Diagnostic model for distinguishing fresh or old osteoporotic vertebral compression fractures based on modified computed tomography window: a retrospective cohort study

PURPOSE

To develop and validate a Computed Tomography (CT) -based nomogram for distinguishing fresh from old osteoporotic vertebral compression fractures (OVCFs), with magnetic resonance imaging (MRI) as the reference standard.

METHODS

In this retrospective study, OVCF patients from The First Affiliated Hospital of Chongqing Medical University (August 2022-December 2023) were analyzed. Modified CT window parameters (width: 400; level: 200) were applied to quantify vertebral features, including CT values, height reduction, endplate integrity, trabecular sparsity, Schmorl's nodes, and high-density shadows. Predictive variables were selected via univariate and multivariate logistic regression, followed by nomogram construction. Model performance was assessed using receiver operating characteristic (ROC) curves (area under the curve, AUC), calibration plots, Hosmer-Lemeshow testing, and decision curve analysis (DCA). 10-fold cross-validation was used to evaluate the generalization performance of the model.

RESULTS

The nomogram incorporated seven imaging biomarkers, achieving AUCs of 0.941 (training cohort) and 0.974 (validation cohort). The calibration accuracy was assessed using the Hosmer-Lemeshow test (χ²=3.30, P = 0.95). DCA demonstrating significant clinical utility across probability thresholds. In cross-validation, the mean AUC on the validation sets was 0.911 ± 0.080 (mean ± standard deviation).

CONCLUSION

The proposed CT-based nomogram achieved adequate performance in identifying fresh and old OVCFs.

CT- and MRI-based endplate osteoporosis status assessment for predicting adjacent vertebral fractures after thoracolumbar percutaneous kyphoplasty: a matched comparison study

PURPOSE

This study aimed to investigate the predictive value of adjacent vertebral endplate osteoporotic status for adjacent vertebral fracture (AVF) after percutaneous kyphoplasty (PKP) for treating osteoporotic vertebral compression fracture (OVCF) and compared it with vertebral cancellous bone.

METHODS

This study retrospectively analyzed consecutive patients with OVCF who underwent PKP. Participants were categorized into the study group (49 patients with AVF) and the control group (49 matched patients without AVF). The vertebral Hounsfield units (VHU), endplate HU (EHU), vertebral bone quality (VBQ), and endplate bone quality (EBQ) scores were calculated using preoperative CT and MRI. To identify the predictive ability of the HU and EBQ scores, multivariable logistic regression and receiver operating characteristic (ROC) curve analyses were conducted.

RESULTS

VHU (p = 0.006), EHU proximal to the fracture segment (pEHU) (p = 0.001), average level of EHUs (aEHU) (p = 0.001), and EBQ score proximal to the fracture segment (pEBQ score) (p = 0.034) of the adjacent fractured vertebrae (AFV) in the study group were significantly different from the control study. The lower VHU (OR = 0.981, p = 0.007), pEHU (OR = 0.984, p < 0.001), lower aEHU (OR = 0.986, p = 0.002), and higher pEBQ score (OR = 1.002, p = 0.050) of the AFV were determined as the independent predictors for AVF. The ROC curve analysis revealed that the adjusted area under the curve (AUC) for the pEHU was 0.756 and the most suitable threshold was 254.87 (sensitivity: 69.4% and specificity: 77.6%), indicating that the predictive performance of pEHU was relatively higher. Further, the adjusted AUCs for the VHU, aEHU, and pEBQ scores were 0.703, 0.699, and 0.666, respectively.

CONCLUSION

This study revealed that the endplate osteoporosis status predicted AVF better than the vertebral cancellous bone and the predictive performance of pEHU was relatively higher.

CT-Based Body Composition Measures and Systemic Disease: A Population-Level Analysis Using Artificial Intelligence Tools in Over 100,000 Patients

BACKGROUND. CT-based abdominal body composition measures have shown associations with important health outcomes. Advances in artificial intelligence (AI) now allow deployment of tools that measure body composition in large patient populations. OBJECTIVE. The purpose of this study was to assess associations of age, sex, and common systemic diseases with CT-based body composition measurements derived using a panel of fully automated AI tools in a population-level adult patient sample. METHODS. This retrospective study included 140,606 adult patients (67,613 men and 72,993 women; mean age, 53.1 ± 17.6 [SD] years) who underwent abdominal CT at a single academic institution between January 1, 2000, and February 28, 2021. CT examinations were not restricted on the basis of patient setting, clinical indication, or IV contrast media use. Thirteen fully automated AI body composition tools quantifying liver, spleen, and kidney volume and attenuation; vertebral trabecular attenuation; skeletal muscle area and attenuation; and abdominal fat area and attenuation were applied to each patient's first available abdominal CT examination. EHR review was performed to identify common systemic diseases, including cancer, cardiovascular disease (CVD), diabetes mellitus (DM), and cirrhosis, on the basis of relevant ICD-10 codes; 64,789 patients (46.1%) had at least one systemic disease diagnosed. Multiple linear regression models were performed for the 118,141 patients (84.0%) with no systemic disease or a single systemic disease, to assess age, sex, and the presence of systemic disease as predictors of body composition measures; effect sizes were characterized using the unstandardized regression coefficient B. RESULTS. Multiple linear regression models using age, sex, and systemic disease as predictors were overall significant for all 13 body composition measures (all p < .001) with variable goodness of fit (R2 = 0.03-0.43 across models). In the models, age was predictive of all 13 body composition measures; sex, 12 measures; cancer, nine measures; CVD, 11 measures; DM, 13 measures; and cirrhosis, 12 measures (all p < .05). CONCLUSION. Age, sex, and the presence of common systemic diseases were predictors of AI-derived CT-based body composition measures. CLINICAL IMPACT. An understanding of the identified associations with common systemic diseases will be critical for establishing normative reference ranges as CT-based AI body composition tools are developed for clinical use.

Opportunistic Hounsfield units measurements on computed tomography scans can predict the number of prevalent vertebral compression fractures in older adults

PURPOSE

There is a growing interest in using computed tomography (CT) scans to opportunistically assess bone mineral density via Hounsfield units (HU). Previous studies have shown lower HU in patients with vertebral compression fractures (VCFs) and that HU can predict pre-existing VCFs. This study evaluated whether HU from CT scans can predict the number of prevalent VCFs.

METHODS

We studied 353 patients (199 females) aged 58 and older who underwent thorax-abdomen-pelvis or thoracic-lumbar CT scans. HU were measured at T11 and L1. Inter- and intra-observer agreement measuring HU was assessed. We compared mean HU values using the T-test, performed Spearman's correlation between HU and VCFs, and conducted logistic and linear regression analyses to determine the independent effect of sex, age, and HU on the presence and number of VCFs.

RESULTS

The median age was 73 years. Ninety-eight patients had at least one VCF, with 46 having one and 52 having two or more VCFs. Inter- and intra-observer agreement assessing HU was excellent (ICC = 0.98 and 0.99 respectively). Mean HU were significantly lower in patients with VCFs (91.14 ± 39.33) than in patients without VCFs (145.03 ± 41.07, p < 0.01). HU negatively correlated with the number of VCFs (r = - 0.54, p < 0.01). Logistic regression showed that age (p < 0.01) and HU (p < 0.01) predicted the presence of VCFs. Linear regression found that HU independently predicted the number of VCFs (p < 0.01), while age (p = 0.67) and sex (p = 0.12) did not.

CONCLUSION

HU from CT scans can predict the presence and number of VCFs.

Validation of a Deep Learning Tool for Detection of Incidental Vertebral Compression Fractures

OBJECTIVE

This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.

METHODS

This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old. Images were obtained from 2 teleradiology companies in France and United States and were processed by CINA-VCF v1.0, a deep learning algorithm designed for VCF detection. Ground truth was established by majority consensus across 3 board-certified radiologists. Overall performance of CINA-VCF was evaluated, as well as subset analyses based on imaging acquisition parameters, baseline patient characteristics, and VCF severity. A subgroup was also analyzed and compared with available clinical radiology reports.

RESULTS

Four hundred seventy-four CT scans were included in this study, comprising 166 (35.0%) positive and 308 (65.0%) negative VCF cases. CINA-VCF demonstrated an area under the curve (AUC) of 0.97 (95% CI: 0.96-0.99), accuracy of 93.7% (95% CI: 91.1%-95.7%), sensitivity of 95.2% (95% CI: 90.7%-97.9%), and specificity of 92.9% (95% CI: 89.4%-96.5%). Subset analysis based on VCF severity resulted in a specificity of 94.2% (95% CI: 90.9%-96.6%) for grade 0 negative cases and a specificity of 64.3% (95% CI: 35.1%-87.2%) for grade 1 negative cases. For grades 2 and 3 positive cases, sensitivity was 89.7% (95% CI: 79.9%-95.8%) and 99.0% (95% CI: 94.4%-100.0%), respectively.

CONCLUSIONS

CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.

Accelerated fusion dynamics by recombinant human bone morphogenetic protein-2 following transforaminal lumbar interbody fusion, particularly in osteoporotic conditions

BACKGROUND CONTEXT

Early fusion is crucial in interbody procedures to minimize mechanical complications resulting from delayed union, especially for patients with osteoporosis. Bone morphogenetic proteins (BMPs) are used in spinal fusion procedures; however, limited evaluation exists regarding time-to-fusion for BMP use, particularly in patients with osteoporosis.

PURPOSE

To evaluate the difference in time-to-fusion after single-level transforaminal lumbar interbody fusion (TLIF) surgery between recombinant human bone morphogenetic protein-2 (rhBMP-2) usage and nonusage groups according to bone density.

STUDY DESIGN

Retrospective single-center cohort study.

PATIENT SAMPLE

This study enrolled 132 patients (mean age, 65.25±8.66; male patients, 40.9%) who underwent single-level TLIF for degenerative disorders between February 2012 and December 2021, with pre- and postoperative computed tomography (CT).

OUTCOME MEASURE

The interbody fusion mass and bone graft status on postoperative CT scans was obtained annually, and time-to-fusion was recorded for each patient.

METHODS

The patients were divided into 2 groups based on rhBMP-2 use during the interbody fusion procedure. Patients were further divided into osteoporosis, osteopenia, and normal groups based on preoperative L1 vertebral body attenuation values, using cutoffs of 90 and 120 Hounsfield units. It was strictly defined that fusion is considered complete when a trabecular bone bridge was formed, and therefore, the time-to-fusion was measured in years. Time-to-fusion was statistically compared between BMP group and non-BMP groups, followed by further comparison according to bone density.

RESULTS

The time-to-fusion differed significantly between BMP and non-BMP groups, with half of the patients achieving fusion within 2.5 years in the BMP group compared with 4 years in the non-BMP group (p<.001). The fusion rate varied based on bone density, with the maximum difference observed in the osteoporosis group, when half of the patients achieved fusion within 3 years in the BMP group compared to 5 years in the non-BMP group (p<.001). Subgroup analysis was conducted, revealing no significant associations between time-to-fusion and factors known to influence the fusion process, including age, gender, medical history, smoking and alcohol use, and medication history, except for rh-BMP2 use and bone density.

CONCLUSIONS

RhBMP-2 usage significantly reduced time-to-fusion in single-level TLIF, especially in patients with osteoporosis.

LEVEL OF EVIDENCE

Level III.

Trabecular Attenuation of L1 in Adult Patients with Multiple Myeloma: An Observational Study on Low-Dose CT Images

BACKGROUND

The aim of this study was to evaluate the impact of trabecular attenuation of the L1 vertebral body in low-dose CT in adult patients with multiple myeloma (MM), smoldering multiple myeloma (SMM), and monoclonal gammopathy of undetermined significance (MGUS).

MATERIALS AND METHODS

The study population consisted of 22 patients with MGUS and 51 consecutive patients with newly diagnosed MM (SMM, n = 21; symptomatic MM, n = 36). CT scans were conducted using a 128-slice CT scanner (Somatom go.Top, Siemens, Munich, Germany). Low-dose whole-body CT scans were performed at a single time point for each patient. Trabecular bone density values were obtained by defining regions of interest on non-contrast images at the level of L1 vertebra. A threshold of p = 0.05 was applied to determine statistical significance.

RESULTS

The median Hounsfield unit (HU) value in patients with MGUS, SMM, and MM was 148 HU (range 81-190), 130 HU (range 93-193), and 92 HU (range 26-190), respectively, with a statistically significant difference between the groups (p = 0.0015). Patients with HU values ≤ 92 had lower progression-free survival with statistically significant differences compared to the group with HU values > 92 (p < 0.0499).

CONCLUSIONS

This is the earliest evidence of the importance of evaluating L1 attenuation values in low-dose CT images in patients with MGUS, SMM, and MM. Further prospective studies could contribute to reinforcing these results and exploring the clinical applicability and generalization of L1 attenuation values in low-dose whole-body CT scans in routine clinical practice.

The influence of proliferative tissue on Hounsfield unit and its correlation with BMD in middle-aged and elderly patients with lumbar degenerative diseases

PURPOSE

The difference of Hounsfield Unit (HU) value in different regions of L3 vertebra in middle-aged and elderly patients with lumbar degeneration diseases (LDD) was analyzed. To investigate the influence of proliferative tissue on HU value of cancellous bone and its correlation with bone mineral density (BMD).

METHODS

The medical records of middle-aged and elderly patients with LDD in our hospital from December 2020 to December 2023 were retrospectively analyzed. The patients were divided into osteophyte group and no-osteophyte group according to the presence or absence of osteophyte formation on lumbar spine X-ray. In osteophyte group, cancellous bone HU value, containing cortical bone overall HU value and containing osteophyte overall HU value in L3 vertebra were measured on the lumbar CT cross-section. In no-osteophyte group, only the cancellous bone HU value and the containing cortical bone overall HU value were measured. Differences in HU value in different regions of the L3 vertebral body were compared within and between groups of middle-aged and elderly patients with LDD, respectively. To investigate its effect on cancellous bone HU measurements and to do a correlation analysis with patients' BMD.

RESULTS

A total of 115 patients with LDD were included in this study, including 65 males and 50 females, with an average age of 67.83 ± 6.59 years. The results of the study showed no statistical differences in age (P = 0.15), gender (P = 0.57), smoking (P = 0.88), drinking history (P = 0.76), medical history (P > 0.05) and BMI(P = 0.29) between the two groups. In osteophyte group, the mean cancellous bone HU value was 98.00 ± 25.50 HU, the containing cortical bone overall HU value was 189.02 ± 46.18 HU, and the containing osteophyte overall HU value was 232.69 ± 56.01 HU. The overall HU values containing cortical bone and containing osteophyte were significantly higher than cancellous bone HU value (P < 0.001). In no-osteophyte group, the mean cancellous bone HU value was 102.04 ± 19.64 HU, and the containing cortical bone overall HU value was 175.00 ± 28.97 HU, which was statistically significantly different (P < 0.001). There was no significant difference in cancellous bone HU value and the containing cortical bone overall HU value between the two groups (P > 0.05). The results of the Pearson correlation analysis showed a significant correlation between the cancellous bone HU value of the L3 vertebrae and the QCT BMD value of the patients (r = 0.95, P < 0.001). However, there was no significant correlation between containing cortical bone overall HU value and containing osteophyte overall HU value and the patient's QCT BMD value (P > 0.05).

CONCLUSIONS

Vertebral HU value is an alternative measurement that effectively reflects the patient's BMD. In middle-aged and elderly LDD patients, HU values in different areas of L3 vertebra are significantly different, and hyperplastic tissues such as cortical bone and osteophytes may exponentially lead to higher HU value in patients. Compared with other measurement areas, vertebral cancellous bone HU value have the advantage of accurately assessing patients' BMD.

Osteosarcopenia and Mortality in Older Adults Undergoing Transcatheter Aortic Valve Replacement

Importance

Osteosarcopenia is an emerging geriatric syndrome characterized by age-related deterioration in muscle and bone. Despite the established relevance of frailty and sarcopenia among older adults undergoing transcatheter aortic valve replacement (TAVR), osteosarcopenia has yet to be investigated in this setting.

Objective

To determine the association between osteosarcopenia and adverse outcomes following TAVR.

Design, Setting, and Participants

This is a post hoc analysis of the Frailty in Aortic Valve Replacement (FRAILTY-AVR) prospective multicenter cohort study and McGill extension that enrolled patients aged 70 years or older undergoing TAVR from 2012 through 2022. FRAILTY-AVR was conducted at 14 centers in Canada, the United States, and France between 2012 and 2016, and patients at the McGill University-affiliated center in Montreal, Québec, Canada, were enrolled on an ongoing basis up to 2022.

Exposure

Osteosarcopenia as measured on computed tomography (CT) scans prior to TAVR.

Main Outcomes and Measures

Clinically indicated CT scans acquired prior to TAVR were analyzed to quantify psoas muscle area (PMA) and vertebral bone density (VBD). Osteosarcopenia was defined as a combination of low PMA and low VBD according to published cutoffs. The primary outcome was 1-year all-cause mortality. Secondary outcomes were 30-day mortality, hospital length of stay, disposition, and worsening disability. Multivariable logistic regression was used to adjust for potential confounders.

Results

Of the 605 patients (271 [45%] female) in this study, 437 (72%) were octogenarian; the mean (SD) age was 82.6 (6.2) years. Mean (SD) PMA was 22.1 (4.5) cm2 in men and 15.4 (3.5) cm2 in women. Mean (SD) VBD was 104.8 (35.5) Hounsfield units (HU) in men and 98.8 (34.1) HU in women. Ninety-one patients (15%) met the criteria for osteosarcopenia and had higher rates of frailty, fractures, and malnutrition at baseline. One-year mortality was highest in patients with osteosarcopenia (29 patients [32%]) followed by those with low PMA alone (18 patients [14%]), low VBD alone (16 patients [11%]), and normal bone and muscle status (21 patients [9%]) (P < .001). Osteosarcopenia, but not low VBD or PMA alone, was independently associated with 1-year mortality (odds ratio [OR], 3.18; 95% CI, 1.54-6.57) and 1-year worsening disability (OR, 2.11; 95% CI, 1.19-3.74). The association persisted in sensitivity analyses adjusting for the Essential Frailty Toolset, Clinical Frailty Scale, and geriatric conditions such as malnutrition and disability.

Conclusions and Relevance

The findings suggest that osteosarcopenia detected using clinical CT scans could be used to identify frail patients with a 3-fold increase in 1-year mortality following TAVR. This opportunistic method for osteosarcopenia assessment could be used to improve risk prediction, support decision-making, and trigger rehabilitation interventions in older adults.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

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

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

Bone loss over time and risk of osteoporosis in advanced pancreatic cancer

BACKGROUND

Pancreatic cancer has a high risk of developing osteoporosis. However, the impact of osteoporosis has not been well-studied. This study aimed to evaluate bone loss over time and risk of osteoporosis in patients with advanced pancreatic cancer.

METHODS

We retrospectively examined consecutive patients with unresectable pancreatic cancer who had evaluable computed tomography before treatment and at 1-year follow-up. Bone mineral density at the first lumbar vertebra was measured on computed tomography, and osteoporosis was defined as bone mineral density < 135 Hounsfield units. The prevalence and risk factors for osteoporosis, changes in bone mineral density over time and incidence of bone fractures were analyzed.

RESULTS

Three hundred eighty patients were included. Osteoporosis was associated with older age, female sex, low body mass index and poor performance status at baseline. A consistent decrease in bone mineral density was observed over time regardless of age, sex or disease status, resulting in an increase in the prevalence of osteoporosis over time (47% at baseline, 79% at 1 year, 88% at 2 years, 89% at 3 years, 95% at 4 years and 100% at 5 years). Changes in bone mineral density from baseline were greater in patients with locally-advanced pancreatic cancer, in those who received modified FOLFIRINOX or S-IROX for more than 3 months, and in those who received radiation therapy. Incident fractures developed in 45 patients (12%) during follow-up.

CONCLUSIONS

Osteoporosis and osteoporotic fractures were highly prevalent in patients with advanced pancreatic cancer. This study highlights the importance of screening for osteoporosis in such patients.

Serum periostin levels correlate with severity of intervertebral disc degeneration

PURPOSE

Periostin, an extracellular matrix protein closely related to mechanical stress, inflammation, and ageing, has been implicated in intervertebral disc degeneration (IVDD) in basic research. However, it has not been examined in clinical cases. This study aimed to evaluate the association between IVDD severity and serum periostin concentration as well as to analyse potential associations between IVDD and clinical and demographic factors.

METHODS

This retrospective cohort study included 198 patients who underwent lumbar disc herniation and lumbar canal stenosis between January 2020 and December 2022. The severity of IVDD was evaluated using the Pfirrmann grading, whereas serum periostin levels were measured using ELISA kits. Clinical demographics, including age, sex, body mass index, comorbidities, psoas muscle index, and spinal disease, were also recorded.

RESULTS

This study demonstrated a significant correlation between high serum periostin levels and IVDD severity, as indicated by a high cumulative Pfirrmann score. Serum periostin levels were identified as an independent risk factor for IVDD in a multivariate regression model. Correlation analysis showed a correlation between periostin levels and Pfirrmann grade at each lumbar level (ρ = 0.458-0.550, p < 0.001) and a strong correlation with cumulative Pfirrmann score (ρ = 0.690, p < 0.001).

CONCLUSION

The higher the serum periostin level, the higher the cumulative Pfirrmann score. Multivariate analysis showed that serum periostin was an independent risk factor for IVDD. Periostin levels may be a clinically suitable and useful biomarker for diagnosing IVDD, estimating disease progression and activity, providing prognostic information, and evaluating treatment options.

State of the Art Imaging of Osteoporosis

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

Biomarkers of Body Composition

The importance and impact of imaging biomarkers has been increasing over the past few decades. We review the relevant clinical and imaging terminology needed to understand the clinical and research applications of body composition. Imaging biomarkers of bone, muscle, and fat tissues obtained with dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging, and ultrasonography are described.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Opportunistic CT Screening-Machine Learning Algorithm Identifies Majority of Vertebral Compression Fractures: A Cohort Study

Vertebral compression fractures (VCF) are common in patients older than 50 years but are often undiagnosed. Zebra Medical Imaging developed a VCF detection algorithm, with machine learning, to detect VCFs from CT images of the chest and/or abdomen/pelvis. In this study, we evaluated the diagnostic performance of the algorithm in identifying VCF. We conducted a blinded validation study to estimate the operating characteristics of the algorithm in identifying VCFs using previously completed CT scans from 1200 women and men aged 50 years and older at a tertiary-care center. Each scan was independently evaluated by two of three neuroradiologists to identify and grade VCF. Disagreements were resolved by a senior neuroradiologist. The algorithm evaluated the CT scans in a separate workstream. The VCF algorithm was not able to evaluate CT scans for 113 participants. Of the remaining 1087 study participants, 588 (54%) were women. Median age was 73 years (range 51-102 years; interquartile range 66-81). For the 1087 algorithm-evaluated participants, the sensitivity and specificity of the VCF algorithm in diagnosing any VCF were 0.66 (95% confidence interval [CI] 0.59-0.72) and 0.90 (95% CI 0.88-0.92), respectively, and for diagnosing moderate/severe VCF were 0.78 (95% CI 0.70-0.85) and 0.87 (95% CI 0.85-0.89), respectively. Implementing this VCF algorithm within radiology systems may help to identify patients at increased fracture risk and could support the diagnosis of osteoporosis and facilitate appropriate therapy. © 2023 Amgen, Inc. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Correlation analysis of the vertebral compression degree and CT HU value in elderly patients with osteoporotic thoracolumbar fractures

BACKGROUND

To explore the correlation of the vertebral compression degree and cancellous bone CT HU in elderly patients with osteoporotic thoracolumbar fractures.

METHODS

Elderly patients with single-segment vertebral fragility fractures were retrospectively reviewed. All patients experienced a low-energy trauma and underwent thoracolumbar MRI. The consistency of measurement between two spine surgeons was evaluated. The average CT HU value of the adjacent vertebral body was used instead.

RESULTS

A total of 54 patients were included in the final analysis. The patients' average age was 70.39 ± 8.53 years, and the average CT HU value was 72.78 ± 29.75 HU. The average vertebral compression ratio was 0.57 ± 0.16. Measurements showed both good intrarater repeatability and good interrater reproducibility of the vertebral compression ratio (ICC = 0.978). The degree of vertebral compression in thoracolumbar osteoporotic fractures was strongly positively correlated with the cancellous bone CT HU value (P < 0.01).

CONCLUSIONS

The local bone quality as evaluated by the CT HU value is an important factor affecting the degree of compression in osteoporotic vertebral fractures. This study provides quantitative evidence that a greater compression ratio with thoracolumbar osteoporotic fractures was associated with lower bone density in elderly patients. Further longitudinal studies with larger cohorts are needed to verify this relationship.

Differences in time-to-fusion based on "absence of peri-graft radiolucency" and "trabecular bone bridging" criteria after transforaminal lumbar interbody fusion in patients with low and normal bone density

OBJECTIVE

To investigate the difference in time-to-fusion between two sets of interbody fusion criteria (absence of peri-graft radiolucency vs. trabecular bone bridging), and to determine the effect of osteoporosis on time-to-fusion.

MATERIALS AND METHODS

This retrospective study enrolled 79 patients treated for degenerative disease with one-level transforaminal lumbar interbody fusion from February 2012 to December 2018, and who had both pre- and post-operative CTs. Patients were divided into osteoporosis, osteopenia, and normal groups based on L1 vertebral body attenuation values in pre-operative CT with cutoff of 90 Hounsfield units (HU) and 120 HU. The osteoporosis, osteopenia, and normal groups included 36 patients (mean age: 69.9 years; 8 men and 28 women), 18 patients (mean age: 62.6 years; 7 men and 11 women), and 25 patients (mean age: 56.6 years; 15 men and 10 women), respectively. Fusion was assessed annually on post-operative CT images using absence of peri-graft radiolucency and trabecular bone bridging criteria. Time-to-fusion was estimated using the Kaplan-Meier method, and differences between the groups were examined using the log-rank test. Cox proportional hazards regression was performed.

RESULTS

Time-to-fusion took significantly longer in the osteoporosis group in both fusion criteria (0.5 years in normal vs. 2 years in osteopenia vs. 3 years in osteoporosis for absence of peri-graft radiolucency; p = 0.003, and 3 years vs. 4 years vs. 5 years for trabecular bone bridging; p = 0.001). Only osteoporosis grouping was independent risk factor for slow trabecular bone fusion (hazard ratio:0.339; p = 0.003).

CONCLUSION

The median time to fusion was significantly longer when using trabecular bone bridging criteria than absence of peri-graft radiolucency criteria.

Opportunistic screening for osteoporosis and osteopenia from CT scans of the abdomen and pelvis using machine learning

OBJECTIVES

To use multivariable machine learning using the computed tomography (CT) attenuation of each of the bones in the lumbar spine, pelvis, and sacrum, to predict osteoporosis/osteopenia.

METHODS

This was a retrospective study of 394 patients aged 50 years or older with CT scans of the abdomen and pelvis and dual-energy x-ray absorptiometry (DXA) scans obtained within 6 months of each other. Volumetric segmentations were performed for each of the bones from L1-L4 vertebrae, pelvis, and sacrum to obtain the mean CT attenuation of each bone. The data was randomly split into training/validation (n = 274, 70%) and test (n = 120, 30%) datasets. The CT attenuation of the L1 vertebrae, univariate logistic regression, least absolute shrinkage and selection operator (LASSO), and support vector machines (SVM) with radial basis function (RBF) were used to predict osteoporosis/osteopenia. The performance of using the CT attenuation at L1 to the univariate logistic regression, LASSO, and SVM models were compared using DeLong's test in the test dataset.

RESULTS

All CT attenuation measurements were predictive of osteoporosis/osteopenia (p < 0.001 for all). The SVM model (accuracy = 0.892, AUC = 0.886) outperformed the models using the CT attenuation of threshold of 173.9 Hounsfield units (HU) at L1 (accuracy = 0.725, AUC = 0.739, p = 0.010), the univariate logistic regression model (accuracy = 0.767, AUC = 0.533, p < 0.001) and the LASSO model (accuracy = 0.817, AUC = 0.711, p = 0.007) to predict osteoporosis/osteopenia.

CONCLUSION

A SVM model using the CT attenuations of multiple bones within the lumbar spine and pelvis and clinical data has a better ability to predict osteoporosis/osteopenia than using the CT attenuation of L1 or a LASSO model.

KEY POINTS

• Multivariable SVM model using the CT attenuation of multiple bones and clinical/demographic data was more predictive than using the CT attenuation at L1 only.

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.

Association between opportunistic vertebral bone density measurements and new vertebral fractures after percutaneous vertebral cementoplasty: a case-control study

OBJECTIVES

To study the relationship between opportunistic CT bone density measurements and the occurrence of new vertebral fractures after percutaneous vertebral cementoplasty (PVC) of osteoporotic vertebral compression fractures (OVCF).

METHODS

A prospective analysis of retrospective data of 275 patients with OVCF treated by PVC between 2014 and 2019 with a clinico-radiological follow-up one year after treatment was conducted. Opportunistic bone density measurements were obtained at the trabecular bone of the L1 or an adjacent vertebra in Hounsfield units performed on the preoperative CT study. These density measurements values ​​were then compared between patients with and without new OCVF and in various population subgroups.

RESULTS

There were 275 patients included, with 53 (19%) presenting a new OCVF and 24 (9%) developing a fracture cascade. The median opportunistic density measurements ​​in patients with recurrent OCVF were lower than those without (median 52[40.5]) HU and 77[49] HU)(p < 0.00001). Among the patients with new OVCF the median opportunistic density measurements in patients with fracture cascades were also lower than those without (44 HU and 62 HU, respectively) (p < 0.0096). Patients with density measurements under 61 HU were 3.6 times more likely to present recurrent fractures and those with density under 54 HU were 9.8 times more likely to develop a fracture cascade. The 36 HU threshold yielded a high specificity (90-91%) for the prediction of recurrent fractures and fracture cascade but with low sensitivity (respectively 26% and 37%).

CONCLUSION

Low opportunistic vertebral density measurements are associated with a higher risk of OVCF and fracture cascades after PVC.

KEY POINTS

• Low opportunistic density measurements are associated with a higher risk of OVCF and fracture cascades after PVC. • Measuring bone density before performing a PVC could help predict the risk of new vertebral fracture after treatment • Patient management could be adapted according to bone density.

Opportunistic osteoporosis screening using chest CT with artificial intelligence

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Hounsfield unit measurement method and related factors that most appropriately reflect bone mineral density on cervical spine computed tomography

OBJECTIVE

Our study's purpose was to determine the most reliable Hounsfield unit (HU) measurement method to reflect bone mineral density (BMD) on cervical spine computed tomography (CT) and to identify any factors that influence these results.

MATERIALS AND METHODS

We retrospectively analyzed 439 consecutive patients with mild head and neck injuries. Mean HU values of the C2-C7 vertebra were determined on each sagittal, coronal, and axial CT image. Correlation patterns were analyzed between the HU value and corresponding dual-energy X-ray absorptiometry (DXA) in the lumbar vertebra (T-score) and femoral neck (T-score). A sub-group analysis was performed according to patient age, sex, and degree of spinal degeneration.

RESULTS

The correlation coefficients for HU and DXA ranged from 0.52 to 0.65 in all cervical segments. A simple linear regression analysis revealed the following formula: T-score = 0.01 × (HU) - 4.55. The mean HU values for osteopenia and osteoporosis were 284.0 ± 63.3 and 231.5 ± 52.8, respectively. The ROC curve indicated that the HU method has a sensitivity of 89.2% and specificity of 88.7% to diagnose osteoporosis. The HU measurement showed a high correlation value (range: r = 0.64-0.70) with spine DXA score regardless of the degree of degeneration or patient age or sex.

CONCLUSION

HU values using the upper two cervical vertebrae (C2 and C3) reflected a more reliable BMD level than other segments. Additionally, the HU of cervical CT provided reliable information regardless of measurement plane, age or sex, and degree of degeneration.

CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation

CT-based body composition measures are well established in research settings as prognostic markers in oncologic patients. Numerous retrospective studies have shown the role of objective measurements extracted from abdominal CT images of skeletal muscle, abdominal fat, and bone mineral density in providing more accurate assessments of frailty and cancer cachexia in comparison with traditional clinical methods. Quantitative CT-based measurements of liver fat and aortic atherosclerotic calcification have received relatively less attention in cancer care but also provide prognostic information. Patients with cancer routinely undergo serial CT examinations for staging, treatment response, and surveillance, providing the opportunity for quantitative body composition assessment to be performed as part of routine clinical care. The emergence of fully automated artificial intelligence-based segmentation and quantification tools to replace earlier time-consuming manual and semiautomated methods for body composition analysis will allow these opportunistic measures to transition from the research realm to clinical practice. With continued investigation, the measurements may ultimately be applied to achieve more precise risk stratification as a component of personalized oncologic care.

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.

Computed tomography-based paravertebral muscle density predicts subsequent vertebral fracture risks independently of bone mineral density in postmenopausal women following percutaneous vertebral augmentation

BACKGROUND

The risk of subsequent vertebral fractures (SVF) after the primary vertebral fracture cannot be explained by lower bone mineral density (BMD) alone. Computed tomography (CT) measurements of paravertebral muscle density (PMD) are recognized radiographic markers used to predict physical function, fragile fractures.

AIMS

This study aims to investigate the relationship between PMD and the risk of SVF in cohorts of postmenopausal women, and to determine if combining both PMD and BMD measures derived from CT can improve the accuracy of predicting SVF.

METHODS

This study enrolled 305 postmenopausal women between the ages of 50 and 88 for 3 years of follow-up studies. Trabecular attenuation (Hounsfield units, HU) was measured at L1 level and muscle attenuation of paravertebral muscle at L3 level on preoperative lumbar CT scans to determine the L1 BMD and L3 PMD. Kaplan-Meier analysis was applied to evaluate SVF-free survival. The hazard ratios (HRs) of PMD for SVF events were estimated with the Cox proportional hazards model. The predictive values of L1 BMD and L3 PMD for SVF were quantified using the Receiver-Operating Characteristic (ROC) curve.

RESULT

During the 3 years of follow-up studies, 88 patients (28.9%) suffered an SVF. ROC curve analysis demonstrated that an L3 PMD threshold of 32 HU had a sensitivity of 89.8% and a specificity of 62% for the prediction of SVF. Kaplan-Meier analysis showed that L3 PMD ≤ 32 HU was significantly associated with lower SVF-free survival (p < 0.001; log-rank test). After adjusting for age, BMI, diabetes, postoperative osteoporosis treatment, handgrip strength, L1 BMD, multivariate analyses also indicated a persistent modest effect of L3 PMD on SVF-free survival. The area under the ROC curve of L3 PMD and L1 BMD, combined to predict the risk of SVF, was 0.790, which was significantly higher than the value for L1 BMD alone (0.735). L3 PMD and L1 BMD significantly improved the accuracy of SVF risk prediction compared with L1 BMD alone, which was confirmed by reclassification improvement measures. The inclusion of handgrip strength and postoperative osteoporosis treatment in the model further improved SVF prediction accuracy, and PMD remained significant in the model.

CONCLUSION

Decreased L3 PMD is an independent risk predictor of SVF. Combined CT-based L1 BMD and L3 PMD can significantly improve the accuracy of predicting the risk of SVF in postmenopausal women who have suffered prior osteoporotic vertebral fractures.

Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion

BACKGROUND CONTEXT

The importance of bone status assessment in spine surgery is well recognized. The current gold standard for assessing bone mineral density is dual-energy X-ray absorptiometry (DEXA). However, DEXA has been shown to overestimate BMD in patients with spinal degenerative disease and obesity. Consequently, alternative radiographic measurements using data routinely gathered during preoperative evaluation have been explored for the evaluation of bone quality and fracture risk. Opportunistic quantitative computed tomography (QCT) and more recently, the MRI-based vertebral bone quality (VBQ) score, have both been shown to correlate with DEXA T-scores and predict osteoporotic fractures. However, to date the direct association between VBQ and QCT has not been studied.

PURPOSE

The objective of this study was to evaluate the correlation between VBQ and spine QCT BMD measurements and assess whether the recently described novel VBQ score can predict the presence of osteopenia/osteoporosis diagnosed with QCT.

STUDY DESIGN/SETTING

Cross-sectional study using retrospectively collected data.

PATIENT SAMPLE

Patients undergoing lumbar fusion from 2014-2019 at a single, academic institution with available preoperative lumbar CT and T1-weighted MRIs were included.

OUTCOME MEASURES

Correlation of the VBQ score with BMD measured by QCT, and association between VBQ score and presence of osteopenia/osteoporosis.

METHODS

Asynchronous QCT measurements were performed. The average L1-L2 BMD was calculated and patients were categorized as either normal BMD (>120 mg/cm³) or osteopenic/osteoporotic (≤120 mg/cm³). The VBQ score was calculated by dividing the median signal intensity of the L1-L4 vertebral bodies by the signal intensity of the cerebrospinal fluid on midsagittal T1-weighted MRI images. Inter-observer reliability testing of the VBQ measurements was performed. Demographic data and the VBQ score were compared between the normal and osteopenic/osteoporotic group. To determine the area-under-curve (AUC) of the VBQ score as a predictor of osteopenia/osteoporosis receiver operating characteristic (ROC) analysis was performed. VBQ scores were compared with QCT BMD using the Pearson's correlation.

RESULTS

A total of 198 patients (53% female) were included. The mean age was 62 years and the mean BMI was 28.2 kg/m². The inter-observer reliability of the VBQ measurements was excellent (ICC of 0.90). When comparing the patients with normal QCT BMD to those with osteopenia/osteoporosis, the patients with osteopenia/osteoporosis were significantly older (64.9 vs. 56.7 years, p<.0001). The osteopenic/osteoporotic group had significantly higher VBQ scores (2.6 vs. 2.2, p<.0001). The VBQ score showed a statistically significant negative correlation with QCT BMD (correlation coefficient = -0.358, 95% CI -0.473 - -0.23, p<.001). Using a VBQ score cutoff value of 2.388, the categorical VBQ score yielded a sensitivity of 74.3% and a specificity of 57.0% with an AUC of 0.7079 to differentiate patients with osteopenia/osteoporosis and with normal BMD.

CONCLUSIONS

We found that the VBQ score showed moderate diagnostic ability to differentiate patients with normal BMD versus osteopenic/osteoporotic BMD based on QCT. VBQ may be an interesting adjunct to clinically performed bone density measurements in the future.

Opportunistic osteoporosis screening using routine computed tomography images to identify bone loss in gynecologic cancer survivors

OBJECTIVE

Cancer treatment-induced bone loss is a known side effect of cancer therapy. Computed tomography (CT) bone mineral density screening is a novel tool for identifying bone loss. This study aims to use routine CT images to determine long-term bone mineral density changes and osteoporosis risk among women with gynecologic cancers.

METHODS

Bone loss was evaluated in a retrospective cohort of women ≤65 years old with gynecologic cancer who underwent oophorectomy from January 2010 to December 2014. Opportunistic CT-based bone mineral density measurements (Hounsfield units, HU) were performed at baseline and intervals up to 5 years after cancer diagnosis. Osteoporosis risk was categorized by HU. Bivariate and multivariate analyses were performed to compare baseline to follow-up bone mineral density at 1, 3, and 5 years and to identify predictors of bone loss following diagnosis.

RESULTS

A total of 185 patients (median age 53 years, range 23-65 years, 78.1% ovarian cancer) were included. Bone mineral density significantly decreased between baseline and 1 year (p<0.001), 3 years (p<0.001), and 5 years (p<0.001). Half with normal bone mineral density at baseline had risk for osteopenia or osteoporosis at 5 years. Four percent had osteoporosis risk at baseline compared with 1 year (7.4%), 3 years (15.7%), and 5 years (18.0%). Pre-treatment bone mineral density was a significant predictor at 1 and 5 years (1 year: p<0.01; 5 years: p<0.01). History of chemotherapy predicted bone loss at 1 year (p=0.03). More lifetime chemotherapy cycles were associated with increased risk of osteoporosis at 1 year (p=0.03) and 5 years (p=0.01).

CONCLUSIONS

Women with gynecologic cancers may experience accelerated cancer treatment-induced bone loss. Routine CT imaging is a convenient screening modality to identify those at highest risk for osteoporosis who warrant further evaluation with dual-energy X-ray absorptiometry. Routine bone mineral density assessments 1 year following oophorectomy for cancer treatment may be warranted in this population.

The Usefulness of Trabecular CT Attenuation Measurement at L4 Level to Predict Screw Loosening After Degenerative Lumbar Fusion Surgery: Consider Number of Fused Levels and Postoperative Sagittal Balance

STUDY DESIGN

Retrospective study.

OBJECTIVE

To evaluate the absolute value of L4 trabecular region-of-interest (t-ROI) computed tomography (CT) attenuation, which can predict pedicle screw loosening, and determine the changes in value according to number of fused levels and sagittal balance in patients undergoing lumbar fusion surgery.

SUMMARY OF BACKGROUND DATA

Although osteoporosis was not diagnosed in spinal dual x-ray absorptiometry preoperatively, we encountered several cases of screw loosening within 1 year of lumbar fusion surgery.

METHODS

We enrolled 478 patients and analyzed factors related to screw loosening. We evaluated the association between L4 t-ROI CT attenuation and screw loosening and determined the best cutoff value of t L4 t-ROI CT attenuation for predicting screw loosening.

RESULTS

The number of fused levels, postoperative C7-S1 sagittal vertical axis (SVA), and L4 t-ROI CT attenuation were independently correlated with screw loosening. According to number of fused level and postoperative C7-T1 SVA (≥36.9 mm or <36.9 mm), in patients with one-level fusion and C7-S1 SVA less than 36.9 mm, the optimal cutoff point of the L4 t-ROI CT attenuation predicting screw loosening was 106.5 Hounsfield unit (HU). L4 t-ROI attenuation did not change until two-level fusions. In patients with three-level fusions and C7-S1 SVA less than 36.9 mm, the optimal cutoff point of the L4 t-ROI CT attenuation predicting screw loosening was 159.0 HU. The optimal cutoff point of L4 t-ROI CT attenuation in patients with three-level fusions and C7-S1 SVA more than or equal to 36.9 mm was 191.0 HU.

CONCLUSION

L4 t-ROI CT attenuation value considering number of fused levels and sagittal balance is an accurate measurement method to predict screw loosening. Spine surgeons should be aware of the L4 t-ROI attenuation before surgery to improve the fusion rate and reduce instrument-related complications of lumbar spine surgery in osteoporotic patients.

LEVEL OF EVIDENCE

3.

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.

Value-added Opportunistic CT Screening: State of the Art

Opportunistic CT screening leverages robust imaging data embedded within abdominal and thoracic scans that are generally unrelated to the specific clinical indication and have heretofore gone largely unused. This incidental imaging information may prove beneficial to patients in terms of wellness, prevention, risk profiling, and presymptomatic detection of relevant disease. The growing interest in CT-based opportunistic screening relates to a confluence of factors: the objective and generalizable nature of CT-based body composition measures, the emergence of fully automated explainable AI solutions, the sheer volume of body CT scans performed, and the increasing emphasis on precision medicine and value-added initiatives. With a systematic approach to body composition and other useful CT markers, initial evidence suggests that their ability to help radiologists assess biologic age and predict future adverse cardiometabolic events rivals even the best available clinical reference standards. Emerging data suggest that standalone "intended" CT screening over an unorganized opportunistic approach may be justified, especially when combined with established cancer screening. This review will discuss the current status of opportunistic CT screening, including specific body composition markers and the various disease processes that may be impacted. The remaining hurdles to widespread clinical adoption include generalization to more diverse patient populations, disparate technical settings, and reimbursement.

Computed Tomography-Based L1 Bone Mineral Density in 624 Dutch Trauma Patients—Are North American Reference Values Valid in Europe?

Opportunistic screening for bone mineral density (BMD) of the first lumbar vertebra (L1) using computed tomography (CT) is increasingly used to identify patients at risk for osteoporosis. An extensive study in the United States has reported sex-specific normative values of CT-based BMD across all ages. The current study aims to validate North American reference values of CT-based bone mineral density in a Dutch population of level-1 trauma patients. All trauma patients aged 16 or older, admitted to our level-1 trauma center during 2017, who underwent a CT scan of the chest or abdomen at 120 kVp within 7 days of hospital admission, were retrospectively included. BMD measurements in Hounsfield Units (HU) were performed manually in L1 or an adjacent vertebra. Student’s t-tests were performed to compare the Dutch mean BMD value per age group to the North American reference values. Linear regression analysis and Pearson’s correlation coefficient (ρ) calculations were performed to assess the correlation between BMD and age. In total, 624 patients were included (68.4% men, aged 16–95). Mean BMD decreased linearly with 2.4 HU per year of age (ρ = −0.77). Sex-specific analysis showed that BMD of premenopausal women was higher than BMD of men at these ages. Dutch mean BMD values in the age groups over 35 years were significantly lower than the North American reference values. Our findings indicate that using North American BMD thresholds in Dutch clinical practice would result in overdiagnosis of osteoporosis and osteopenia. Dutch guidelines may benefit from population-specific thresholds.

Bone health and glucocorticoid-containing lymphoma therapy - a review of risk factors and preventative measures

With survival outcomes ever improving for patients with a wide range of lymphoma histologies, the focus on reducing long-term complications of therapy has increased. Recently published, complimentary population and retrospective series have highlighted the importance of considering bone health in patients treated for lymphoma. Fracture-related events or the requirement for secondary bone prophylaxis, likely linked to glucocorticoid-induced osteoporosis (GIO) are substantial and clinically meaningful in a significant minority of patients following routinely employed steroid-containing immunochemotherapy. In this review, we describe the pathophysiology of GIO, the risk of GIO in observational front-line lymphoma studies and efficacy of prophylactic measures from several prospective clinical trials are summarized. Finally, areas of importance for future research are discussed and recommendations for GIO risk assessment and management in lymphoma are provided based on the current available literature.

Accuracy of Opportunistic Bone Mineral Density Assessment on Staging Computed Tomography for Gynaecological Cancers

Background and Objectives: Women with gynecological cancers constitute a high-risk cohort for loss of bone density. International guidance stipulates women undergoing cancer treatments associated with bone loss should have a quantitative assessment of bone density. Access to Dual-energy X-ray Absorptiometry (DXA) is limited. This study aimed to assess the accuracy of opportunistic bone density measurement on staging computed tomography (CT) scans for gynaecological malignancies, in comparison to the gold standard DXA. Materials and Methods: Women with a staging CT scan of the abdomen and pelvis for a new diagnosis of gynecological cancer were recruited. DXA was performed within 6 weeks of treatment for gynaecological cancer. Lumbar bone density was measured by CT attenuation values, in Hounsfield units (HU), of the anterior trabecular region. Correlations between CT and DXA parameters were analysed. Receiver Operating Characteristic(ROC) curves for diagnosis of low bone density and osteoporosis were analysed. Results: Final cohort included 48 of 50 women recruited. There was good diagnostic accuracy for abnormal bone density and osteoporosis, with areas under the ROC curve at L1 of 0.77 (p = 0.002) and 0.80 (p = 0.020) respectively. CT-HU of 170-190 yielded sensitivities of 87-90%, positive predictive values of 75-84% and negative predictive values of 71-75% for the diagnosis of low bone mineral density. CT-HU of 90-110 yielded specificities of 85-93% for the diagnosis of osteoporosis. Moderate correlations were found between CT-HU and both DXA T-scores and diagnostic categories. Conclusions: This is the first study to assess the opportunistic application of CT in the assessment of bone health in women with gynaecological cancer, a cohort at high-risk of osteoporosis. The correlation between bone density assessment in CT-HU and DXA, and strong AUC values for the diagnosis of low bone density (0.77) and osteoporosis (0.80) support this pragmatic solution in resolving the care-gap in cancer treatment-induced bone loss, often associated with poor access to DXA.

Visualizing Frailty: Exploring Radiographical Measures of Frailty in Trauma Patients

Purpose: This study assessed the relationship of core muscle sarcopenia, myosteatosis, and L1 attenuation to the 5-factor modified frailty index (mFI-5), discharge disposition, and post-admission complications in orthopedic and general trauma patients. It was hypothesized that reduced sarcopenia, L1 attenuation, and increased myosteatosis is associated with higher mFI-5 scores (≥ 0.3), discharge into care, and increased post-admission complications.Methods: This prospective cohort study was performed at a Level 1 trauma center. Patients were surveyed and metrics of the mFI-5 were used. Frail was categorized as a mFI-5 score ≥ 0.3. Recent abdominal computed tomography (CT) scans were used to extract radiographical information of total psoas cross-sectional area, psoas myosteatosis, and L1 vertebrae attenuation.Results: There were 140 patients who consented to the study, of which 83 had available abdomen and pelvis CT scans. The mean age was 43.19 (± 17.36), and 65% were male (<i>n</i> = 52). When comparing the frail (16%, <i>n</i> = 13) and not frail (84%, <i>n</i> = 70) patients, there was a significant difference in mean psoas myosteatosis (<i>p</i> < 0.0001) and the attenuation of the L1 vertebrae (<i>p</i> < 0.001). On multivariate analysis when accounting for age, myosteatosis of the psoas muscles was predictive of an mFI-5 score ≥ 0.3.Conclusion: The findings suggest that myosteatosis and L1 attenuation are associated with frailty indices (mFI-5) after traumatic injury. Future studies are needed to prospectively assess the validity of both radiographical and index-based markers of frailty in predicting post-traumatic complications, mortality, and hospital utilization.

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.

Deep learning takes the pain out of back breaking work - Automatic vertebral segmentation and attenuation measurement for osteoporosis

BACKGROUND

Osteoporosis is an underdiagnosed and undertreated disease worldwide. Recent studies have highlighted the use of simple vertebral trabecular attenuation values for opportunistic osteoporosis screening. Meanwhile, machine learning has been used to accurately segment large parts of the human skeleton.

PURPOSE

To evaluate a fully automated deep learning-based method for lumbar vertebral segmentation and measurement of vertebral volumetric trabecular attenuation values.

MATERIAL AND METHODS

A deep learning-based method for automated segmentation of bones was retrospectively applied to non-contrast CT scans of 1008 patients (mean age 57 years, 472 female, 536 male). Each vertebral segmentation was automatically reduced by 7 mm in all directions in order to avoid cortical bone. The mean and median volumetric attenuation values from Th12 to L4 were obtained and plotted against patient age and sex. L1 values were further analyzed to facilitate comparison with previous studies.

RESULTS

The mean L1 attenuation values decreased linearly with age by -2.2 HU per year (age > 30, 95% CI: -2.4, -2.0, R² = 0.3544). The mean L1 attenuation value of the entire population cohort was 140 HU ± 54.

CONCLUSIONS

With results closely matching those of previous studies, we believe that our fully automated deep learning-based method can be used to obtain lumbar volumetric trabecular attenuation values which can be used for opportunistic screening of osteoporosis in patients undergoing CT scans for other reasons.

Could Computed Tomography Hounsfield Unit Values of Lumbar Vertebrae Detect Osteoporosis?

BACKGROUND

Dual-energy X-ray absorptiometry (DEXA) scanning has several disadvantages determining osteoporosis, especially for the degenerative spine.

INTRODUCTION

This study aims to determine spinal osteoporosis in patients suffering from lumbar degenerative disease using computed tomography (CT).

METHODS

A total of 547 subjects that underwent DEXA and abdominal CT within a period of three months were examined retrospectively and separated into groups based on lumbar degenerative alteration on the CT scan. The subjects that showed degenerative severity at L1-L4, in at least two levels, were graded and placed in the degenerative group (Group D, n=350). In contrast, the other subjects constituted the control group (Group C, n=197). The Hounsfield unit (HU) of the vertebral body trabecular bone, the T-score, and bone mineral density (BMD) of L1-L4 and hips were determined from the CT images. CT-HU parameters for osteoporosis acquired from the control group were used to ascertain undiagnosed osteoporosis.

RESULTS

The CT-HU was positively correlated with T-score and lumbar BMD for both groups (P<0.001), while the L1-L4 correlation was higher in Group C than in Group D. Based on linear regression, the T-score and CT-HU for L1-L4 osteoporosis were 129, 136, 129 and 120 HU, respectively in Group C. Undiagnosed spinal osteoporosis was greater in Group D compared to the controls (44.2% vs. 9.6%, respectively) based on the CT-HU thresholds.

CONCLUSION

Lumbar spine degeneration can augment BMD and T-score, resulting in the underestimation of lumbar osteoporosis. The osteoporosis threshold determined by CT-HU may be a valuable technique to determine undiagnosed spinal osteoporosis.

Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation

Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, deformity, height loss and pain before diagnosis. There were an estimated 520,000 fragility fractures in the United Kingdom (UK) in 2017 (costing £4.5 billion), a figure set to increase 30% by 2030. One way to improve both vertebral fracture identification and the diagnosis of osteoporosis is to assess a patient's spine or hips during routine computed tomography (CT) scans. Patients attend routine CT for diagnosis and monitoring of various medical conditions, but the skeleton can be overlooked as radiologists concentrate on the primary reason for scanning. More than half a million CT scans done each year in the National Health Service (NHS) could potentially be screened for osteoporosis (increasing 5% annually). If CT-based screening became embedded in practice, then the technique could have a positive clinical impact in the identification of fragility fracture and/or low bone density. Several companies have developed software methods to diagnose osteoporosis/fragile bone strength and/or identify vertebral fractures in CT datasets, using various methods that include image processing, computational modelling, artificial intelligence and biomechanical engineering concepts. Technology to evaluate Hounsfield units is used to calculate bone density, but not necessarily bone strength. In this rapid evidence review, we summarise the current literature underpinning approved technologies for opportunistic screening of routine CT images to identify fractures, bone density or strength information. We highlight how other new software technologies have become embedded in NHS clinical practice (having overcome barriers to implementation) and highlight how the novel osteoporosis technologies could follow suit. We define the key unanswered questions where further research is needed to enable the adoption of these technologies for maximal patient benefit.

Opportunistic application of phantom-less calibration methods for fracture risk prediction using QCT/FEA

OBJECTIVES

Quantitative computed tomography (QCT)-based finite element analysis (FEA) implements a calibration phantom to estimate bone mineral density (BMD) and assign material properties to the models. The objectives of this study were to (1) propose robust phantom-less calibration methods, using subject-specific tissues, to obtain vertebral fracture properties estimations using QCT/FEA; and (2) correlate QCT/FEA predictions to DXA values of areal BMD.

METHODS

Eighty of a cohort of 111 clinical QCT scans were used to obtain subject-specific parameters using a phantom calibration approach and for the development of the phantom-less calibration equations. Equations were developed based on the HU measured from various soft tissues and regions, and using multiple linear regression analyses. Thirty-one additional QCT scans were used for cross-validation of QCT/FEA estimated fracture loads from the L₃ vertebrae based on the phantom and phantom-less equations. Finally, QCT/FEA-predicted fracture loads were correlated with aBMD obtained from DXA.

RESULTS

Overall, 217 QCT/FEA models from 31 subjects (20 females, 11 men) with mean ages of 69.6 (13.1) and 67.3 (14) were used to cross-validate the phantom-less equations and assess bone strength. The proposed phantom-less equations showed high correlations with phantom-based estimates of BMD (99%). Cross-validation of QCT/FEA-predicted fracture loads from phantom-less equations and phantom-specific outcomes resulted in high correlations for all proposed methods (0.94-0.99). QCT/FEA correlation outcomes from the phantom-less equations and DXA-aBMD were moderately high (0.64-0.68).

CONCLUSIONS

The proposed QCT/FEA subject-specific phantom-less calibration methods demonstrated the potential to be applied to both prospective and retrospective applications in the clinical setting.

KEY POINTS

• QCT/FEA overcomes the disadvantages of DXA and improves fracture properties predictions of vertebrae. • QCT/FEA fracture estimates using the phantom-less approach highly correlated to values obtained using a calibration phantom. • QCT/FEA prediction using a phantom-less approach is an accurate alternative over phantom-based methods.

Prospective validation of a rapid CT-based bone mineral density screening method using colored spinal images

PURPOSE

To prospectively validate a method to accurately and rapidly differentiate normal from abnormal spinal bone mineral density (BMD) using colored abdominal CT images.

METHODS

For this prospective observational study, 196 asymptomatic women ≥ 50 years of age presenting for screening mammograms underwent routine nonenhanced CT imaging of the abdomen. The CT images were processed with software designed to generate sagittal colored images with green vertebral trabecular bone indicating normal BMD and red indicating abnormal BMD (low BMD or osteoporosis). Four radiologists evaluated L1/L2 BMD on sagittal images using visual assessment of grayscale images, quantitative measurements of mean vertebral attenuation, and visual assessment of colored images. Mean BMD values at L1/L2 using quantitative CT with a phantom served as the reference standard. The average accuracy and time of interpretation were calculated. Inter-observer agreement was assessed using intraclass correlation coefficient (ICC).

RESULTS

Mean attenuation at L1/L2 was highly correlated with mean BMD (r = 0.96/0.91, p < 0.001 for both). The average accuracy and mean time to assess BMD among four readers for differentiating normal from abnormal BMD was 66% and 6.0 s using visual assessment of grayscale images, 88% and 15.2 s using quantitative measurements of mean vertebral attenuation, and 92% and 2.1 s using visual assessment of colored images (p < 0.001 and p < 0.001, respectively). Inter-observer agreement was poor using visual assessment of grayscale images (ICC:0.31), good using quantitative measurements of mean vertebral attenuation (ICC:0.73), and excellent using visual assessment of colored images (ICC:0.90).

CONCLUSION

Detection of abnormal BMD using colored abdominal CT images was highly accurate, rapid, and had excellent inter-observer agreement.

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.

Risk of vertebral fractures: evaluation on vertebral trabecular attenuation value and hydroxyapatite concentration in patients by chest spectral CT

OBJECTIVES

To analyze vertebral fractures risk in patients with chest scans by evaluating vertebral hydroxyapatite concentration measured on spectral CT compared to trabecular attenuation value measured on conventional CT.

METHODS

Our retrospective study reviewed CT of 216 patients. Analysis of vertebral (T11 - L1) hydroxyapatite concentration by spectral imaging and trabecular attenuation value by conventional CT imaging were performed in patients with chest CT examinations. Specificity, sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were performed by using receiver operating characteristic (ROC) curves in patients with and without vertebral fractures.

RESULTS

In male patients, vertebral hydroxyapatite concentration had high area under the ROC curve (0.916), by using the optimal threshold of 72.27 mg/cm³, specificity, sensitivity, NPV, and PPV were 91.7, 80.2, 36.7, and 98.7%, respectively. In female patients, vertebral hydroxyapatite concentration also had high area under the ROC curve (0.870), by using the optimal threshold of 74.79 mg/cm³, specificity, sensitivity, NPV, and PPV were 100.0, 77.8, 47.4, and 100.0%, respectively. Area under the ROC curve was significantly different between spectral CT-measured bone hydroxyapatite concentration and conventional CT-measured attenuation value in distinguishing vertebral fractures (p = 0.007 for males; p = 0.005 for females).

CONCLUSIONS

Quantitative assessment with spectral CT may appear as higher accuracy than that of conventional CT imaging to analyze risk of vertebral fractures. Hydroxyapatite concentration measured with chest spectral CT may be used to evaluate risk of bone fractures.

ADVANCES IN KNOWLEDGE

Hydroxyapatite concentration measured with chest spectral CT may be used to evaluate risk of bone fractures.

Correlation of vertebral trabecular attenuation in Hounsfield units and the upper instrumented vertebra with proximal junctional failure after surgical treatment of degenerative lumbar disease

OBJECTIVE

The aim of this study was to investigate whether bone mineral density (BMD) measured in Hounsfield units (HUs) is correlated with proximal junctional failure (PJF).

METHODS

A retrospective study of 104 patients with adult degenerative lumbar disease was performed. All patients underwent posterior instrumented fusion of 4 or more segments and were followed up for at least 2 years. Patients were divided into two groups on the basis of whether they had mechanical complications of PJF. Age, sex ratio, BMI, follow-up time, upper instrumented vertebra (UIV), lower instrumented vertebra, and vertebral body osteotomy were recorded. The spinopelvic parameters were measured on early postoperative radiographs. The HU value of L1 trabecular attenuation was measured on axial and sagittal CT scans. Statistical analysis was performed to compare the difference of continuous and categorical variables. Receiver operating characteristic (ROC) curve analysis was used to obtain attenuation thresholds. A Kaplan-Meier curve and log-rank test were used to analyze the differences in PJF-free survival. Multivariate analysis via a Cox proportional hazards model was used to analyze the risk factors.

RESULTS

The HU value of L1 trabecular attenuation in the PJF group was lower than that in the control group (p < 0.001). The spinopelvic parameter L4-S1 lordosis was significantly different between the groups (p = 0.033). ROC curve analysis determined an optimal threshold of 89.25 HUs (sensitivity = 78.3%, specificity = 80.2%, area under the ROC curve = 0.799). PJF-free survival significantly decreased in patients with L1 attenuation ≤ 89.25 HUs (p < 0.001, log-rank test). When L1 trabecular attenuation was ≤ 89.25 HUs, PJF-free survival in patients with the UIV at L2 was the lowest, compared with patients with their UIV at the thoracolumbar junction or above (p = 0.028, log-rank test).

CONCLUSIONS

HUs could provide important information for surgeons to make a treatment plan to prevent PJF. L1 trabecular attenuation ≤ 89.25 HUs measured by spinal CT scanning could predict the incidence of PJF. Under this condition, the UIV at L2 significantly increases the incidence of PJF.

First intention vertebroplasty in fractures within an ankylosed thoracolumbar spinal segment

PURPOSE

To evaluate the outcome of percutaneous vertebral cementoplasty (PVC) as the first-line treatment for traumatic thoracolumbar fractures within an ankylosed spinal segment.

MATERIALS AND METHODS

Thirty-one patients (15 men, 16 women; mean age: 79.2±11 [SD] years; age range: 66-95 years) with thoracolumbar fractures within an ankylosed spine segment without neurological impairment treated with PVC were retrospectively evaluated. All patients were controlled at six weeks and one year after PVC. Ankylosing conditions, fractures sites and types, radiological consolidation, spinal complications were assessed. Anterior/posterior vertebral height ratios were measured before and after PVC. Postoperative pain relief and treatment success (radiological fracture consolidation) rates were considered.

RESULTS

The 31 patients had a total of 39 fractures (19 stable [49%], 20 unstable [51%]) treated with PVC. Primary success rate of PVC (initial fracture consolidation without complication) was 61% (19/31). Seven patients (7/31; 23%) exhibited new fractures, and the secondary success rate of PVC (global fracture consolidation one year after repeat PVC) was 87% (34/39). Global consolidation rates of unstable fractures were 85% (17/20) of treated levels. Pain score was null in 84% patients (26/31) one year after PVC. There were no significant differences between pre-PVC (0.62±0.18 [SD]; range: 0.22-0.88) and post-PVC (0.60±0.18 [SD]; range: 0.35-0.88) vertebral height ratios (P=0.94).

CONCLUSION

PVC conveys a high overall success rate and effectively controls pain in patients with vertebral fractures within ankylosed spine segments.