anterior-right section whereas the least increase was found in the inferior-posterior-left section with aging. The cutoff points were 345.1 mm³ between healthy and osteopenia groups (sensitivity = 0.923, specificity = 0.932) and 1693.4 mm³ between osteopenia and osteoporosis groups (sensitivity = 1.000, specificity = 0.897). In conclusion, this study demonstrated the bone void distribution in vertebrae using clinical QCT data. The findings provide a new perspective for the description of bone quality and showed that bone void could guide clinical practice such as osteoporosis screening.

End to End Multitask Joint Learning Model for Osteoporosis Classification in CT Images

Osteoporosis is a significant global health concern that can be difficult to detect early due to a lack of symptoms. At present, the examination of osteoporosis depends mainly on methods containing dual-energy X-ray, quantitative CT, etc., which are high costs in terms of equipment and human time. Therefore, a more efficient and economical method is urgently needed for diagnosing osteoporosis. With the development of deep learning, automatic diagnosis models for various diseases have been proposed. However, the establishment of these models generally requires images with only lesion areas, and annotating the lesion areas is time-consuming. To address this challenge, we propose a joint learning framework for osteoporosis diagnosis that combines localization, segmentation, and classification to enhance diagnostic accuracy. Our method includes a boundary heat map regression branch for thinning segmentation and a gated convolution module for adjusting context features in the classification module. We also integrate segmentation and classification features and propose a feature fusion module to adjust the weight of different levels of vertebrae. We trained our model on a self-built dataset and achieved an overall accuracy rate of 93.3% for the three label categories (normal, osteopenia, and osteoporosis) in the testing datasets. The area under the curve for the normal category is 0.973; for the osteopenia category, it is 0.965; and for the osteoporosis category, it is 0.985. Our method provides a promising alternative for the diagnosis of osteoporosis at present.

Prevalence of osteoporosis and incidence of related fractures in developed economies in the Asia Pacific region: a systematic review

Robust data on osteoporosis in the Asia Pacific region could improve healthcare decision-making. Osteoporosis affects 10-30% of women aged 40 + , and up to 10% of men in 7 developed economies in Asia Pacific. Fractures affect 500-1000 adults aged 50 + per 100,000 person-years. Policymakers and clinicians must address this problem.

PURPOSE

Osteoporosis and associated fractures result in considerable morbidity, loss of productivity, early mortality, and increased healthcare expenses. Many countries in the Asia Pacific (AP) region, especially middle- and higher-income economies, are faced with aging and increasingly sedentary populations. It is critical to consolidate and analyze the available information on the prevalence and incidence of the disease in these countries.

METHODS

We systematically reviewed articles and gray literature for Australia, China, Hong Kong, Japan, Singapore, South Korea, and Taiwan. We searched PubMed, ScienceDirect, JSTOR, Cochrane, Google Scholar, and other databases for data published 2009-2018. We included articles with prevalence or incidence estimates for adults with osteoporosis or related fractures.

RESULTS

All locations had data available, but of widely varying quantity and quality. Most estimates for osteoporosis prevalence ranged from 10 to 30% for women ages 40 and older, and up to 10% for men. Osteoporotic fracture incidence typically ranged between 500 and 1000 per 100,000 person-years among adults aged 50 and older. Both outcomes typically increased with age and were more common among women.

CONCLUSION

Osteoporosis and associated fractures affect significant portions of the adult population in developed economies in the AP region. Governments and healthcare systems must consider how best to prevent and diagnose osteoporosis, and manage affected individuals, to reduce healthcare costs and mortality associated with fractures.

Age and gender differences in vertebral bone marrow adipose tissue and bone mineral density, based on MRI and quantitative CT

PURPOSE

To investigate the age and gender differences in vertebral bone marrow adipose tissue (BMAT) and volumetric bone mineral density (vBMD).

METHOD

A total of 427 healthy adults, including 175 males (41 %) and 252 females (59 %) with an age range of 21-82 years, underwent MRI and quantitative CT examinations of the lumbar spine (L2-L4), and the corresponding BMAT and vBMD values were measured. The age-related progressions of BMAT and vBMD in men and women were evaluated and compared.

RESULTS

In males, vertebral BMAT rose gradually throughout life, while in females, BMAT increased sharply between 41 and 60 years of age. In participants aged < 40 years, BMAT was greater in males compared to females (p ≤ 0.01), while after the age of 60, BMAT was higher in females (p < 0.05). In males, vBMD decreased gradually with age, while in females, there was a sharp decrease in vBMD after the age of 40 years. At age of 31-40 years, vBMD was higher in females (P < 0.002), while at age > 60 years, vBMD was higher in males (61-70 years, P < 0.01; > 70 years, P = 0.02).

CONCLUSIONS

We found significant age and gender differences in lumbar BMAT and vBMD. These findings will help to improve our understanding of the interaction between bone marrow fat content and bone mineral density in the ageing process.

Development of a system to assess the two- and three-dimensional bone mineral density of the lumbar vertebrae from clinical quantitative CT images

This study developed a system to quantify the lumbar spine's bone mineral density (BMD) in two and three dimensions for osteoporosis screening using quantitative CT images. Measuring the two-dimensional BMD could reproduce the BMD measurement performed in dual-energy X-ray absorptiometry, and an accurate diagnosis of osteoporosis was possible.

PURPOSE

To date, the assessment of bone mineral density (BMD) using CT images has been made in three dimensions, leading to errors in detecting osteoporosis based on the two-dimensional assessments of BMD using dual-energy X-ray absorptiometry (DXA-BMD). Herein, we aimed to develop a system that measures two- and three-dimensional lumbar BMD from quantitative CT images and validated the accuracy of the system in diagnosing osteoporosis with regard to the DXA classification.

METHODS

Fifty-nine pairs of spinal CT and DXA images were analyzed. First, the three-dimensional BMD was measured at the axial slice of the L1 vertebra on CT images (L1-vBMD). Then, the L1-L4 vertebrae were segmented from the CT images to measure the three-dimensional BMD at the trabecular region of the L1-L4 vertebral bodies (CT-vBMD). Lastly, the segmented vertebrae were projected onto the coronal plane to measure the two-dimensional BMD (CT-aBMD). Each parameter was correlated with DXA-BMD, and the receiver operating characteristic (ROC) curve to diagnose osteoporosis was assessed.

RESULTS

The correlation coefficients of DXA-BMD with L1-vBMD, CT-vBMD, and CT-aBMD were 0.364, 0.456, and 0.911, respectively (all p < 0.01). In the ROC curve analysis to diagnose osteoporosis, the area under the curve for CT-aBMD (0.941) was significantly higher than those for L1-vBMD (0.582) and CT-vBMD (0.657) (both p < 0.01).

CONCLUSION

Compared with L1-vBMD and CT-vBMD, CT-aBMD could accurately predict DXA-BMD and detect patients with osteoporosis. Given that our method can quantify BMD in both two and three dimensions, it could be used to screen for osteoporosis from quantitative CT images.

Influence of image reconstruction kernel on computed tomography-based finite element analysis in the clinical opportunistic screening of osteoporosis-A preliminary result

PURPOSE

This study aimed to evaluate the difference in vertebral mechanical properties estimated by finite element analysis (FEA) with different computed tomography (CT) reconstruction kernels and evaluate their accuracy in the screening and classification of osteoporosis.

METHODS

There were 31 patients enrolled retrospectively from the quantitative CT database of our hospital, uniformly covering the range from osteoporosis to normal. All subjects' CT raw data were reconstructed both with a smooth standard convolution kernel (B40f) and a sharpening bone convolution kernel (B70f), and FEA was performed on L1 of each subject based on two reconstructed images to obtain vertebral estimated strength and stiffness. The trabecular volumetric bone mineral density (vBMD) of the same vertebral body was also measured. FEA measurements between two kernels and their accuracy for osteoporosis screening were compared.

RESULTS

The vertebral stiffness and strength measured in FEA-B40f were significantly lower compared with those of FEA-B70f (12.0%, p = 0.000 and 10.7%, p = 0.000, respectively). The correlation coefficient between FEA-B70F and vBMD was slightly higher than that of FEA-B40F in both vertebral strength and stiffness (strength: r ²-B40f = 0.21, p = 0.009 vs. r ²-B70f = 0.27, p = 0.003; stiffness: r ²-B40f = 0.37, p = 0.002 vs. r ²-B70f = 0.45, p=0.000). The receiver operator characteristic curve showed little difference in the classification of osteoporosis between FEA-B40f and FEA-B70f.

CONCLUSION

Two kernels both seemed to be applicable to the opportunistic screening of osteoporosis by CT-FEA despite variance in FE-estimated bone strength and bone stiffness. A protocol for CT acquisition and FEA is still required to guarantee the reproducibility of clinical use.

Lumbar Bone Mineral Density Estimation From Chest X-Ray Images: Anatomy-Aware Attentive Multi-ROI Modeling

Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e.g., via Dual-energy X-ray Absorptiometry (DXA). This paper proposes a method to predict BMD from Chest X-ray (CXR), one of the most commonly accessible and low-cost medical imaging examinations. The proposed method first automatically detects Regions of Interest (ROIs) of local CXR bone structures. Then a multi-ROI deep model with transformer encoder is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. The proposed method is evaluated on 13719 CXR patient cases with ground truth BMD measured by the gold standard DXA. The model predicted BMD has a strong correlation with the ground truth (Pearson correlation coefficient 0.894 on lumbar 1). When applied in osteoporosis screening, it achieves a high classification performance (average AUC of 0.968). As the first effort of using CXR scans to predict the BMD, the proposed algorithm holds strong potential to promote early osteoporosis screening and public health.

Cortex or cancellous-which is early for the decrease of bone content for vertebral body in health?

OBJECTIVE

To obtain the cortex and cancellous parameter of the vertebral bone of healthy subjects using QCT. To explore which is earlier or faster for bone loss with age.

MATERIALS AND METHODS

733 physical examiners underwent chest low-dose CT examination were recruited, from April 1, 2021 to October 1, 2021. QCT sequence was used to obtain the bone mineral density of T12-L2 vertebral body without additional radiation. The mass and area of vertebral cortex and cancellous at the central level of L2 vertebral body were measured. The age -related characteristics of vertebral cortex and cancellous between male and female was analyzed and compared.

RESULTS

The vBMD of T12-L2 vertebral body decreased with age. Significant differences were found in volumetric bone mineral density (vBMD) of T12-L2 vertebral body. For female, significant differences were found in bone content involving cortical mass, cancellous mass, cortical area, cancellous area, cortical mass/cancellous mass and cortical area/cancellous area in different age groups, respectively. The cortical mass decreased with age in female. The cancellous mass of female increased and then decreased with peak at 31-40 y. The cortical area of female decreased gradually before 71 y. The cancellous area of female increased and then decreased with peak at 51-60 y. The values of mass ratio and area ratio in female showed a slowly downward trend with age. Significant differences of bone content between non-menopausal and menopausal women were found except the cancellous mass. For male, no significant differences were found in all parameters of bone content.

CONCLUSION

The changes of vertebral BMD, bone content of cortex and cancellous have different characteristics in different age. The change of cortex in female maybe earlier and faster than that of cancellous, especially in menopausal women.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria

RATIONALE AND OBJECTIVES

Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool.

MATERIALS AND METHODS

The dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture.

RESULTS

Our model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively.

CONCLUSION

Our model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

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.

Bone Loss in Patients with Pancreatic Neuroendocrine Tumors

Background: Pancreatic diseases may affect nutritional status, which is one of the important associated factors of bone health. High prevalence of osteoporosis or osteopenia has been reported in patients with pancreatitis. The bone loss in pancreatic neuroendocrine tumors (PNETs) has not been reported. In this study, we showed the prevalence of bone loss and possible associated factors in PNET patients. Methods: A total of 91 PNET patients were included. Bone status was evaluated based on computed tomography (CT) attenuation (Housfield units, HU): >160 HU, normal bone mineral density; osteopenia, 135 HU ≤ CT value ≤ 160 HU; osteoporosis, <135 HU. Associated factors for bone loss were identified by logistic regression analyses. Results: The average age was 55.76 years old in PNET patients. The prevalence of osteoporosis and low bone mass was 37.4% and 60.4%, respectively. Higher prevalence of osteoporosis was observed in patients older than 50 years (64.0%). Multivariate logistic analysis showed that age was an associated factor for low bone mass (odds ratio (OR) = 1.13, 95% confidence interval (CI): 1.04−1.22) and osteoporosis (OR = 1.14, 95% CI: 1.03−1.20). Diabetes was also associated with bone loss in PNET patients after adjusting with confounders (OR = 13.56, 95% CI: 1.02−132.4). Conclusions: Our data show that bone loss is common in patients with PNETs. Age and diabetes are associated with bone loss in PNET patients.

Effects of tele-exercise rehabilitation intervention on women at high risk of osteoporotic fractures: study protocol for a randomised controlled trial

INTRODUCTION

Premenopausal and postmenopausal osteoporosis and associated fragility fractures are major public health problems. Exercise, especially moderate-to-high-intensity impact exercise, has been recommended as an effective, low-cost non-pharmacological strategy for bone strength improvement; however, evidence on fracture risk is limited. In addition, maintaining regular training is currently a problem. Therefore, this study aims to conduct a randomised controlled trial of moderate-to-high-intensity tele-exercise intervention using a tele-rehabilitation app and quantify its effects on vertical fracture and fall prevention in women at high risk of osteoporotic fractures.

METHODS AND ANALYSIS

In this multicentre, randomised controlled trial, 794 women at high risk of osteoporotic fractures will be recruited and randomised into either the tele-exercise rehabilitation or control group. Participants in the control group will receive routine remote rehabilitation, while those in the intervention group will be provided with a 6-month tele-exercise rehabilitation. The primary outcomes are the percentage of participants with one or more new vertebral fractures and incidence of falls. Intention-to-treat, full analysis set and per-protocol approaches will be used for outcome analyses.

ETHICS AND DISSEMINATION

The study was approved by the biomedical research ethics committee of the West China Hospital of Sichuan University (2021-579). Written informed consent will be obtained from each participant after agreeing to participate in the study. The study findings will be presented at national and international scientific conferences and published in peer-reviewed journals. Results are propagated regardless of the magnitude or direction of the impact. Authorship is assigned according to authorship guidelines as defined by the International Board of Medical Journal Editors, and each author's role is based on journal requirements for publication.

TRIAL REGISTRATION NUMBER

The study was registered with the Chinese Clinical Trial Registry (ChiCTR2200058780) prior to recruitment (May 2022).

A Mini Review on Osteoporosis: From Biology to Pharmacological Management of Bone Loss

Osteoporosis refers to excessive bone loss as reflected by the deterioration of bone mass and microarchitecture, which compromises bone strength. It is a complex multifactorial endocrine disease. Its pathogenesis relies on the presence of several endogenous and exogenous risk factors, which skew the physiological bone remodelling to a more catabolic process that results in net bone loss. This review aims to provide an overview of osteoporosis from its biology, epidemiology and clinical aspects (detection and pharmacological management). The review will serve as an updated reference for readers to understand the basics of osteoporosis and take action to prevent and manage this disease.