Using opportunistic screening with abdominal CT to identify osteoporosis and osteopenia in patients with diabetes

Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis

Computed tomography (CT) offers advanced biomedical imaging of the body and is broadly utilized for clinical diagnosis. Traditionally, clinical CT scans have not been used for volumetric bone mineral density (vBMD) assessment; however, computational advances can now leverage clinically obtained CT data for the secondary analysis of bone, known as opportunistic CT analysis. Initial applications focused on using clinically acquired CT scans for secondary osteoporosis screening, but opportunistic CT analysis can also be applied to answer research questions related to vBMD changes in response to various disease states. There are several considerations for opportunistic CT analysis, including scan acquisition, contrast enhancement, the internal calibration technique, and bone segmentation, but there remains no consensus on applying these methods. These factors may influence vBMD measures and therefore the robustness of the opportunistic CT analysis. Further research and standardization efforts are needed to establish a consensus and optimize the application of opportunistic CT analysis for accurate and reliable assessment of vBMD in clinical and research settings. This review summarizes the current state of opportunistic CT analysis, highlighting its potential and addressing the associated challenges.

Opportunistic screening for osteoporosis by abdominal CT in a British population

BACKGROUND

It has previously been shown that CT scans performed for other indications can be used to identify patients with osteoporosis. This has not yet been tested in a British population. We sought to evaluate the use of vertebral CT attenuation measures for predicting osteoporosis in a British cohort, using dual-energy X-ray absorptiometry (DEXA) as a reference standard.

METHODS

Patients who underwent abdominal CT in 2018 and concomitantly underwent DEXA within a six-month interval were retrospectively included. CT attenuation values in Hounsfield units (HU) were measured by placement of a region-of-interest at the central portion of the L1 vertebral body and then compared to their corresponding DEXA score. Receiver operating characteristic (ROC) curves were generated to evaluate the performance of a logistic regression model and to determine sensitivity and specificity thresholds.

RESULTS

536 patients (394 females, mean age 65.8) were included, of which 174 had DEXA-defined osteoporosis. L1 attenuation measures were significantly different (p < 0.01) between the three DEXA-defined groups of osteoporosis (118 HU), osteopenia (143 HU) and normal bone density (178 HU). The area under the ROC curve was 0.74 (95% CI 0.69-0.78). A threshold of 169 HU was 90% sensitive, and a threshold of 104 HU was 90% specific for diagnosing osteoporosis.

CONCLUSIONS

Routine abdominal CT can be used to opportunistically screen for osteoporosis without additional cost or radiation exposure. The thresholds identified in this study are comparable with previous studies in other populations. We recommend radiologists engage with primary care and rheumatology providers to determine appropriate cut-off values for further investigation.

Opportunistic Screening Techniques for Analysis of CT Scans

PURPOSE OF REVIEW

Opportunistic screening is a combination of techniques to identify subjects of high risk for osteoporotic fracture using routine clinical CT scans prescribed for diagnoses unrelated to osteoporosis. The two main components are automated detection of vertebral fractures and measurement of bone mineral density (BMD) in CT scans, in which a phantom for calibration of CT to BMD values is not used. This review describes the particular challenges of opportunistic screening and provides an overview and comparison of current techniques used for opportunistic screening. The review further outlines the performance of opportunistic screening.

RECENT FINDINGS

A wide range of technologies for the automatic detection of vertebral fractures have been developed and successfully validated. Most of them are based on artificial intelligence algorithms. The automated differentiation of osteoporotic from traumatic fractures and vertebral deformities unrelated to osteoporosis, the grading of vertebral fracture severity, and the detection of mild vertebral fractures is still problematic. The accuracy of automated fracture detection compared to classical radiological semi-quantitative Genant scoring is about 80%. Accuracy errors of alternative BMD calibration methods compared to simultaneous phantom-based calibration used in standard quantitative CT (QCT) range from below 5% to about 10%. The impact of contrast agents, frequently administered in clinical CT on the determination of BMD and on fracture risk determination is still controversial. Opportunistic screening, the identification of vertebral fracture and the measurement of BMD using clinical routine CT scans, is feasible but corresponding techniques still need to be integrated into the clinical workflow and further validated with respect to the prediction of fracture risk.

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.

Screening for osteoporosis using L1 vertebral density on abdominal CT in an Australian population

AIM

To assess the utility of osteoporosis screening using abdominal computed tomography (CT) versus dual-energy X-ray absorptiometry (DXA) T-scores as the reference.

MATERIALS AND METHODS

Patients ≥30 years undergoing abdominal CT and DXA within 12 months were assessed retrospectively. Bone mineral density (BMD) was measured using axial CT attenuation at L1, correlating with DXA T-scores. Sensitivity, specificity, area under the curve (AUC), and odds ratio (OR) were calculated.

RESULTS

The study cohort comprised 407 CT-DXA pairs (58.2% women). The prevalence of osteoporosis was 11.8%. L1 density and T-score were significantly correlated in both women (r=0.35, p<0.001) and men (r=0.15, p=0.04). The AUC to distinguish osteoporosis from osteopenia and normal BMD was 0.64 (95% CI: 0.56-0.71). In women, a threshold of 190 HU detected T-scores ≤ -2.5 with a negative predictive value (NPV) of 94.4% (OR=4.4, p<0.01). In the entire cohort, a threshold of 180 HU detected T-scores ≤ -2.5 with a NPV of 96.2% (OR=4.7, p<0.01).

CONCLUSIONS

CT L1 attenuation correlates with L1 DXA T-scores. Density values < 190 and 180 HU increased the probability of an osteoporosis diagnosis in Australian women and the overall cohort, respectively. Opportunistic screening for osteoporosis using abdominal CT is feasible, enabling identification of at-risk subjects for formal DXA imaging, thereby improving treatment initiation and reducing fracture risk.

Bone mineral density alteration in obstructive sleep apnea by derived computed tomography screening

The association between obstructive sleep apnea (OSA) and bone mineral density (BMD) is poorly elucidated and has contradictory findings. Abdominal computed tomography (CT) for other indications can provide a valuable opportunity for osteoporosis screening. Thus, we retrospectively explored the association between OSA and BMD by examining abdominal CT vertebrae images for a multitude of conditions and indications. We included 315 subjects (174 with OSA and 141 without OSA) who performed at least two CT scans (under similar settings). Both groups had a similar duration between the first and second CT scans of 3.6 years. BMD decreased in those with OSA and increased age. A multivariate linear regression indicated that OSA is associated with BMD alterations after controlling for age, gender, and cardiovascular diseases. Here, we report that OSA is associated with BMD alterations. Further studies are required to untangle the complex affect of OSA on BMD and the possible clinical implications of vertebra-depressed or femoral neck fractures.

Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150,000 routine computed tomography scans

OBJECTIVE

Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of vertebral fractures and osteoporosis in a large database of lumbar or abdominal CT scans.

MATERIAL AND METHODS

Data were analyzed from CT scans obtained in 35 hospitals from patients aged 60 years and more and stored in a Picture Archiving and Communication System in Assistance-Publique-Hôpitaux de Paris, from 2007 to 2013. Dedicated software analyzed the presence of at least 1 vertebral fracture (VF), and measured Hounsfield Units (HU) in lumbar vertebrae. A simulated T-score was calculated.

RESULTS

Data were analyzed from 152 268 patients (73.2 ± 9.07 years). Success rates for VF assessment and HU measurements were 82 and 87% respectively. Prevalence of VF was 24.5% and increased with age. Areas under the receiver operating characteristic curves for the detection of VF were 0.61 and 0.62 for mean HU of lumbar vertebrae and L1 HU, respectively. In patients without VF, HU decreased with age, similarly in males and females. The prevalence of osteoporosis (sT-score ≤ - 2.5) was 23.8% and 36.5% in patients without and with VFs respectively.

CONCLUSION

Opportunistic screening in patients 60 years and older having lumbar or abdominal CT scans is feasible at large scale to screen vertebral fractures and osteoporosis.

Advancements in Osteoporosis Imaging, Screening, and Study of Disease Etiology

PURPOSE OF REVIEW

The purpose of this review is to inform researchers and clinicians with the most recent imaging techniques that are employed (1) to opportunistically screen for osteoporosis and (2) to provide a better understanding into the disease etiology of osteoporosis.

RECENT FINDINGS

Phantomless calibration techniques for computed tomography (CT) may pave the way for better opportunistic osteoporosis screening and the retroactive analysis of imaging data. Additionally, hardware advances are enabling new applications of dual-energy CT and cone-beam CT to the study of bone. Advances in MRI sequences are also improving imaging evaluation of bone properties. Finally, the application of image registration techniques is enabling new uses of imaging to investigate soft tissue-bone interactions as well as bone turnover. While DXA remains the most prominent imaging tool for osteoporosis diagnosis, new imaging techniques are becoming more widely available and providing additional information to inform clinical decision-making.

Automated assessment of longitudinal biomarker changes at abdominal CT: correlation with subsequent cardiovascular events in an asymptomatic adult screening cohort

BACKGROUND

Cardiovascular (CV) disease is a major public health concern, and automated methods can potentially capture relevant longitudinal changes on CT for opportunistic CV screening purposes.

METHODS

Fully-automated and validated algorithms that quantify abdominal fat, muscle, bone, liver, and aortic calcium were retrospectively applied to a longitudinal adult screening cohort undergoing serial non-contrast CT examination between 2005 and 2016. Downstream major adverse events (MI/CVA/CHF/death) were identified via algorithmic EHR search. Logistic regression, ROC curve, and Cox survival analyses assessed for associations between changes in CT variables and adverse events.

RESULTS

Final cohort included 1949 adults (942 M/1007F; mean age, 56.2 ± 6.2 years at initial CT). Mean interval between CT scans was 5.8 ± 2.0 years. Mean clinical follow-up interval from initial CT was 10.4 ± 2.7 years. Major CV events occurred after follow-up CT in 230 total subjects (11.8%). Mean change in aortic calcium Agatston score was significantly higher in CV(+) cohort (591.6 ± 1095.3 vs. 261.1 ± 764.3), as was annualized Agatston change (120.5 ± 263.6 vs. 46.7 ± 143.9) (p < 0.001 for both). 5-year area under the ROC curve (AUC) for Agatston change was 0.611. Hazard ratio for Agatston score change > 500 was 2.8 (95% CI 1.5-4.0) relative to < 500. Agatston score change was the only significant univariate CT biomarker in the survival analysis. Changes in fat and bone measures added no meaningful prediction.

CONCLUSION

Interval change in automated CT-based abdominal aortic calcium load represents a promising predictive longitudinal tool for assessing cardiovascular and mortality risks. Changes in other body composition measures were less predictive of adverse events.

Use of routine computed tomography scans for detecting osteoporosis in thoracolumbar vertebral bodies

OBJECTIVE

This study observed the distribution of CT attenuation values for T10-L3 vertebral bodies and derived the Hounsfield unit (HU) thresholds using the quantitative computed tomography (QCT) as a reference to predict osteoporosis and normal bone density.

METHODS

We included 482 subjects who were scheduled to undergo CT lung cancer screening and pulmonary nodule follow-up from May 2015 to February 2019. The subjects were scanned with the calibration phantom beneath the back while performing a chest CT scan. The volumetric bone mineral density (vBMD) and CT attenuation values of T10-L3 vertebral bodies were measured, and the correlation between the two measurements was analyzed. Receiver operator characteristic (ROC) curves were generated to determine diagnostic optimal thresholds.

RESULTS

A total of 2716 vertebral bodies of 457 participants were measured after exclusion screening. CT attenuation value of each plane's vertebral body showed a strong correlation with vBMD. The optimal threshold of > 141 HU was 93.5% sensitive and 86.1% specific for the recognition of normal BMD. The optimal threshold of < 102.4 HU was 96.9% specific and 82.1% sensitive for distinguishing osteoporosis from osteopenia and normal BMD. The average CT attenuation values of vertebral bodies with compressed and normal morphology were 108.9 ± 20.6 and 136.8 ± 32.2 HU, respectively.

CONCLUSION

Sagittal reconstruction of the thoracic vertebrae using routine thoracic CT image combined with CT attenuation value measurements of the spine is valuable for predicting bone mineral density in high-risk populations. The mean CT attenuation values of the vertebral bodies with vertebral compression appearance were lower than that of normal vertebral shape.