Opportunistic screening for osteoporosis by abdominal CT in a British population

Upper Instrumented Vertebra Hounsfield Unit Assessment and Association With Mechanical Complications Following Posterior Spinal Fusion

BACKGROUND

Bone mineral density is often considered in preoperative surgical planning for spinal fusions. CT imaging-based measurement of Hounsfield units (HU) has been suggested as a surrogate marker for dual-energy x-ray absorptiometry scans. However, research establishing a threshold for HU at the uppermost instrumented vertebrae (UIV) that can mitigate the risk of mechanical and junctional complications following posterior spinal fusion is limited.

METHODS

Adults who underwent posterior spinal fusion and had 1-year follow-up data available were included in this retrospective cohort study. HU measurements were collected at various vertebral levels, including at the UIV. Inter- and intrarater reliability were calculated using the using intraclass correlation coefficients (ICCs). HU thresholds for mechanical and junctional complications were established using receiver operating characteristic (ROC) curve analyses, while accounting for age, sex, UIV level, and osteoporosis.

RESULTS

Across 70 patients, mean age was 64.4 years, 65.3% were female, and 10.0% had osteoporosis. The intrarater reliability ICCs were classified as excellent (ie, greater than 0.9) for all vertebral levels, whereas the interrater reliability ICCs were classified as good (ie, greater than 0.8) for all vertebral levels. ROC curve analyses identified a HU cutoff of 105. Patients below the HU threshold had markedly higher rates of mechanical and junctional complications (52.0% vs. 8.9%, P < 0.001), specifically adjacent segment disease (32.0% vs. 2.2%, P < 0.001).

CONCLUSION

CT-based measurement of HU at the upper instrumented vertebra has excellent intrarater and good interrater reliability. In this cohort of lumbar and thoracolumbar spinal fusion patients, patients with HU under 105 at the UIV had an increased risk of mechanical and junctional complications following spinal fusions. Future studies may work to refine HU assessment and the association with postoperative complications, as using HU to guide surgical candidacy and UIV selection may be beneficial.

Hounsfield units predict vertebral compression fractures in gastric cancer survivors after adjuvant irradiation

PURPOSE

This study aimed to investigate the risk factors and predictive value of vertebral Hounsfield units (HUs) for vertebral compression fracture (VCF) development in gastric cancer (GC) patients who received adjuvant radiotherapy (RT).

MATERIALS AND METHODS

We retrospectively analyzed the data of 271 patients with non-metastatic GC who received adjuvant RT between 2010 and 2020. The vertebral bodies from 9th thoracic (T9) to 2nd lumbar (L2) were contoured in computed tomographies used for RT planning, and V30, V35, V40, mean doses, and HUs of vertebrae were documented. We conducted univariate and multivariate analyses to identify the risk factors for VCF development.

RESULTS

The median follow-up time was 35.7 months. VCF developed in 23 patients (8.5%) in a median of 30.6 months (range, 3.4 to 117.3) after the end of RT. In total, 37 vertebrae were fractured, with 14 located in T12, nine in L1, seven in T11, four in L2, and three in T10. Older age, female sex, non-smoking status, and lower median vertebrae HUs were significantly associated with VCF in the univariate analysis. In the multivariate analysis, lower median HUs of T12 vertebrae (odds ratio, 0.965; 95% confidence interval, 0.942 to 0.989; p = 0.004) remained significant. The optimal cut-off value for T12 HU was 205.1, with an area under the receiver operating characteristic curve of 0.765, sensitivity of 85.7%, and specificity of 65%.

CONCLUSION

The lower median HU value of T12 vertebrae is a significant and independent risk factor for VCF development in GC patients who received adjuvant RT. HUs values serve as a simple and reliable predictor of VCF development in this population.

Measurement of lumbar vertebral body attenuation at PET-CT is a reliable method of diagnosing osteoporosis

AIM

This study investigates the potential of positron emission tomography-computed tomography (PET-CT) as a reliable tool for the diagnosis of osteoporosis, using attenuation values as a marker for bone mineral density (BMD) assessment.

MATERIALS AND METHODS

We retrospectively identified 305 patients who underwent both PET-CT and dual-energy X-ray absorptiometry (DEXA) within a six-month interval at a tertiary healthcare centre. Attenuation values were measured from the first lumbar vertebra (L1) on noncontrast CT images acquired during PET-CT scans. These values were then compared with corresponding DEXA T-scores to determine their diagnostic performance. Statistical analyses, including one-way Analysis of Variance (ANOVA), Pearson correlation, and receiver operating characteristic (ROC) curve analysis were employed to assess the correlation between PET-CT attenuation values and DEXA-defined osteoporosis.

RESULTS

The mean Hounsfield units (HU) differed significantly between groups classified by DEXA as osteoporosis, osteopenia, or normal BMD (P < 0.001). A strong correlation was found between HU and DEXA T-scores (Pearson coefficient = 0.65). Using logistic regression, we identified HU thresholds of 120 for 90% sensitivity and 98 for 90% specificity. The optimal balanced threshold was 109 HU, achieving both 80% sensitivity and specificity. The ROC curve for the model showed an area under the curve (AUC) of 0.88, indicating high diagnostic accuracy.

CONCLUSION

PET-CT can effectively screen for osteoporosis, offering a noninvasive, opportunistic diagnostic tool that requires no additional radiation exposure or resources. This study establishes 109 HU as the optimal threshold for diagnosing osteoporosis on PET-CT, providing a significant opportunity for early intervention and improved patient care.

Assessment of trabecular bone Hounsfield units in the lumbar spine for osteoporosis evaluation in individuals aged 65 and above: a review

Osteoporosis is a prevalent condition that significantly increases fracture risk, particularly in the elderly population. Despite its widespread occurrence, osteoporosis is often underdiagnosed and inadequately managed. Traditional diagnostic methods, such as dual-energy X-ray absorptiometry (DXA), have limitations in terms of accessibility and accuracy, necessitating exploration of alternative diagnostic approaches.This review aims to evaluate the diagnostic potential of Hounsfield Unit (HU) values derived from abdominal computed tomography (CT) scans, specifically focusing on the trabecular bone of the lumbar spine, for osteoporosis assessment in individuals aged 65 and older. The review seeks to assess the sensitivity, specificity, and overall diagnostic performance of HU values in distinguishing between normal bone density, osteopenia, and osteoporosis, and to identify areas for further investigation to establish standardized diagnostic criteria.This review compiles existing studies on the use of HU values from abdominal CT scans for osteoporosis diagnosis. It examines the relationship between HU values and DXA T-scores, analyzes optimal HU thresholds for classifying bone density categories, and explores the potential of CT scans as a viable alternative to DXA.The findings indicate that HU values from abdominal CT scans show strong correlations with DXA T-scores, suggesting a promising diagnostic tool for assessing bone density and quality. HU values have demonstrated the ability to differentiate between osteopenia, osteoporosis, and normal bone density, with varying sensitivity and specificity depending on the established HU threshold. CT scans are identified as a scalable, cost-effective alternative to DXA, with the added benefit of utilizing routine abdominal CT scans, which are often conducted for other clinical reasons, thereby reducing additional costs and radiation exposure.HU values derived from abdominal CT scans represent a promising approach for osteoporosis screening, offering a potential solution for routine, cost-effective, and accurate diagnosis, especially in older adults. However, there is a need for standardized HU thresholds and further research to refine diagnostic criteria and enhance the accuracy of osteoporosis detection. Establishing standardized guidelines would improve diagnostic consistency and facilitate early intervention, potentially improving patient outcomes and reducing healthcare burdens.

Opportunistic screening for osteoporosis using routine clinical care computed tomography brain studies

OBJECTIVE

Osteoporosis and falls are both prevalent in the elderly, and CT brain (CTB) is frequently performed post head-strike. We aim to validate the relationship between frontal bone density (Hounsfield unit) from routine CTB and bone mineral density from dual-energy X-ray absorptiometry (DEXA) scan for opportunistic osteoporosis screening.

MATERIALS AND METHODS

Patients who had a non-contrast CTB followed by a DEXA scan in the subsequent year were included in this multi-center retrospective study. The relationship between frontal bone density on CT and femoral neck T-score on DEXA was examined using ANOVA, Pearson's correlation, and receiver operating curve (ROC) analysis. Sensitivity, specificity, negative and positive predictive values, and area under the curve (AUC) were calculated.

RESULTS

Three hundred twenty-six patients (205 females and 121 males) were analyzed. ANOVA analysis showed that frontal bone density was lower in patients with DEXA-defined osteoporosis (p < 0.001), while Pearson's correlation analysis demonstrated a fair correlation with femoral neck T-score (r = 0.3, p < 0.001). On subgroup analysis, these were true in females but not in males. On ROC analysis, frontal bone density weakly predicted osteoporosis (AUC 0.6, 95% CI 0.5-0.7) with no optimal threshold identified. HU < 610 was highly specific (87.5%) but poorly sensitive (18.9%). HU > 1200 in females had a strong negative predictive value for osteoporosis (92.6%, 95% CI 87.1-98.1%).

CONCLUSION

Frontal bone density from routine CTB is significantly different between females with and without osteoporosis, but not between males. However, frontal bone density was a weak predictor for DEXA-defined osteoporosis. Further research is required to determine the role of CTB in opportunistic osteoporosis screening.

Opportunistic Screening for Low Bone Mineral Density in Routine Computed Tomography Scans: A Brazilian Validation Study

INTRODUCTION/BACKGROUND

Osteoporotic fractures are a major health concern worldwide due to high mortality rates, deterioration in quality of life, and elevated healthcare costs related to hospital treatment. However, most patients who sustain an osteoporotic fracture have never been formally screened for osteoporosis. Opportunistic screening of osteoporosis through conventional computed tomography (CT) scans performed for unrelated reasons could help identify patients with low bone mass. There are currently no studies validating the opportunistic screening of low bone mass through CT in South America. The aim of our study is to assess whether conventional CT scans could be used for the opportunistic screening of osteopenia and osteoporosis in Brazilian patients.

METHODOLOGY

Patients who underwent unenhanced CT and dual-energy X-ray absorptiometry (DXA) scans within a six-month interval were assessed retrospectively. Mean CT attenuation was measured in the first lumbar vertebra (L1) in axial, coronal, and sagittal planes and compared to their respective DXA T-scores; vertebral fractures were assessed in the sagittal plane. Potential thresholds suggestive of low bone mass density (BMD) were established using receiver operating characteristics analysis.

RESULTS

491 patients were included (93.2 % female; mean age of 64.1 ± 9.8 years; mean interval of 63.5 days between scans). Mean L1 CT attenuation was significantly lower in osteopenic and osteoporotic patients in all CT planes (p < 0.001). Positive linear correlations were found between DXA T-scores and the average L1 attenuations in all CT planes (p < 0.001). An average L1 attenuation equal or below 100 Hounsfield Units (HU) in the sagittal plane identified low BMD (osteopenia or osteoporosis) with a specificity of 96.3 % and a positive predictive value of 96 %. In contrast, an average L1 attenuation above 180 HU demonstrated a sensitivity of 97.6 % and a negative predictive value of 94.9 % for detecting osteoporosis. Patients with L1 sagittal attenuation at or below 100 HU exhibited a significantly higher prevalence of vertebral fractures (prevalence ratio: 8.67; p < 0.001). An online calculator based on the results of this study is freely available at www.osteotc.com.br.

CONCLUSIONS

Routine CT scans can identify probable low bone density (osteopenia or osteoporosis) in Brazilian patients without additional costs or radiation exposure. Opportunistic CT screening does not substitute formal bone mineral density assessment; instead, it assists in identifying patients who may benefit from it.

Opportunistic CT screening demonstrates increased risk for peri-articular fractures in osteoporotic patients

BACKGROUND

Underdiagnosis or undertreatment of osteoporosis consequently impacts individual morbidity and mortality, as well as on healthcare systems and communities as a whole. Dual-energy x-ray absorptiometry (DXA) is the gold standard method for identifying osteoporosis, however, opportunistic CT screening is capable of precisely estimating bone mineral density (BMD) in abdominopelvic imaging with no additional cost, radiation exposure or inconvenience to patients. This study uses opportunistic CT screening to determine the prevalence of osteoporosis and anatomic distribution patterns in patients presenting with lower extremity fractures at our institution.

HYPOTHESIS

Trauma patients with low bone mineral density (BMD) are more likely to present with peri-articular versus shaft fractures.

PATIENTS AND METHODS

We conducted a retrospective review of 721 patients presenting as trauma activations to the emergency department (ED) of a Level 1 Trauma Center with lower extremity fractures. Patients were excluded if under the age of 18 or lacking a CT scan upon arrival in the ED. Hounsfield Units (HU) were measured at the L1 vertebral level on CT scans to determine bone mineral density. Values of ≤100 HU were consistent with osteoporosis, whereas 101-150 HU were consistent with osteopenia.

RESULTS

The final cohort included 416 patients, with mean age of 49 ± 21 years. Average bone density was 203.9 ± 73.4 HU. 15.9% of patients were diagnosed as osteopenic and 9.9% as osteoporotic. 64.2% of fractures were peri-articular, 25.7% were shaft, and 10.1% were a combination. Peri-articular fractures were significantly more likely to have lower average BMD than shaft fractures (189 ± 74.7 HU vs. 230.6 ± 66.1 HU, p < 0.001).

DISCUSSION

Our study demonstrates a significant relationship between low bone mineral density and lower extremity fracture pattern, however, likely influenced by other factors such as sex. Opportunistic CT screening for osteoporosis in trauma settings provides ample opportunity for early detection of low BMD and implementation of highly effective lifestyle modification and pharmacotherapy intervention. Reduction in the overall incidence of peri-articular fracture with widespread adoption of opportunistic CT screening may lessen the morbidity, mortality, and total cost currently afflicting patients, healthcare systems, and communities.

LEVEL OF EVIDENCE

III, therapeutic.

Opportunistic Computed Tomography: A Novel Opportunity for Osteoporosis Screening

STUDY DESIGN

Retrospective review.

OBJECTIVE

To use opportunistic computed tomography (CT) screening to determine the prevalence of osteoporosis (OP) in patients presenting with spinal fractures and the rate of identification and treatment at our institution.

BACKGROUND

OP remains a highly underdiagnosed and undertreated disease. Opportunistic abdominopelvic CT scans offer a feasible, accessible, and cost-effective screening tool for OP.

METHODS

Retrospective review of 519 patients presenting as trauma activation to the emergency department of a Level 1 Trauma Center after a spinal fracture. Patients were excluded if under the age of 18 or lacking a CT scan upon arrival in the emergency department. Hounsfield Units (HU) were measured at the L1 vertebral level on CT scans to determine bone density levels. Values of ≤100 HU were considered osteoporotic, whereas 101-150 HU were osteopenic.

RESULTS

A total of 424 patients were included. The average HU was 204.8 ± 74.3 HU. Of the patients, 16.7% were diagnosed as osteopenic and 9.9% as osteoporotic. The mean age was 65 ± 14 years for osteopenic patients and 77 ± 11 years for osteoporotic. A statistically significant inverse relationship was found between age and bone density. Of the patients, 42.5% with low bone density HU measurements had a previously documented history of OP/osteopenia. There was a statistically significant association between females and low bone density. Patients injured in a fall were statistically significantly more likely to have lower bone densities than those in motor vehicle accidents. Of the osteoporotic patients, 9.5% were treated by our institution's fragility fracture team.

CONCLUSIONS

Our study shows that among a cohort of patients with spinal fractures, 58% of patients with radiographic signs of OP are currently undiagnosed, resulting in a low treatment rate of OP. Increasing and standardizing the use of opportunistic CT scans would allow an increase in the diagnosis and treatment of OP in patients with spinal fractures. Further, opportunistic CT scans could also be useful for a broader orthopedic population at high risk of fragility fractures.

LEVEL OF EVIDENCE

Level II-therapeutic.

A cost-free approach to evaluating vertebral body bone density and height loss in lung transplant recipients using routine chest CT

BACKGROUND

To assess changes in bone density and vertebral body height of patients undergoing lung transplant surgery using computed tomography (CT).

METHODS

This institutional review board (IRB) approved retrospective observational study enrolled patients with a history of lung transplant who had at least two chest CT scans. Vertebral body bone density (superior, middle, and inferior sections) and height (anterior, middle, and posterior sections) were measured at T1-T12 at baseline and follow up CT scans. Changes in the mean bone density, mean vertebral height, vertebral compression ratio (VBCR), percentage of anterior height compression (PAHC), and percentage of middle height compression (PMHC) were calculated and analyzed.

RESULTS

A total of 93 participants with mean age of 58 ± 12.3 years were enrolled. The most common underlying disease that led to lung transplants was interstitial lung diseases (57 %). The inter-scan interval was 34.06 ± 24.8 months. There were significant changes (p-value < 0.05) in bone density at all levels from T3 to T12, with the greatest decline at the T10 level from 163.06 HU to 141.84 HU (p-value < 0.05). The average VBCR decreased from 96.91 to 96.15 (p-value < 0.05).

CONCLUSION

Routine chest CT scans demonstrate a gradual decrease in vertebral body bone density over time in lung transplant recipients, along with evident anatomic changes such as vertebral body bone compression. This study shows that utilizing routine chest CT for lung transplant recipients can be regarded as a cost-free tool for assessing the vertebral body bone changes in these patients and potentially aiding in the prevention of complications related to osteoporosis.

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults

BACKGROUND

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality.

METHODS

The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141).

RESULTS

The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.

CONCLUSIONS

A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

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.

The role of sarcopenia in fragility fractures of the pelvis - is sarcopenia an underestimated risk factor?

BACKGROUND

Fragility fractures of the pelvis (FFPs) represent a significant health burden, particularly for the elderly. The role of sarcopenia, an age-related loss of muscle mass and function, in the development and impact of these fractures is not well understood. This study aims to investigate the prevalence and impact of osteoporosis and sarcopenia in patients presenting with FFPs.

METHODS

This retrospective study evaluated 140 elderly patients with FFPs. The diagnosis of sarcopenia was assessed by psoas muscle area (PMA) and the height-adjusted psoas muscle index (PMI) measured on computed tomography (CT) scans. Clinical data, radiological findings and functional outcomes were recorded and compared with the presence or absence of sarcopenia and osteoporosis.

RESULTS

Our study cohort comprised 119 female (85.0%) and 21 (15.0%) male patients. The mean age at the time of injury or onset of symptoms was 82.26 ± 8.50 years. Sarcopenia was diagnosed in 68.6% (n = 96) patients using PMA and 68.8% (n = 88) using PMI. 73.6% (n = 103) of our study population had osteoporosis and 20.0% (n = 28) presented with osteopenia. Patients with sarcopenia and osteoporosis had longer hospital stays (p < 0.04), a higher rate of complications (p < 0.048) and functional recovery was significantly impaired, as evidenced by a greater need for assistance in daily living (p < 0.03). However, they were less likely to undergo surgery (p < 0.03) and the type of FFP differed significantly (p < 0.04). There was no significant difference in mortality rate, pre-hospital health status, age or gender.

CONCLUSION

Our study highlights the important role of sarcopenia in FFPs in terms of the serious impact on health and quality of life in elderly patients especially when osteoporosis and sarcopenia occur together. Identifying and targeting sarcopenia in older patients may be an important strategy to reduce pelvic fractures and improve recovery. Further research is needed to develop effective prevention and treatment approaches that target muscle health in the elderly.

Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study

OBJECTIVES

We aimed to develop an automated method for measuring the volume of the psoas muscle using CT to aid sarcopenia research efficiently.

METHODS

We used a data set comprising the CT scans of 520 participants who underwent health check-ups at a health promotion centre. We developed a psoas muscle segmentation model using deep learning in a three-step process based on the nnU-Net method. The automated segmentation method was evaluated for accuracy, reliability, and time required for the measurement.

RESULTS

The Dice similarity coefficient was used to compare the manual segmentation with automated segmentation; an average Dice score of 0.927 ± 0.019 was obtained, with no critical outliers. Our automated segmentation system had an average measurement time of 2 min 20 s ± 20 s, which was 48 times shorter than that of the manual measurement method (111 min 6 s ± 25 min 25 s).

CONCLUSION

We have successfully developed an automated segmentation method to measure the psoas muscle volume that ensures consistent and unbiased estimates across a wide range of CT images.

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.

Interobserver variability in the determination of bone mineral density in Hounsfield units from differently configured fields of measurement in the cancellous bone of vertebral bodies from elderly body donors

Background

Due to the absence of suitable diagnostic procedures, osteoporosis (OP) is frequently detected late or not at all. Many elderly persons undergo computed tomographies (CT). The routine determination of Hounsfield units (HU) in bone as a part of these examinations could close a gap here.

Methods

Spines were extracted from 22 body donors, fixed in a PVC water phantom, and subjected to a high-resolution CT investigation. Cancellous bone was examined and its bone mineral density measured in HU from cervical vertebra 3 to lumbar vertebra 5 (484 vertebral bodies). On sagittal sections, a circular and a rectangular region of interest (ROI) were defined in mid-vertebral cancellous bone, positioned manually, and the measurements were performed by three experienced radiologists. Bone mineral density (BMD), measured in mg/cm3, was used to determine the presence of OP.

Results

All of the spines were osteoporotic. In the presence of a BMD below 60 mg/cm3 and HU values below 63.36 in lumbar vertebrae, there were significantly more vertebral body fractures in the thoracic and thoracolumbar spine. No difference was observed between the manually positioned circular and rectangular regions of interest (ROI) on the sagittal CT section (p > 0.05). Similar HU counts were obtained by the individual examiners (p > 0.05). The following formula was used to determine QCT values on a non-contrasted CT of the spine: QCT = 0.6 × HU + 13.7.

Conclusions

Measurement of the density of cancellous bone in HU can be used to determine BMD for estimating demineralization. Quantitative BMD values in mg/cm3, which can be calculated from the HU data, concur well with QCT values.

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.

A diagnostic approach integrated multimodal radiomics with machine learning models based on lumbar spine CT and X-ray for osteoporosis

INTRODUCTION

The aim of this analysis is to construct a combined model that integrates radiomics, clinical risk factors, and machine learning algorithms to diagnose osteoporosis in patients and explore its potential in clinical applications.

MATERIALS AND METHODS

A retrospective analysis was conducted on 616 lumbar spine. Radiomics features were extracted from the computed tomography (CT) scans and anteroposterior and lateral X-ray images of the lumbar spine. Logistic regression (LR), support vector machine (SVM), and random forest (RF) algorithms were used to construct radiomics models. The receiver operating characteristic curve (ROC) was employed to select the best-performing model. Clinical risk factors were identified through univariate logistic regression analysis (ULRA) and multivariate logistic regression analysis (MLRA) and utilized to develop a clinical model. A combined model was then created by merging radiomics and clinical risk factors. The performance of the models was evaluated using ROC curve analysis, and the clinical value of the models was assessed using decision curve analysis (DCA).

RESULTS

A total of 4858 radiomics features were extracted. Among the radiomics models, the SVM model demonstrated the optimal diagnostic capabilities and accuracy, with an area under the curve (AUC) of 0.958 (0.9405-0.9762) in the training cohort and 0.907 (0.8648-0.9492) in the test cohort. Furthermore, the combined model exhibited an AUC of 0.959 (0.9412-0.9763) in the training cohort and 0.910 (0.8690-0.9506) in the test cohort.

CONCLUSION

The combined model displayed outstanding ability in diagnosing osteoporosis, providing a safe and efficient method for clinical decision-making.

Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation

BACKGROUND

Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation.

METHODS

A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models.

RESULTS

The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model.

CONCLUSIONS

The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.