Longitudinal trends in use of bone mass measurement among older americans, 1999-2005

Comprehensive Diagnostic Value of Vertebral Bone Quality Scores and Paravertebral Muscle Quality Parameters in Osteoporotic Vertebral Fractures

OBJECTIVE

Both vertebral bone quality (VBQ) scores and paravertebral muscle quality can predict osteoporotic vertebral fractures (OVFs). This study aimed to compare the diagnostic value of opportunistic VBQ scores and sarcopenia for OVF and to determine if their combined use could enhance diagnostic efficacy.

METHODS

A total of 194 patients, matched 1:1 by age and sex, were included. VBQ scores and paravertebral muscle cross-sectional areas (CSAs) were measured from lumbar magnetic resonance imaging. Independent risk factors for OVF were determined using multivariate conditional logistic regression. The predictive value of VBQ and muscle-related parameters for OVF were assessed with receiver operating characteristic curves.

RESULTS

VBQ, CSA, and degree of fat infiltration (DFF) showed significant differences between the fracture and nonfracture groups (P < 0.001). Multivariate analysis identified lower multifidus (MF) CSA, higher MF DFF, and higher VBQ as independent risk factors for OVF. Thresholds of 3.46 for VBQ and 11.83 cm2 for MF CSA yielded area under the curve values of 0.668 and 0.736, respectively, for predicting OVF. Combining VBQ and MF CSA notably enhanced the sensitivity and specificity of OVF diagnosis.

CONCLUSIONS

The predictive value of MF CSA in anticipating OVF was marginally superior to that of VBQ and MF DFF. Furthermore, the concurrent utilization of VBQ and MF CSA substantially enhanced the diagnostic accuracy of OVF. Considering that both VBQ and MF CSA can be opportunistically obtained during routine examinations, individuals with a VBQ ≥3.46 and MF CSA ≤11.83 cm2 should be categorized as high risk for OVF, warranting timely preventive measures.

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

RATIONALE AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Artificial intelligence-enhanced opportunistic screening of osteoporosis in CT scan: a scoping Review

PURPOSE

This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans.

METHODS

PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings.

RESULTS

Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection.

CONCLUSIONS

The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost.

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

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

Fully automated CT imaging biomarkers for opportunistic prediction of future hip fractures

OBJECTIVE

Assess automated CT imaging biomarkers in patients who went on to hip fracture, compared with controls.

METHODS

In this retrospective case-control study, 6926 total patients underwent initial abdominal CT over a 20-year interval at one institution. A total of 1308 patients (mean age at initial CT, 70.5 ± 12.0 years; 64.4% female) went on to hip fracture (mean time to fracture, 5.2 years); 5618 were controls (mean age 70.3 ± 12.0 years; 61.2% female; mean follow-up interval 7.6 years). Validated fully automated quantitative CT algorithms for trabecular bone attenuation (at L1), skeletal muscle attenuation (at L3), and subcutaneous adipose tissue area (SAT) (at L3) were applied to all scans. Hazard ratios (HRs) comparing highest to lowest risk quartiles and receiver operating characteristic (ROC) curve analysis including area under the curve (AUC) were derived.

RESULTS

Hip fracture HRs (95% CI) were 3.18 (2.69-3.76) for low trabecular bone HU, 1.50 (1.28-1.75) for low muscle HU, and 2.18 (1.86-2.56) for low SAT. 10-year ROC AUC values for predicting hip fracture were 0.702, 0.603, and 0.603 for these CT-based biomarkers, respectively. Multivariate combinations of these biomarkers further improved predictive value; the 10-year ROC AUC combining bone/muscle/SAT was 0.733, while combining muscle/SAT was 0.686.

CONCLUSION

Opportunistic use of automated CT bone, muscle, and fat measures can identify patients at higher risk for future hip fracture, regardless of the indication for CT imaging.

ADVANCES IN KNOWLEDGE

CT data can be leveraged opportunistically for further patient evaluation, with early intervention as needed. These novel AI tools analyse CT data to determine a patient's future hip fracture risk.

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. It has significant prognostic implications, identifying individuals at higher risk of all-cause mortality. This Artificial Intelligence (AI)-enabled CXR strategy may function as an early detection screening tool for osteoporosis. The aim of this study was to develop a deep learning model (DLM) to identify osteoporosis via CXR features and investigate the performance and clinical implications. This study collected 48,353 CXRs with the corresponding T score according to Dual energy X-ray Absorptiometry (DXA) from the academic medical center. Among these, 35,633 CXRs were used to identify CXR- Osteoporosis (CXR-OP). Another 12,720 CXRs were used to validate the performance, which was evaluated by the area under the receiver operating characteristic curve (AUC). Furthermore, CXR-OP was tested to assess the long-term risks of mortality, which were evaluated by Kaplan‒Meier survival analysis and the Cox proportional hazards model. The DLM utilizing CXR achieved AUCs of 0.930 and 0.892 during internal and external validation, respectively. The group that underwent DXA with CXR-OP had a higher risk of all-cause mortality (hazard ratio [HR] 2.59, 95% CI: 1.83-3.67), and those classified as CXR-OP in the group without DXA also had higher all-cause mortality (HR: 1.67, 95% CI: 1.61-1.72) in the internal validation set. The external validation set produced similar results. Our DLM uses CXRs for early detection of osteoporosis, aiding physicians to identify those at risk. It has significant prognostic implications, improving life quality and reducing mortality. AI-enabled CXR strategy may serve as a screening tool.

Development and Validation of a Feature-Based Broad-Learning System for Opportunistic Osteoporosis Screening Using Lumbar Spine Radiographs

RATIONALE AND OBJECTIVES

Osteoporosis is primarily diagnosed using dual-energy X-ray absorptiometry (DXA); yet, DXA is significantly underutilized, causing osteoporosis, an underdiagnosed condition. We aimed to provide an opportunistic approach to screen for osteoporosis using artificial intelligence based on lumbar spine X-ray radiographs.

MATERIALS AND METHODS

In this institutional review board-approved retrospective study, female patients aged ≥50 years who received both X-ray scans and DXA of the lumbar vertebrae, in three centers, were included. A total of 1180 cases were used for training and 145 cases were used for testing. We proposed a novel broad-learning system (BLS) and then compared the performance of BLS models using radiomic features and deep features as a source of input. The deep features were extracted using ResNet18 and VGG11, respectively. The diagnostic performances of these BLS models were evaluated with the area under the curve (AUC), sensitivity, and specificity.

RESULTS

The incidence rate of osteoporosis in the training and test sets was 35.9% and 37.9%, respectively. The radiomic feature-based BLS model achieved higher testing AUC (0.802 vs. 0.654 vs. 0.632, both P = .002), sensitivity (78.2% vs. 56.4% vs. 50.9%), and specificity (82.2% vs. 74,4% vs. 75.6%) than the two deep feature-based BLS models.

CONCLUSION

Our proposed radiomic feature-based BLS model has the potential to expand osteoporosis screening to a broader population by identifying osteoporosis on lumbar spine X-ray radiographs.

Computer Vision in Osteoporotic Vertebral Fracture Risk Prediction: A Systematic Review

Osteoporotic vertebral fractures (OVFs) are a significant health concern linked to increased morbidity, mortality, and diminished quality of life. Traditional OVF risk assessment tools like bone mineral density (BMD) only capture a fraction of the risk profile. Artificial intelligence, specifically computer vision, has revolutionized other fields of medicine through analysis of videos, histopathology slides and radiological scans. In this review, we provide an overview of computer vision algorithms and current computer vision models used in predicting OVF risk. We highlight the clinical applications, future directions and limitations of computer vision in OVF risk prediction.

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.

Physician-Reported Barriers to Osteoporosis Screening: A Nationwide Survey

OBJECTIVE

Despite increased awareness, osteoporosis screening rates remain low. The objective of this survey study was to identify physician-reported barriers to osteoporosis screening.

METHODS

We conducted a survey of 600 physician members of the Endocrine Society, American Academy of Family Practice, and American Geriatrics Society. The respondents were asked to rate barriers to osteoporosis screening in their patients. We performed multivariable logistic regression analyses to determine correlates with the most commonly reported barriers.

RESULTS

Of 566 response-eligible physicians, 359 completed the survey (response rate, 63%). The most commonly reported barriers to osteoporosis screening included patient nonadherence (63%), physician concern about cost (56%), clinic visit time constraints (51%), low on the priority list (45%), and patient concern about cost (43%). Patient nonadherence as a barrier was correlated with physicians in academic tertiary centers (odds ratio [OR], 2.34; 95% confidence interval [CI], 1.06-5.13), whereas clinic visit time constraints were correlated with physicians in both community-based academic affiliates and academic tertiary care ([OR, 1.96; 95% CI, 1.10-3.50] and [OR, 2.48; 95% CI, 1.22-5.07], respectively). Geriatricians (OR, 0.40; 95% CI, 0.21-0.76) and physicians with >10 years in practice were less likely to report clinic visit time constraints as a barrier (11-20 years: OR, 0.41; 95% CI, 0.20-0.85; >20 years: OR, 0.32; 95% CI, 0.16-0.65). Physicians with more patient-facing time (3-5 compared with 0.5-2 d/wk) were more likely to place screening low on the priority list (OR, 2.66; 95% CI, 1.34-5.29).

CONCLUSION

Understanding barriers to osteoporosis screening is vital in developing strategies to improve osteoporosis care.

Racial and ethnic difference in the risk of fractures in the United States: a systematic review and meta-analysis

This systematic review and meta-analysis examined the association between race and ethnicity and fracture risk in the United States. We identified relevant studies by searching PubMed and EMBASE for studies published from the databases' inception date to December 23, 2022. Only observational studies conducted in the US population that reported the effect size of racial-ethnic minority groups versus white people were included. Two investigators independently conducted literature searches, study selection, risk of bias assessment, and data abstraction; discrepancies were resolved by consensus or consultation of a third investigator. Twenty-five studies met the inclusion criteria, and the random-effects model was used to calculate the pooled effect size due to heterogeneity between the studies. Using white people as the reference group, we found that people of other races and ethnic groups had a significantly lower fracture risk. In Black people, the pooled relative risk (RR) was 0.46 (95% confidence interval (CI), 0.43-0.48, p < 0.0001). In Hispanics, the pooled RR was 0.66 (95% CI, 0.55-0.79, p < 0.0001). In Asian Americans, the pooled RR was 0.55 (95% CI, 0.45-0.66, p < 0.0001). In American Indians, the pooled RR was 0.80 (95% CI, 0.41-1.58, p = 0.3436). Subgroup analysis by sex in Black people revealed the strength of association was greater in men (RR = 0.57, 95% CI = 0.51-0.63, p < 0.0001) than in women (RR = 0.43, 95% CI = 0.39-0.47, p < 0.0001). Our findings suggest that people of other races and ethnic groups have a lower fracture risk than white people.

Osteoporosis Screening Disparities among Ethnic and Racial Minorities: A Systematic Review

Background

Osteoporosis is a preventable disease that is simple and cost-effective to screen based on clinical practice guidelines, yet many patients go undiagnosed and untreated leading to increased burden of the disease. Specifically, racial and ethnic minorities have lower rates of dual energy absorptiometry (DXA) screening. Inadequate screening may lead to an increased risk of fracture, higher health care costs, and increased morbidity and mortality disproportionately experienced by racial-ethnic minority populations.

Purpose

This systematic review assessed and summarized the racial and ethnic disparities that exist for osteoporosis screening by DXA.

Methods

Using terms related to osteoporosis, racial and ethnic minorities, and DXA, an electronic search of databases was performed in SCOPUS, CINAHL, and PubMed. Articles were screened using predefined inclusion and exclusion criteria which dictated the final articles used in the review. Full text articles that were selected for inclusion underwent quality appraisal and data extraction. Once extracted, data from the articles were combined at an aggregate level.

Results

The search identified 412 articles. After screening, a total of 16 studies were included in the final review. The overall quality of the studies included was high. Of the 16 articles reviewed, 14 identified significant disparities between racial minority and majority groups and determined that the eligible patients in racial minority groups were less likely to be referred to DXA screening.

Conclusion

There is a significant disparity in osteoporosis screening among racial and ethnic minorities. Future efforts should focus on addressing these inconsistencies in screening and removing bias from the healthcare system. Additional research is required to determine the consequence of this discrepancy in screening and methods of equitizing osteoporosis care.

Clinical profile, risk factors and functional outcomes in women and men presenting with hip fractures in KwaZulu-Natal, South Africa

Rationale Appropriate screening can prevent osteoporotic hip fractures (HF). There is little data on clinical risk factors (CRFs) from Africa.

MAIN RESULT

Subjects with HF had similar CRFs to high income countries and poor functional outcomes post HF.

SIGNIFICANCE

Screening and treatment algorithms to improve outcomes post HF need to be implemented.

PURPOSE

Limited data exist on clinical risk factors (CRFs) for and functional outcomes following hip fractures (HF) in South Africa (SA).

METHODS

In a prospective observational study conducted in two municipalities in KwaZulu-Natal, a structured questionnaire recorded demographic data, CRFs, self-reported chronic medical conditions and functional status. Parametric and non-parametric tests were used to test for differences and the McNemar test for change over time.

RESULTS

The median age of the 287 subjects was 72 years (IQR 64-80 years) with the majority women (67.2%), who were significantly older than men. Two or more comorbidities were present in 76.3%. Hypertension (71.4%) and diabetes (29.6%) were most common. Eleven (3.8%) reported a previous diagnosis of osteoporosis and four (1.4%) prior treatment for osteoporosis. A history of cancer (15.4% v. 1.2%, p < 0.001), previous diagnosis of osteoporosis (17.9% v. 1.6%, p < 0.001) and treatment for osteoporosis (7.7% v. 0.4%, p < 0.001) was significantly more common in private compared to public sector subjects. African subjects had a higher prevalence of HIV infection compared to Indian (12.5% v. 0%, p < 0.001) while Indian subjects were more likely to report two or more comorbidities (p = 0.003) and hypertension (p = 0.005) compared to African subjects. Common CRFs were a previous fracture (32.4%), prior fall (24.7%), weight below 57 kg (23.3%), smoking (19.2%) and alcohol use of more than 3 units per day (17.8%). Less than 5% reported a history of parental HF or glucocorticosteroid use. Functional status was available for 206 subjects. Of the 163 participants who had surgery, 81% were independent prior to the HF, compared to the significantly lower 6.7% and 56.4% at 30 days and 1 year post fracture, respectively. The proportion with some degree of dependency rose significantly from 19% pre-fracture to 43.6%, 1 year post-fracture. Walking up stairs and transfer from bed to chair were the most commonly affected activities.

CONCLUSION

Clinical risk factors for HF are similar to those published internationally and support the use of current risk assessment models in SA. Targeted management and rehabilitation programs are required to improve functional outcomes post-HF.

Improved CT-based Osteoporosis Assessment with a Fully Automated Deep Learning Tool

Purpose

To develop, test, and validate a deep learning (DL) tool that improves upon a previous feature-based CT image processing bone mineral density (BMD) algorithm and compare it against the manual reference standard.

Materials and Methods

This single-center, retrospective, Health Insurance Portability and Accountability Act-compliant study included manual L1 trabecular Hounsfield unit measurements from abdominal CT scans in 11 035 patients (mean age, 58 years ± 12 [SD]; 6311 women) as the reference standard. Automated level selection and L1 trabecular region of interest (ROI) placement were then performed in this CT cohort with both a previously validated feature-based image processing tool and a new DL tool. Overall technical success rates and agreement with the manual reference standard were assessed.

Results

The overall success rate of the DL tool in this heterogeneous patient cohort was significantly higher than that of the older image processing BMD algorithm (99.3% vs 89.4%, P < .001). Using this DL tool, the closest median Hounsfield unit values for single-, three-, and seven-slice vertebral ROIs were within 5% of the manual reference standard Hounsfield unit values in 35.1%, 56.9%, and 85.8% of scans; within 10% in 56.6%, 75.6%, and 92.9% of scans; and within 25% in 76.5%, 89.3%, and 97.1% of scans, respectively. Trade-offs in sensitivity and specificity for osteoporosis assessment were observed from the single-slice approach (sensitivity, 39.4%; specificity, 98.3%) to the minimum value of the multislice approach (for seven contiguous slices; sensitivity, 71.3% and specificity, 94.6%).

Conclusion

The new DL BMD tool demonstrated a higher success rate than the older feature-based image processing tool, and its outputs can be targeted for higher specificity or sensitivity for osteoporosis assessment.Keywords: CT, CT-Quantitative, Abdomen/GI, Skeletal-Axial, Spine, Deep Learning, Machine Learning Supplemental material is available for this article. © RSNA, 2022.

Utilizing machine learning for opportunistic screening for low BMD using CT scans of the cervical spine

BACKGROUND

Computed Tomography (CT) scans of the cervical spine are often performed to evaluate patients for trauma and degenerative changes of the cervical spine. We hypothesized that the CT attenuation of the cervical vertebrae can be used to identify patients who should be screened for osteoporosis.

METHODS

Retrospective study of 253 patients (177 training/validation and 76 test) with unenhanced CT scans of the cervical spine and DXA studies within 12 months of each other. Volumetric segmentation of C1-T1, clivus, and first ribs was performed to obtain the CT attenuation of each bone. The correlations of the CT attenuations between the bones and with DXA measurements were evaluated. Univariate receiver operator characteristic (ROC) analyses, and multivariate classifiers (Random Forest (RF), XGBoost, Naïve Bayes (NB), and Support Vector Machines (SVM)) analyzing the CT attenuation of all bones, were utilized to predict patients with osteopenia/osteoporosis and femoral neck bone mineral density (BMD) T-scores <-1.

RESULTS

There were positive correlations between the CT attenuation of each bone, and with the DXA measurements. A CT attenuation threshold of 305.2 Hounsfield Units (HU) at C3 had the highest accuracy =0.763 (AUC=0.814) to detect femoral neck BMD T-scores ≤-1 and a CT attenuation threshold of 323.6 HU at C3 had the highest accuracy=0.774 (AUC=0.843) to detect osteopenia/osteoporosis. The SVM classifier (AUC=0.756) had higher AUC than the RF (AUC=0.692, P=0.224), XGBoost (AUC=0.736; P=0.814), NB (AUC=0.622, P=0.133) and CT threshold of 305.2 HU at C3 (AUC=0.704, P=0.531) classifiers to identify patients with femoral neck BMD T-scores <-1. The SVM classifier (accuracy=0.816) was more accurate than using the CT threshold of 305.2 HU at C3 (accuracy=0.671) (McNemar's χ₁²=7.55, P=0.006).

CONCLUSION

Opportunistic screening for low BMD can be done using cervical spine CT scans. A SVM classifier was more accurate than using the CT threshold of 305.2 HU at C3.

Deep Radiomics-based Approach to the Diagnosis of Osteoporosis Using Hip Radiographs

PURPOSE

To develop and validate deep radiomics models for the diagnosis of osteoporosis using hip radiographs.

MATERIALS AND METHODS

A deep radiomics model was developed using 4924 hip radiographs from 4308 patients (3632 women; mean age, 62 years ± 13 [SD]) obtained between September 2009 and April 2020. Ten deep features, 16 texture features, and three clinical features were used to train the model. T score measured with dual-energy x-ray absorptiometry was used as a reference standard for osteoporosis. Seven deep radiomics models that combined different types of features were developed: clinical (model C); texture (model T); deep (model D); texture and clinical (model TC); deep and clinical (model DC); deep and texture (model DT); and deep, texture, and clinical features (model DTC). A total of 444 hip radiographs obtained between January 2019 and April 2020 from another institution were used for the external test. Six radiologists performed an observer performance test. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic performance.

RESULTS

For the external test set, model D (AUC, 0.92; 95% CI: 0.89, 0.95) demonstrated higher diagnostic performance than model T (AUC, 0.77; 95% CI: 0.70, 0.83; adjusted P < .001). Model DC (AUC, 0.95; 95% CI: 0.92, 0.97; adjusted P = .03) and model DTC (AUC, 0.95; 95% CI: 0.92, 0.97; adjusted P = .048) showed improved diagnostic performance compared with model D. When observer performance without and with the assistance of the model DTC prediction was compared, performance improved from a mean AUC of 0.77 to 0.87 (P = .002).

CONCLUSION

Deep radiomics models using hip radiographs could be used to diagnose osteoporosis with high performance.Keywords: Skeletal-Appendicular, Hip, Absorptiometry/Bone Densitometry© RSNA, 2022.

Race and Age Impact Osteoporosis Screening Rates in Women Prior to Hip Fracture

Bone mineral density screening and clinical risk factors are important to stratify individuals for increased risk of fracture. In a population with no history of fractures or baseline bone density measurement, black women were less likely to be screened than white counterparts prior to hip fracture.

PURPOSE

To evaluate overall BMD (bone mineral density) screening rates within two years of hip fracture and to identify any disparities for osteoporosis screening or treatment in a female cohort who were eligible for screening under insurance and national recommendations.

METHODS

Data were obtained from 1,109 female patients listed in the Research Action for Health Network (REACHnet) database, which consists of multiple health partner systems in Louisiana and Texas. Patients < 65 years old or with a history of hip fracture or osteoporosis diagnosis, screening or treatment more than 2 years before hip fracture were removed.

RESULTS

Only 223 (20.1%) females were screened within the two years prior to hip fracture. Additionally, only 23 (10%) of the screened patients received treatment, despite 187 (86.6%) patients being diagnosed with osteoporosis or osteopenia. Screening rates reached a maximum of 27.9% in the 75-80 age group, while the 90 + age group had the lowest screening rates of 12%. We found a quadratic relationship between age and screening rates, indicating that the screening rate increases in age until age 72 and then decreases starkly. After adjusting for potential confounders, we found that black patients had significantly decreased screening rates compared to white patients (adjusted OR = .454, 95% CI = .227-.908, p value = .026) which held in general and for patient ages 65-97.

CONCLUSION

Despite national recommendations, overall BMD screening rates among women prior to hip fracture are low. If individuals are not initially screened when eligible, they are less likely to ever be screened prior to fracture. Clinicians should address racial disparities by recommending more screening to otherwise healthy black patients above the age of 65. Lastly, treatment rates need to increase among those diagnosed with osteoporosis since all patients went on to hip fracture.

Comparison of diagnostic accuracy of 2D and 3D measurements to determine opportunistic screening of osteoporosis using the proximal femur on abdomen-pelvic CT

OBJECTIVES

To compare the osteoporosis-predicting ability of computed tomography (CT) indexes in abdomen-pelvic CT using the proximal femur and the reliability of measurements in two- and three-dimensional analyses.

METHODS

Four hundred thirty female patients (age range, 50-96 years) who underwent dual-energy X-ray absorptiometry and abdominal-pelvic CT within 1 month were retrospectively selected. The volumes of interest (VOIs) from the femoral head to the lesser trochanter and the femoral neck were expressed as 3DFemur. Round regions of interest (ROIs) of image plane drawn over the femoral neck touching the outer cortex were determined as 2Dcoronal. In HU histogram analysis (HUHA), the percentages of HU histogram ranges related to the ROI or VOI were classified as HUHAFat (<0 HU) and HUHABone (126 HU≤). Diagnostic performance, correlation analysis and measurement reliability were analyzed by receiver operating characteristic curves, correlation coefficient and interobserver correlation coefficient (ICC), respectively.

RESULTS

AUCs of each HUHA and mean-HU measurement on 2D-ROI and 3D-VOI were 0.94 or higher (P < 0.001). Both 3DFemur-Mean-HU and 3DFemur-HUHABone showed the highest AUC (0.96). The cut-off value of 3DFemur-Mean-HU was 231HU or less, (sensitivity: 94.8%; specificity: 85.0%; correlation coefficient: -0.65; P <0.001) for diagnosis of osteoporosis. There was no superiority between AUCs in 2D-ROI and 3D-VOI measurements (P > 0.05). Reliability of the 3D-VOI measurement showed perfect agreement (ICC ≥ 0.94), and 2D-ROI showed moderate to good agreement (ICC range: 0.63~0.84).

CONCLUSIONS

CT indexes on 3D-VOI for predicting femoral osteoporosis showed similar diagnostic accuracy with better reproducibility of measurement, compared with 2D-ROI.

Automated bone mineral density prediction and fracture risk assessment using plain radiographs via deep learning

Dual-energy X-ray absorptiometry (DXA) is underutilized to measure bone mineral density (BMD) and evaluate fracture risk. We present an automated tool to identify fractures, predict BMD, and evaluate fracture risk using plain radiographs. The tool performance is evaluated on 5164 and 18175 patients with pelvis/lumbar spine radiographs and Hologic DXA. The model is well calibrated with minimal bias in the hip (slope = 0.982, calibration-in-the-large = -0.003) and the lumbar spine BMD (slope = 0.978, calibration-in-the-large = 0.003). The area under the precision-recall curve and accuracy are 0.89 and 91.7% for hip osteoporosis, 0.89 and 86.2% for spine osteoporosis, 0.83 and 95.0% for high 10-year major fracture risk, and 0.96 and 90.0% for high hip fracture risk. The tool classifies 5206 (84.8%) patients with 95% positive or negative predictive value for osteoporosis, compared to 3008 DXA conducted at the same study period. This automated tool may help identify high-risk patients for osteoporosis.

MRI-based Texture Analysis of Trabecular Bone for Opportunistic Screening of Skeletal Fragility

CONTEXT

Many individuals at high risk for osteoporosis and fragility fracture are never screened by traditional methods. Opportunistic use of imaging obtained for other clinical purposes is required to foster identification of these patients.

OBJECTIVE

The aim of this pilot study was to evaluate texture features as a measure of bone fragility, by comparing clinically acquired magnetic resonance imaging (MRI) scans from individuals with and without a history of fragility fracture.

METHODS

This study retrospectively investigated 100 subjects who had lumbar spine MRI performed at our institution. Cases (n = 50) were postmenopausal women with osteoporosis and a confirmed history of fragility fracture. Controls (n = 50) were age- and race-matched postmenopausal women with no known fracture history. Trabecular bone from the lumbar vertebrae was segmented to create regions of interest within which a gray level co-occurrence matrix was used to quantify the distribution and spatial organization of voxel intensity. Heterogeneity in the trabecular bone texture was assessed by several features, including contrast (variability), entropy (disorder), and angular second moment (homogeneity).

RESULTS

Texture analysis revealed that trabecular bone was more heterogeneous in fracture patients. Specifically, fracture patients had greater texture variability (+76% contrast; P = 0.005), greater disorder (+10% entropy; P = 0.005), and less homogeneity (-50% angular second moment; P = 0.005) compared with controls.

CONCLUSIONS

MRI-based textural analysis of trabecular bone discriminated between patients with known osteoporotic fractures and controls. Further investigation is required to validate this promising methodology, which could greatly expand the number of patients screened for skeletal fragility.

Assessment of Skeletal Strength: Bone Density Testing and Beyond

A bone fractures when a force applied to it exceeds its strength. Assessment of bone strength is an important component in determining the risk of fracture and guiding treatment decisions. Dual-energy X-ray absorptiometry is used to diagnosis osteoporosis, estimate fracture risk, and monitor changes in bone density. Fracture risk algorithms provide enhanced fracture risk predictability. Advanced technologies with computed tomography (CT) and MRI can measure parameters of bone microarchitecture. Mathematical modeling using CT data can evaluate the behavior of bone structures in response to external loading. Microindentation techniques directly measure the strength of outer bone cortex.

Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study

Background

Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (APCT) is one of the most widely conducted medical tests. Artificial intelligence and radiomics analysis have recently been spotlighted. This is the first study to evaluate the prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT.

Materials and methods

500 patients (M: F = 70:430; mean age, 66.5 ± 11.8yrs; range, 50–96 years) underwent both dual-energy X-ray absorptiometry and APCT within 1 month. The volume of interest of the left proximal femur was extracted and 41 radiomics features were calculated using 3D volume of interest analysis. Top 10 importance radiomic features were selected by the intraclass correlation coefficient and random forest feature selection. Study cohort was randomly divided into 70% of the samples as the training cohort and the remaining 30% of the sample as the validation cohort. Prediction performance of machine-learning analysis was calculated using diagnostic test and comparison of area under the curve (AUC) of receiver operating characteristic curve analysis was performed between training and validation cohorts.

Results

The osteoporosis prevalence of this study cohort was 20.8%. The prediction performance of the machine-learning analysis to diagnose osteoporosis in the training and validation cohorts were as follows; accuracy, 92.9% vs. 92.7%; sensitivity, 86.6% vs. 80.0%; specificity, 94.5% vs. 95.8%; positive predictive value, 78.4% vs. 82.8%; and negative predictive value, 96.7% vs. 95.0%. The AUC to predict osteoporosis in the training and validation cohorts were 95.9% [95% confidence interval (CI), 93.7%-98.1%] and 96.0% [95% CI, 93.2%-98.8%], respectively, without significant differences (P = 0.962).

Conclusion

Prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information. Prime examples of cardiometabolic information include measurement of bone mineral density for osteoporosis screening, quantification of aortic calcium for assessment of cardiovascular risk, quantification of visceral fat for evaluation of metabolic syndrome, assessment of muscle bulk and density for diagnosis of sarcopenia, and quantification of liver fat for assessment of hepatic steatosis. All of these relevant biometric measures can now be fully automated through the use of artificial intelligence algorithms, which provide rapid and objective assessment and allow large-scale population-based screening. Initial investigations into these measures of body composition have demonstrated promising performance for prediction of future adverse events that matches or exceeds the best available clinical prediction models, particularly when these CT-based measures are used in combination. In this review, the concept of CT-based opportunistic screening is discussed, and an overview of the various automated biomarkers that can be derived from essentially all abdominal CT examinations is provided, drawing heavily on the authors' experience. As radiology transitions from a volume-based to a value-based practice, opportunistic screening represents a promising example of adding value to services that are already provided. If the potentially high added value of these objective CT-based automated measures is ultimately confirmed in subsequent investigations, this opportunistic screening approach could be considered for intentional CT-based screening. ^©RSNA, 2021.

Automated Abdominal CT Imaging Biomarkers for Opportunistic Prediction of Future Major Osteoporotic Fractures in Asymptomatic Adults

Background Body composition data from abdominal CT scans have the potential to opportunistically identify those at risk for future fracture. Purpose To apply automated bone, muscle, and fat tools to noncontrast CT to assess performance for predicting major osteoporotic fractures and to compare with the Fracture Risk Assessment Tool (FRAX) reference standard. Materials and Methods Fully automated bone attenuation (L1-level attenuation), muscle attenuation (L3-level attenuation), and fat (L1-level visceral-to-subcutaneous [V/S] ratio) measures were derived from noncontrast low-dose abdominal CT scans in a generally healthy asymptomatic adult outpatient cohort from 2004 to 2016. The FRAX score was calculated from data derived from an algorithmic electronic health record search. The cohort was assessed for subsequent future fragility fractures. Subset analysis was performed for patients evaluated with dual x-ray absorptiometry (n = 2106). Hazard ratios (HRs) and receiver operating characteristic curve analyses were performed. Results A total of 9223 adults were evaluated (mean age, 57 years ± 8 [standard deviation]; 5152 women) at CT and were followed over a median time of 8.8 years (interquartile range, 5.1-11.6 years), with documented subsequent major osteoporotic fractures in 7.4% (n = 686), including hip fractures in 2.4% (n = 219). Comparing the highest-risk quartile with the other three quartiles, HRs for bone attenuation, muscle attenuation, V/S fat ratio, and FRAX were 2.1, 1.9, 0.98, and 2.5 for any fragility fracture and 2.0, 2.5, 1.1, and 2.5 for femoral fractures, respectively (P < .001 for all except V/S ratio, which was P ≥ .51). Area under the receiver operating characteristic curve (AUC) values for fragility fracture were 0.71, 0.65, 0.51, and 0.72 at 2 years and 0.63, 0.62, 0.52, and 0.65 at 10 years, respectively. For hip fractures, 2-year AUC for muscle attenuation alone was 0.75 compared with 0.73 for FRAX (P = .43). Multivariable 2-year AUC combining bone and muscle attenuation was 0.73 for any fragility fracture and 0.76 for hip fractures, respectively (P ≥ .73 compared with FRAX). For the subset with dual x-ray absorptiometry T-scores, 2-year AUC was 0.74 for bone attenuation and 0.65 for FRAX (P = .11). Conclusion Automated bone and muscle imaging biomarkers derived from CT scans provided comparable performance to Fracture Risk Assessment Tool score for presymptomatic prediction of future osteoporotic fractures. Muscle attenuation alone provided effective hip fracture prediction. © RSNA, 2020 See also the editorial by Smith in this issue.

Phantomless assessment of volumetric bone mineral density using virtual non-contrast images from spectral detector computed tomography

OBJECTIVE

To evaluate phantomless assessment of volumetric bone mineral density (vBMD) based on virtual non-contrast images of arterial (VNC_a) and venous phase (VNC_v) derived from spectral detector CT in comparison to true non-contrast (TNC) images and adjusted venous phase conventional images (CI_V(adjusted)).

METHODS

104 consecutive patients who underwent triphasic spectral detector CT between January 2018 and April 2019 were retrospectively included. TNC, VNC_a, VNC_v and venous phase images (CI_V) were reconstructed. vBMD was obtained by two radiologists using an FDA/CE-cleared software. Average vBMD of the first three lumbar vertebrae was determined in each reconstruction; vBMD of CI_V was adjusted for contrast enhancement as suggested earlier.

RESULTS

vBMD values obtained from CI_V(adjusted) are comparable to vBMD values derived from TNC images (91.79 ± 36.52 vs 90.16 ± 41.71 mg/cm³, p = 1.00); however, vBMD values derived from VNC_a and VNC_v (42.20 ± 22.50 and 41.98 ± 23.3 mg/cm³ respectively) were significantly lower as compared to vBMD values from TNC and CI_V(adjusted) (all p ≤ 0.01).

CONCLUSION

Spectral detector CT-derived virtual non-contrast images systematically underestimate vBMD and therefore should not be used without appropriate adjustments. Adjusted venous phase images provide reliable results and may be utilized for an opportunistic BMD screening in CT examinations.

ADVANCES IN KNOWLEDGE

Adjustments of venous phase images facilitate opportunistic assessment of vBMD, while spectral detector CT-derived VNC images systematically underestimate vBMD.

Automated opportunistic osteoporotic fracture risk assessment using computed tomography scans to aid in FRAX underutilization

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA)^1,2 and risk predictors like the Fracture Risk Assessment Tool (FRAX)^3-6, are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates^7,8.

Quality Measures and Quality Improvement Initiatives in Osteoporosis-an Update

PURPOSE OF REVIEW

The aims of this review are to summarize current performance for osteoporosis quality measures used by Centers for Medicare and Medicaid (CMS) for pay-for-performance programs and to describe recent quality improvement strategies around these measures.

RECENT FINDINGS

Healthcare Effectiveness Data and Information (HEDIS) quality measures for the managed care population indicate gradual improvement in osteoporosis screening, osteoporosis identification and treatment following fragility fracture, and documentation of fall risk assessment and plan of care between 2006 and 2016. However, population-based studies suggest achievement for these process measures is lower where reporting is not mandated. Performance gaps remain, particularly for post-fracture care. Elderly patients with increased comorbidity are especially vulnerable to fractures, yet underperformance is documented in this population. Gender and racial disparities also exist. As has been shown for other areas of health care, education alone has a limited role as a quality improvement intervention. Multifactorial and systems-based interventions seem to be most successful in leading to measurable change for osteoporosis care and fall prevention. Despite increasing recognition of evidence-based quality measures for osteoporosis and incentives to improve upon performance for these measures, persistent gaps in care exist that will require further investigation into sustainable and value-adding quality improvement interventions.

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

OBJECTIVES

To compare opportunistic quantitative CT (QCT) with dual energy X-ray absorptiometry (DXA) in their ability to predict incident vertebral fractures.

METHODS

We included 84 patients aged 50 years and older, who had routine CT including the lumbar spine and DXA within a 12-month period (baseline) as well as follow-up imaging after at least 12 months or who sustained an incident vertebral fracture documented earlier. Patients with bone disorders aside from osteoporosis were excluded. Fracture status and trabecular bone mineral density (BMD) were retrospectively evaluated in baseline CT and fracture status was reassessed at follow-up. BMDQCT was assessed by opportunistic QCT with asynchronous calibration of multiple MDCT scanners.

RESULTS

Sixteen patients had incident vertebral fractures showing lower mean BMDQCT than patients without fracture (p = 0.001). For the risk of incident vertebral fractures, the hazard ratio increased per SD in BMDQCT (4.07; 95% CI, 1.98-8.38), as well as after adjusting for age, sex, and prevalent fractures (2.54; 95% CI, 1.09-5.90). For DXA, a statistically significant increase in relative hazard per SD decrease in T-score was only observed after age and sex adjustment (1.57; 95% CI, 1.04-2.38). The predictability of incident vertebral fractures was good by BMDQCT (AUC = 0.76; 95% CI, 0.64-0.89) and non-significant by T-scores. Asynchronously calibrated CT scanners showed good long-term stability (linear drift ranging from - 0.55 to - 2.29 HU per year).

CONCLUSIONS

Opportunistic screening of mainly neurosurgical and oncologic patients in CT performed for indications other than densitometry allows for better risk assessment of imminent vertebral fractures than dedicated DXA.

KEY POINTS

• Opportunistic QCT predicts osteoporotic vertebral fractures better than DXA reference standard in mainly neurosurgical and oncologic patients. • More than every second patient (56%) with an incident vertebral fracture was misdiagnosed not having osteoporosis according to DXA. • Standard ACR QCT-cutoff values for osteoporosis (< 80 mg/cm 3 ) and osteopenia (≤ 120 mg/cm 3 ) can also be applied scanner independently in calibrated opportunistic QCT.

Bone mineral density measurements derived from dual-layer spectral CT enable opportunistic screening for osteoporosis

Objective

To investigate the in vivo applicability of non-contrast-enhanced hydroxyapatite (HA)-specific bone mineral density (BMD) measurements based on dual-layer CT (DLCT).

Methods

A spine phantom containing three artificial vertebral bodies with known HA densities was measured to obtain spectral data using DLCT and quantitative CT (QCT), simulating different patient positions and grades of obesity. BMD was calculated from virtual monoenergetic images at 50 and 200 keV. HA-specific BMD values of 174 vertebrae in 33 patients (66 ± 18 years; 33% women) were determined in non-contrast routine DLCT and compared with corresponding QCT-based BMD values.

Results

Examining the phantom, HA-specific BMD measurements were on a par with QCT measurements. In vivo measurements revealed strong correlations between DLCT and QCT (r = 0.987 [95% confidence interval, 0.963–1.000]; p < 0.001) and substantial agreement in a Bland–Altman plot.

Conclusion

DLCT-based HA-specific BMD measurements were comparable with QCT measurements in in vivo analyses. This suggests that opportunistic DLCT-based BMD measurements are an alternative to QCT, without requiring phantoms and specific protocols.

Key Points

• DLCT-based hydroxyapatite-specific BMD measurements show a substantial agreement with QCT-based BMD measurements in vivo.

• DLCT-based hydroxyapatite-specific measurements are on a par with QCT in spine phantom measurements.

• Opportunistic DLCT-based BMD measurements may be a feasible alternative for QCT, without requiring dedicated examination protocols or a phantom.

CT bone density analysis of low-impact proximal femur fractures using Hounsfield units

AIM

To quantify and compare changes in bone mineral density (BMD) via CT analysis in patients with and without spontaneous femoral fractures.

MATERIALS AND METHODS

Consecutive series of patients with CT imaging for spontaneous femoral fractures were compared to the age and gender matched controls. Bone density fixed region of interest measurements were obtained at the site of the fracture, proximally at the femoral head, and distally at the lesser trochanter in fracture patients and controls. Inter- and intrapatient comparisons were performed, including Chi-square and t-test analyses.

RESULTS

24 spontaneous fractures and 25 controls were analyzed with no significant differences in mean age, gender, or body mass index. There were differences in the bone density between the fracture and contralateral non-fracture sides at (p = 0.0001) and distal (p < 0.0001) to the fracture. Proximal and distal bone density differences existed between case fracture and control non-fracture sites (p < 0.0001, p = 0.0001), and between the case non-fracture and control non-fracture sites (p < 0.0001, p < 0.0001). The reliability for measurements was good to excellent proximally (ICC = 0.63-0.87), moderate to excellent at the fracture site (ICC = 0.43-0.78), and fair to good distal (ICC = 0.24-0.68) to the fracture site.

CONCLUSION

Patients with spontaneous femoral fractures exhibit lower bone density than the asymptomatic controls. Bone insufficiency is best demonstrated proximal or distal to, rather than at the fracture site.

Hemiarthroplasty or Total Hip Arthroplasty: Is There a Racial Bias in Treatment Selection for Femoral Neck Fractures?

Introduction

Hip fractures in the elderly individuals are associated with significant morbidity and mortality, and outcomes are directly related to prompt surgical intervention with either total hip arthroplasty (THA) or hemiarthroplasty. Minority hip fracture patients have increased delays to surgical intervention and poorer functional outcomes. This study explored racial biases in the surgical treatment decision between THA and hemiarthroplasty for displaced femoral neck fractures as well as racial disparities in postoperative complications, readmission rates, and 30-day mortality.

Methods

We retrospectively reviewed the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) from 2006 to 2014. Patients were identified using diagnosis code for transcervical femoral neck fractures and Current Procedural Terminology codes for THA or hemiarthroplasty. A multivariable regression analysis was conducted including race, demographic information, and medical comorbidities.

Results

Of 11 408 patients, race was recorded in 8538 individuals. Most patients were white (88.3%), followed by Hispanic (4.7%), African American (4.1%), and Asian/Native Hawaiian/Pacific Islander/American Indian/Alaska Native (2.9%). No differences were observed in the likelihood of receiving a THA versus hemiarthroplasty among racial groups. Only younger age and steroid use were independent risk factors for receiving a THA. Race was significantly associated with postoperative mortality (P = .014) and major postoperative complications for the Asian cohort (P = .013).

Discussion

The NSQIP data do not support a racial bias in the selection of patients for THA versus hemiarthroplasty. However, this study found racial disparities in postoperative mortality and complications. The reasons underlying the differences in postoperative outcomes are uncertain but may be the result of specific challenges to accessing care.

Conclusion

There was no racial bias in the treatment of femoral neck fractures. However, there were racial disparities in postoperative mortality and complication rates. Further research is warranted to elucidate the true causes of these observed disparities.

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade

PURPOSE

Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, although recommended in clinical guidelines, remains markedly underutilized. In contrast to DEXA, CT utilization is high and presents a valuable data source for opportunistic osteoporosis screening. The purpose of this study was to describe a method to simulate lumbar DEXA scores from routinely acquired CT studies using a machine-learning algorithm.

METHODS

Between January 2010 and September 2014, 610 CT studies of the abdomen and pelvis were used to develop spinal column and L1 to L4 multiclass segmentation. DEXA simulation training and validation used 1,843 pairs of CT studies accompanied by DEXA results obtained within a 6-month interval from the same individual. Machine learning-based regression was used to determine correlation between calculated grade (on the basis of vertebrae L1-L4) and DEXA t score.

RESULTS

Analysis of the t score equivalent, generated by the algorithm, revealed true positives in 1,144 patients, false positives in 92 patients, true negatives in 245 patients, and false negatives in 212 patients, resulting in an accuracy of 82%. Sensitivity for the detection of osteoporosis or osteopenia was 84.4% (95% confidence interval, 82.3%-86.2%), and specificity was 72.7% (95% confidence interval, 67.7%-77.2%).

CONCLUSIONS

The presented algorithm can identify osteoporosis and osteopenia with a high degree of accuracy (82%) and a small proportion of false positives. Efforts to cull greater information using machine-learning algorithms from pre-existing data have the potential to have a marked impact on population health efforts such as bone mineral density screening for osteoporosis, in which gaps in screening currently exist.

Digital Patient Education and Decision Aids

New technologies can do more than just digitize health information; they can support multimedia platforms for patient education and health decision support. Technology can simplify the way health decisions are made by offering quick access to a vast amount of information that can be tailored to specific populations. Digital tools can increase knowledge and assist consumers in comparing health care alternatives. They are well received by patients because of the myriad features that render them visually appealing and entertaining, including audiovisual and interactive elements. To be effective, however, digital tools must be evidence based and developed following quality standards.

DXA-equivalent quantification of bone mineral density using dual-layer spectral CT scout scans

OBJECTIVES

To develop and evaluate a method for areal bone mineral density (aBMD) measurement based on dual-layer spectral CT scout scans.

METHODS

A post-processing algorithm using a pair of 2D virtual mono-energetic scout images (VMSIs) was established in order to semi-automatically compute the aBMD at the spine similarly to DXA, using manual soft tissue segmentation, semi-automatic segmentation for the vertebrae, and automatic segmentation for the background. The method was assessed based on repetitive measurements of the standardized European Spine Phantom (ESP) using the standard scout scan tube current (30 mA) and other tube currents (10 to 200 mA), as well as using fat-equivalent extension rings simulating different patient habitus, and was compared to dual-energy X-ray absorptiometry (DXA). Moreover, the feasibility of the method was assessed in vivo in female patients.

RESULTS

Derived from standard scout scans, aBMD values measured with the proposed method significantly correlated with DXA measurements (r = 0.9925, p < 0.001), and mean accuracy (DXA, 4.12%; scout, 1.60%) and precision (DXA, 2.64%; scout, 2.03%) were comparable between the two methods. Moreover, aBMD values assessed at different tube currents did not differ significantly (p ≥ 0.20 for all), suggesting that the presented method could be applied to scout scans with different settings. Finally, data derived from sample patients were concordant with BMD values from a reference age-matched population.

CONCLUSIONS

Based on dual-layer spectral scout scans, aBMD measurements were fast and reliable and significantly correlated with the according DXA measurements in phantoms. Considering the number of CT acquisitions performed worldwide, this method could allow truly opportunistic osteoporosis screening.

KEY POINTS

• 2D scout scans (localizer radiographs) from a dual-layer spectral CT scanner, which are mandatory parts of a CT examination, can be used to automatically determine areal bone mineral density (aBMD) at the spine. • The presented method allowed fast (< 25 s/patient), semi-automatic, and reliable DXA-equivalent aBMD measurements for state-of-the-art DXA phantoms at different tube settings and for various patient habitus, as well as for sample patients. • Considering the number of CT scout scan acquisitions performed worldwide on a daily basis, the presented technique could enable truly opportunistic osteoporosis screening with DXA-equivalent metrics, without involving higher radiation exposure since it only processes existing data that is acquired during each CT scan.

Fracture Prediction by Computed Tomography and Finite Element Analysis: Current and Future Perspectives

PURPOSE OF REVIEW

This review critiques the ability of CT-based methods to predict incident hip and vertebral fractures.

RECENT FINDINGS

CT-based techniques with concurrent calibration all show strong associations with incident hip and vertebral fracture, predicting hip and vertebral fractures as well as, and sometimes better than, dual-energy X-ray absorptiometry areal biomass density (DXA aBMD). There is growing evidence for use of routine CT scans for bone health assessment. CT-based techniques provide a robust approach for osteoporosis diagnosis and fracture prediction. It remains to be seen if further technical advances will improve fracture prediction compared to DXA aBMD. Future work should include more standardization in CT analyses, establishment of treatment intervention thresholds, and more studies to determine whether routine CT scans can be efficiently used to expand the number of individuals who undergo evaluation for fracture risk.

Comparing the Intramedullary Nail and Extramedullary Fixation in Treatment of Unstable Intertrochanteric Fractures

Treatment options for unstable intertrochanteric fractures include intramedullary nail and extramedullary fixation, although evidence regarding the most appropriate treatment for such fractures remains controversial. Our hypothesis was that there would be no obvious differences in mortality rates, functional outcomes and complications between the two groups. We therefore conducted a meta-analysis to compare the relative advantages of intramedullary nail and extramedullary fixation. A total of 10 randomized controlled trials including only patients with unstable intertrochanteric fractures were included in the final analysis. We found that no statistically significant difference in one-year mortality was observed between the two groups (RR: 0.78, 95% CI: 0.55-1.10, p = 0.160). Analysis of exact p values from five included studies indicated that functional outcomes were markedly better for patients of the intramedullary nail group when compared with those of the extramedullary fixation group (p = 0.0028), although evidence remains controversial. Higher incidences of all complications were noted for extramedullary fixation (RR:1.48, 95% CI: 1.12-1.96, p = 0.006). However, no significant differences in implant-related complications were observed between the two groups (RR:1.20, 95% CI: 0.73-1.97, p = 0.475). Therefore, comparing with extramedullary fixation, the intramedullary nail method would be more reliable and should be encouraging for unstable intertrochanteric fractures.

Psychometric properties of belief measures about osteoporosis and its control

BACKGROUND AND PURPOSE

Osteoporosis (OP) is a chronic health condition with potentially serious consequences. Although preventive behaviors are important to control OP, many people do not engage in such behaviors. Although beliefs about preventive behaviors for OP influence such behaviors, we could not find psychometrically strong measures of such beliefs for use in planned research. Our initial study was done to assess the content validity, clarity, and internal consistency of belief measures regarding behaviors to control OP: perceived competence, perceived susceptibility, and perceived severity, based on relevant theories.

METHODS

Using a descriptive design, we recruited five clinicians to rate proposed measures for content validity. We also recruited fifty-one older adults from five different counties in a Midwestern state to respond to proposed measures so we could assess clarity and internal consistency reliability.

RESULTS

The content validity indices of items varied from 0.60-1.00. The content validity indices of scales varied from 0.73-1.00. For reliability, the final Cronbach's alphas were 0.79-0.96.

CONCLUSIONS

Addressing a gap in research, we have documented good psychometric properties of belief measures regarding OP and its control. In describing our search for psychometrically sound measures, we have raised issues for future researchers to consider prior to adopting use of existing measures. Nurses can use these measures to assess and address the accuracy of patients' beliefs either individually or in groups. Researchers can use these measures to examine whether or not psycho-educational interventions influence beliefs about OP and its control.

Bone mineral density measurements in vertebral specimens and phantoms using dual-layer spectral computed tomography

To assess whether phantomless calcium-hydroxyapatite (HA) specific bone mineral density (BMD) measurements with dual-layer spectral computed tomography are accurate in phantoms and vertebral specimens. Ex-vivo human vertebrae (n = 13) and a phantom containing different known HA concentrations were placed in a semi-anthropomorphic abdomen phantom with different extension rings simulating different degrees of obesity. Phantomless dual-layer spectral CT was performed at different tube current settings (500, 250, 125 and 50 mAs). HA-specific BMD was derived from spectral-based virtual monoenergetic images at 50 keV and 200 keV. Values were compared to the HA concentrations of the phantoms and conventional qCT measurements using a reference phantom, respectively. Above 125 mAs, errors for phantom measurements ranged between -1.3% to 4.8%, based on spectral information. In vertebral specimens, high correlations were found between BMD values assessed with spectral CT and conventional qCT (r ranging between 0.96 and 0.99; p < 0.001 for all) with different extension rings, and a high agreement was found in Bland Altman plots. Different degrees of obesity did not have a significant influence on measurements (P > 0.05 for all). These results suggest a high validity of HA-specific BMD measurements based on dual-layer spectral CT examinations in setups simulating different degrees of obesity without the need for a reference phantom, thus demonstrating their feasibility in clinical routine.

Predicting Future Hip Fractures on Routine Abdominal CT Using Opportunistic Osteoporosis Screening Measures: A Matched Case-Control Study

OBJECTIVE

Hip fracture is a major consequence of low bone mineral density, which is treatable but underdiagnosed. The purpose of this case-control study is to determine whether lumbar vertebral trabecular attenuation, vertebral compression fractures, and femoral neck T scores readily derived from abdominopelvic CT scans obtained for various indications are associated with future hip fragility fracture.

MATERIALS AND METHODS

A cohort of 204 patients with hip fracture (130 women and 74 men; mean age, 74.3 years) who had undergone abdominopelvic CT before fracture occurred (mean interval, 24.8 months) was compared with an age- and sex-matched control cohort without hip fracture. L1 trabecular attenuation, vertebral compression fractures of grades 2 and 3, and femoral neck T scores derived from asynchronous quantitative CT were recorded. The presence of one or more clinical risk factor for fracture was also recorded. Multivariate logistic regression models were used to determine the association of each measurement with the occurrence of hip fracture.

RESULTS

The mean L1 trabecular attenuation value, the presence of one or more vertebral compression fracture, and CT-derived femoral neck T scores were all significantly different in patients with hip fracture versus control subjects (p < 0.01). Logistic regression models showed a significant association of all measurements with hip fracture outcome after adjustments were made for age, sex, and the presence of one or more clinical risk factor. L1 trabecular attenuation and CT-derived femoral neck T scores showed moderate accuracy in differentiating case and control patients (AUC, 0.70 and 0.78, respectively).

CONCLUSION

L1 trabecular attenuation, CT-derived femoral neck T scores, and the presence of at least one vertebral compression fracture on CT are all associated with future hip fragility fracture in adults undergoing routine abdominopelvic CT for a variety of conditions.

Interprofessional care for patients with osteoporosis in a continuing care retirement community

OBJECTIVES

To assess the quality of care provided to patients with osteoporosis in a continuing care retirement community (CCRC) after implementation of an interprofessional osteoporosis clinic (OPC). Specifically, quality measures were evaluated, including dual-emission X-ray absorptiometry (DXA) screening, calcium and vitamin D supplementation, and prescription treatment of osteoporosis and low bone mass in an ambulatory independent living community.

SETTING

Large family medicine teaching practice that provides primary care for residents in one main practice, 5 rural satellite practices, and 2 CCRCs. An interprofessional OPC was developed at the main practice in 2005. Patients at all of the organization's sites could be referred to the main practice for osteoporosis management. A needs assessment conducted at one of the CCRCs in 2011 revealed that rates of screening and treatment were suboptimal for its residents despite availability of an off-site OPC.

PRACTICE INNOVATION

In 2012, a new interprofessional OPC including a physician, medical assistant, and pharmacist was replicated on-site at the CCRC so that residents had access to this service within their medical home.

EVALUATION

Quality measures were evaluated after implementation of the team-based OPC on-site at a CCRC and included: 1) DXA screening; 2) calcium and vitamin D supplementation; and 3) prescription treatment of osteoporosis and low bone mass.

RESULTS

Twenty-nine patients were seen in the new OPC from January 2012 to August 2013. Ninety-three percent had appropriate DXA testing after OPC implementation. Patients accepted pharmacist recommendations regarding calcium and vitamin D supplementation 90% and 86% of the time, respectively. All but 4 patients received appropriate treatment for osteoporosis or low bone mass.

CONCLUSION

Providing a team-based OPC on site in a CCRC improved quality measures for screening and treatment of osteoporosis and low bone mass.

Osteoporosis Preventive Practice Between Veteran and Nonveteran Older Adults: Findings From Patient-Reported Data

BACKGROUND

Veterans are prone to bone-related illnesses due to multiple risk factors such as prior injuries. The aim of this study was to compare trends in osteoporosis preventive practices between veteran and nonveteran older adults.

METHODS

This was a secondary data analysis using selected baseline data and discussion postings from an online bone health trial including participants (N = 866) recruited from My HealtheVet (MHV) and SeniorNet (SN). Data were analyzed using descriptive statistics, parametric statistics, and content analysis.

FINDINGS

Overall, MHV participants were younger and included more men than SN participants. However, they reported higher rates of bone health issues, spent less time exercising, took fewer calcium and vitamin D supplements, and were less likely to discuss bone health with their care providers. More MHV participants discussed pain and disability as barriers to bone health behaviors and fear of deteriorating health as motivators. In addition, more MHV participants found that participating in the original study was helpful for changing health behaviors.

CONCLUSION

Overall, the findings suggest a disparity in bone health between veterans and nonveterans and a significant potential for using eHealth programs for veterans.

The Effects of an Online Theory-Based Bone Health Program for Older Adults

An estimated 10 million Americans age 50 and older have osteoporosis, and many experience associated fractures. Although several interventions have been shown to be effective in preventing osteoporosis, their impact on bone health among older adults was limited. The aim of this study was, therefore, to examine the effects of a theory-based online bone health program (Bone Power program) for a large number of older adults. The 8-week program included learning modules, discussion boards, and other resources. Participants ( N = 866; M age = 62.5 years) were recruited online and randomized into a Bone Power or control group. At the end of the intervention, the Bone Power group showed significantly greater improvement over the control group in osteoporosis knowledge, self-efficacy/outcome expectations for calcium intake and exercise, and calcium intake and exercise behaviors. This study's findings suggest that online health programs can be effective in improving older adults' knowledge, beliefs, and health behaviors.

Underuse and Overuse of Osteoporosis Screening in a Regional Health System: a Retrospective Cohort Study

BACKGROUND

The United States Preventive Services Task Force (USPSTF) recommends screening for osteoporosis with dual-energy x-ray absorptiometry (DXA) for women aged ≥ 65 years and younger women with increased risk. "Choosing Wisely" initiatives advise avoiding DXA screening in women younger than 65 years without osteoporosis risk factors.

OBJECTIVE

We aimed to determine the extent to which DXA screening is used in accordance with USPSTF recommendations within a regional health system.

DESIGN

This was a retrospective longitudinal cohort study within 13 primary care clinics in the Sacramento, CA region.

PATIENTS

The study included 50,995 women aged 40-85 years without prior osteoporosis screening, diagnosis, or treatment attending primary care visits from 2006 to 2012, observed for a mean of 4.4 years.

MAIN MEASURES

We examined incidence of DXA screening. Covariates included age, race/ethnicity, and osteoporosis risk factors (body mass index < 20, glucocorticoid use, secondary osteoporosis, prior high-risk facture, rheumatoid arthritis, alcohol abuse, and current smoking).

KEY RESULTS

Among previously unscreened women for whom the USPSTF recommends screening, 7-year cumulative incidence of DXA screening was 58.8 % among women aged 60-64 years with ≥ 1 risk factor (95 % CI: 51.9-65.8 %), 57.8 % for women aged 65-74 years (95 % CI: 55.6-60.0 %), and 42.7 % for women aged ≥ 75 years (95 % CI: 38.7-46.7 %). Among women for whom the USPSTF does not recommend screening, 7-year cumulative incidence was 45.5 % among women aged 50-59 years (95 % CI 44.1-46.9 %) and 58.6 % among women aged 60-64 years without risk factors (95 % CI 55.9-61.4 %).

CONCLUSIONS

DXA screening was underused in women at increased fracture risk, including women aged ≥ 65 years. Meanwhile, DXA screening was common among women at low fracture risk, such as younger women without osteoporosis risk factors. Interventions may be needed to augment the value of population screening for osteoporosis.