Prediction of osteoporosis at the sacrum using opportunistic CT of the abdomen and pelvis: a retrospective feasibility study in 277 patients comparing CT and QCT data

Prediction of osteoporosis at the sacrum using opportunistic CT of the abdomen and pelvis: a retrospective feasibility study in 277 patients comparing CT and QCT data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of osteoporosis at the sacrum using opportunistic CT of the abdomen and pelvis: a retrospective feasibility study in 277 patients comparing CT and QCT data Yan Xiao, Wen Li, Wenqin Zhou, Miao Wei, Bangyuan Long, Jiayi Pu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7716661/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To examine the distribution pattern of bone density in the L1-S3 vertebrae using opportunistic abdominopelvic imaging.QCT was employed as a reference to establish HU thresholds for the sacral vertebrae facilitating the prediction of osteoporosis and the exclusion of bone abnormalities. Methods A total of 277 subjects aged 19 to 81 years who underwent abdominopelvic CT were evaluated. Bone mineral density (BMD) measurements for the L1-S3 vertebrae and HU values for the S1-S3 vertebrae were collected. The study analyzed the correlation between sacral spine HU values and sacral spine BMD, along with the clinically utilized mean BMD for L1-L2, was analyzed. Receiver operating characteristic (ROC) curves were generated to identify the optimal diagnostic thresholds. Results The BMD of the lumbosacral vertebrae displayed a gradual decrease from L1 to L3, followed by an increase from L4 to S1, and a subsequent decline from S1 to S3. HU values of the sacral vertebrae across all planes were strongly correlated with both sacral spine BMD and the mean BMD values for L1-L2( r＝0.830 to 0.905, P ＜0.05）. For individual vertebrae, the area under the curve（AUC） of HU values for predicting osteoporosis ranged from 0.909 to 0.977, while the AUC for excluding bone abnormalities ranged from 0.933 to 0.950, with S1 demonstrating the highest predictive efficacy. The optimal threshold for S1 was >165.17 HU, yielding a specificity of 91.5% and a sensitivity of 83.0% for excluding bone abnormalities. Conversely, an S1 threshold of <130.50 HU resulted in a diagnostic specificity of 90.0% and a sensitivity of 96.6% for osteoporosis. Additionally, a predictive model that incorporated sex, age, and vertebral cancellous bone HU values achieved an AUC of 0.981. Conclusions Our data demonstrate a strong correlation between the HU values of the sacral spine and the clinically used BMD values for L1-L2, supporting the prediction of osteoporosis based on sacral spine HU values. Moreover, a predictive model that includes sex, age, and vertebral measurements offers improved diagnostic accuracy. Computed tomography QCT BMD Osteoporosis sacrum Hounsfield unit(HU) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Osteoporosis is a systemic metabolic bone disease characterized by reduced bone mass and microstructural deterioration, leading to increased bone fragility. This condition has a significant impact on patients' quality of life and can pose direct and indirect risks to life [ 1 – 2 ] . The prevalence of osteoporosis is rising rapidly due to an aging population and lifestyle changes, evolving into a global health concern that now affects over 200 million people [ 3 ] . Early screening for osteoporosis is essential for timely diagnosis and treatment, which can help lower the risk of fractures among high-risk individuals. However, current osteoporosis diagnosis rates remain low, with many cases only being detected at advanced stages, when fractures have already developed [ 4 ] . Dual-energy X-ray absorptiometry (DXA) is recommended by the World Health Organization (WHO) as the gold standard for the diagnosis of osteoporosis [ 5 ] . However, DXA results may be influenced by factors such as osteomalacia and vascular calcification, and a growing body of research has highlighted the limitations of relying exclusively on DXA for osteoporosis screening and fracture risk assessment [ 6 – 7 ] . Quantitative computed tomography (QCT) offers a widely used method of bone densitometry, enabling separate measurements of bone density in cortical and cancellous bone, which provides greater accuracy than DXA [ 8 ] . Despite these benefits, QCT requires additional scans and results in a higher radiation dose. With the extensive use of computed tomography (CT) and rising healthcare costs, the application of conventional CT imaging for opportunistic screening in at-risk populations has gained broad acceptance [ 9 – 12 ] . By measuring the Hounsfield units (HU) of vertebrae, bone density can be indirectly estimated, which vary considerably across spinal regions (cervicothoracic-lumbar spine). Previous studies have demonstrated the utility of routine chest and abdominal CT for opportunistic osteoporosis screening in various populations [ 10 – 12 ] . In clinical settings, however, pelvic CT scans that focus primarily on the sacral spine limit the comprehensive use of opportunistic BMD measurements. For example, patients presenting with lower back pain, often due to suspected gynecological conditions or urinary stones, typically undergo additional pelvic imaging, frequently excluding the sacral spine from assessment for opportunistic osteoporosis evaluation. As a result, this study had two objectives: (1) to examine the pattern of changes in BMD of the lumbosacral spine and evaluate the correlation between sacral spine HU values acquired from conventional CT, sacral spine BMD, and the mean BMD of L1-L2; and (2) to identify a potential critical HU threshold for diagnosing osteoporosis and excluding bone abnormalities in the sacral spine, using the clinically established mean BMD of L1-L2 as the reference standard. Methods Patients This study retrospectively analyzed patients undergoing CT scans at the Department of Radiology at the First Hospital of Chongqing Medical University from September 2022 to September 2024. The inclusion criteria were: 1) age ≥ 18 years, and 2) completion of at least one CT examination encompassing the lumbosacral region. Exclusion criteria included: 1) presence of spinal tuberculosis, bone tumors, ankylosing spondylitis, dense osteitis, or diffuse idiopathic osteomalacia; 2) history of lumbosacral vertebral fractures or surgeries; 3) other conditions affecting bone metabolism; and 4) anatomical variations of the lumbosacral vertebrae. This retrospective study strictly adhered to the Declaration of Helsinki and was approved by the institutional review board of chongqing medical university. Written informed consent was not required from patients. CT protocol and HU values measurements All CT examinations were conducted using a Siemens SOMATOM Force CT scanner. The scanning parameters were set as follows: tube voltage at 120 kV, automatic tube current, slice thickness of 5 mm, and reconstruction of 1 mm thin-slice images. To maintain the accuracy of measurements, the scanners were regularly calibrated using standard musculoskeletal module protocols. Picture Archiving and Communication System (PACS, Siemens) was used for CT measurements, using both axial and sagittal imaging positions. The measurement range included the L1-S3 vertebrae, and the CT measurement methodology involved manually outlining the region of interest (ROI) in the axial position, with subsequent adjustments in the sagittal position. The ROIs were selected at three specific levels of the vertebral body: just below the upper endplate, at the midpoint of the vertebral body, and just above the lower endplate. Efforts were made to exclude the bone cortex, isolated bone structures, hardened regions due to osteochondrosis, and the posterior central venous groove of the vertebral body. The ROI was defined to encompass the cancellous bone region as thoroughly as possible (Fig. 1 ). For each vertebra, HU values were measured at the three axial levels, and an average HU value was then calculated and recorded in Hounsfield units (HU). The individual conducting the measurements was blinded to the subjects' bone densitometry results to prevent any potential subjective bias in data collection. BMD measurement QCT measurements were conducted using the American Mindways (MW) QCT PRO V6.1 bone density analysis software. The 1-mm thin-slice CT images of the lumbosacral vertebrae were imported into the MW software. Using multiplanar reformation, axial images of the vertebral body at the central level were selected with reference to both coronal and sagittal planes. Regions of interest (ROIs) were manually outlined to exclude bone cortex, isolated bone structures, hardened areas such as osteophytes, and the posterior central venous grooves, ensuring the ROIs covered as much cancellous bone as possible (Fig. 2 ). The MW software then measured the BMD of vertebral cancellous bone from L1 to S3, automatically generating a BMD value for each vertebra in mg/cm³. Based on the diagnostic criteria for osteoporosis using lumbar spine QCT [ 13 ] , patients were categorized into three groups on the basis of their BMD values: osteoporosis (BMD ≤ 80 mg/cm³), osteopenia (80 mg/cm³ < BMD < 120 mg/cm³), and normal (BMD ≥ 120 mg/cm³). Statistical methods The statistical analysis was performed using SPSS version 29.0 software. Data normality was evaluated with the Shapiro-Wilk (S-W) test, which indicated that some of the sacral spine BMD and HU values did not follow a normal distribution; these values were thus reported as medians and interquartile ranges [M (P25, P75)]. Spearman's correlation coefficient was used to analyze the relationship between the HU values of the sacral vertebrae, sacral spine BMD, and the mean BMD of L1-L2. Logistic regression analysis was conducted to develop a model incorporating multiple indicators for predicting osteoporosis and excluding bone abnormalities. Optimal thresholds for excluding bone abnormalities and diagnosing osteoporosis were identified for each sacral vertebra using receiver operating characteristic (ROC) curves. The predictive value of sacral vertebrae HU values for normal and osteoporosis was assessed based on the area under the curve (AUC), with a p -value of less than 0.05 considered statistically significant. Results Differences in BMD and HU Values of the Lumbosacral Spine Among Patients with Normal, Osteopenia, and Osteoporosis After applying the inclusion and exclusion criteria, 277 patients in all, aged 19 to 81 years, with a mean age of 51.98 ± 12.21 years, were included in the study. This cohort consisted of 235 females and 42 males. Based on QCT diagnostic criteria, participants were classified into three groups: normal (141 cases), osteopenia (83 cases), and osteoporosis (53 cases). The bone density of the lumbosacral vertebrae showed a gradual decline from L1 to L3, an increase from L4 to S1, and a subsequent decrease from S1 to S3. The highest bone density was observed in the S1 vertebra at 176.43 (132.77, 214.91) mg/cm³, as illustrated in (Fig. 3 ). BMD values of the lumbosacral vertebrae decreased with age. Although males generally exhibited higher BMD than females across various age groups, particularly after age 50, the differences were not statistically significant ( P > 0.05) (Fig. 4 ). Table 1 shows the HU values of the sacral spine across the subgroups defined by QCT as normal, osteopenia, and osteoporosis. A statistically significant difference ( P < 0.05) was observed in the cancellous bone HU values of the sacral spine among the three groups, with the HU values of the S1-S3 vertebral bodies exceeding those of the corresponding BMD values. In addition, the percentage of females gradually increased in the three groups, and the average age also rose progressively within each group. Table 1 The HU values and BMD values of Sacral Spine based on QCT bone density classification Projects Classification based on QCT bone density (277) Normal (141) Osteopenia (83) Osteoporosis (53) Sex (n) Male 24(17%) 12(14%) 6(11%) Female 117(83%) 71(86%) 47(89%) Age/year 46.00(37.00,52.00) 57.00(52.00,61.000) 64.00(58.00,68.00) S1-HU values/HU 246.00(219.67,276.67) 168.67(151.33,189.67) 120.33(101.33,142.67) S2-HU values/HU 129.41(108.40,59.51) 74.60(56.25,94.27) 41.68(30.79,54.40) S3-HU values/HU 104.00(82.76,136.17) 44.67(27.33,62.00) 20.33(-8.50,35.34) S1-BMD values/(mg/cm³) 212.10(187.62,234.11) 151.63(130.30,174.17) 100.38(80.20,127.86) S2-BMD values/(mg/cm³) 128.67(108.17,162.84) 65.00(48.67,82.33) 25.67(14.67,50.00) S3-BMD values/(mg/cm³) 90.54(65.77,119.49) 43.02(19.44,68.16) 14.74(-2.15,32.37) HU average values/HU 158.44(142.00,190.39) 93.56(78.56,109.44) 58.67(41.83,71.67) BMD average values/(mg/cm³) 143.38(96.92,199.42) 84.43(49.83,130.71) 45.51(19.03,88.29) Note: HU-Hounsfield units, BMD-bone mineral density, HU average-mean HU values based on CT measurements for S1-S3 vertebrae, BMD average-mean BMD values based on QCT measurements for S1-S3 vertebrae. Correlation analysis between HU values of the sacral vertebrae and age, as well as the BMD of the sacral vertebrae and the mean BMD values of L1-L2 The HU values of each sacral vertebra demonstrated a significant negative correlation with age ( P < 0.001) (Table 2 ), consistent with the established inverse relationship between BMD and age. Additionally, a strong positive correlation was observed between the HU values of the sacral vertebrae and both the BMD values of the sacral vertebrae and the mean BMD values of L1-L2. The highest correlation was found between the HU values of S1 and the mean BMD values of L1-L2 (r = 0.905, P < 0.01), while the weakest correlation was noted between the HU values of S3 and the mean BMD values of L1-L2 (r = 0.844, P < 0.01). Notably, the correlation between the HU values of S2 and BMD was particularly strong (r = 0.900, P < 0.01). Table 2 Correlation between HU values of sacral spine with age, sacral spine BMD values and mean L1-L2 BMD values HU values Age Sacral spine BMD values L1-L2 BMD average values S1 -0.742 ** 0.890 ** 0.905 ** S2 -0.670 ** 0.900 ** 0.881 ** S3 -0.666 ** 0.830 ** 0.844 ** Note: **P<0.001 Prediction of osteoporosis and exclusion of bone abnormalities through logistic regression analysis of HU values in sacral vertebrae In individual vertebrae, the AUC for using HU values to predict osteoporosis ranged from 0.909 to 0.977, while the AUC for excluding bone abnormalities ranged from 0.933 to 0.950 (Table 3 ). The S1 vertebra displayed the highest AUC for both predicting osteoporosis and excluding bone abnormalities, exceeding the values observed for the S2 and S3 vertebrae. The optimal HU threshold for diagnosing osteoporosis at the S1 level was determined to be 130.50 HU, with a specificity of 90% and a sensitivity of 96.6%. In contrast, the optimal threshold for excluding bone abnormalities was identified as 165.17 HU, yielding a specificity of 91.5% and a sensitivity of 83.0%. Furthermore, when incorporating gender, age, and the S1-S3 vertebrae into the regression model for osteoporosis prediction, the combined model including age, gender, and the S1 vertebra produced the highest AUC value of 0.981, as illustrated in Fig. 5 . Table 3 ROC analysis to identify patients with or without osteoporosis and to detect the presence of bone abnormalities Classifications Mark Cut-off value AUC 95% CI P Specificity Sensitivity Jordon index Lower limit Up limit Osteoporosis Sex + Age + S1 - 0.981 0.967 0.994 < 0.001 0.920 0.988 0.912 S1 130.50 0.977 0.961 0.992 < 0.001 0.900 0.966 0.889 S12 96.00 0.970 0.950 0.990 < 0.001 0.880 0.973 0.868 S123 65.17 0.968 0.946 0.990 < 0.001 0.932 0.889 0.821 S2 36.17 0.928 0.886 0.970 < 0.001 0.956 0.741 0.697 S3 26.00 0.909 0.860 0.958 < 0.001 0.800 0.889 0.689 bone abnormalities S1 165.17 0.950 0.926 0.973 < 0.001 0.915 0.830 0.745 S2 84.84 0.947 0.923 0.970 < 0.001 0.831 0.920 0.751 S3 59.165 0.933 0.906 0.960 < 0.001 0.810 0.955 0.765 Discussion While both QCT and DXA are valuable tools for measuring BMD and predicting osteoporosis, each has its limitations. Opportunistic CT, on the other hand, utilizes existing clinical imaging data to assess bone density and screen for osteoporosis, without incurring additional costs or radiation exposure for patients, thus supporting its broader use in clinical practice [ 14 – 16 ] . Routine abdominopelvic CT scans, commonly employed for diagnosing and managing various diseases, generate images that offer meaningful insights into vertebral quality. This study evaluated the use of sacral vertebral HU values from abdominopelvic CT to predict BMD and established a threshold HU value for diagnosing osteoporosis in the sacral vertebrae. Our findings demonstrated a strong correlation between sacral spine HU values measured through opportunistic CT and the clinically accepted diagnostic criterion of mean BMD for L1-L2. Moreover, sacral spine HU values proved to be an effective metric for identifying patients with osteoporosis. In this study, BMD was typically lower for females than for males within the same age group, with significant differences becoming apparent after age 50. The proportion of females increased progressively across the groups from normal to osteopenia and, ultimately, to osteoporosis. This observation aligns with the well-known susceptibility of women over 50 to osteoporosis [ 17 ] , which is often attributed to the postmenopausal decline in estrogen levels that negatively impacts bone mineral density in females [ 18 ] . Additionally, Jang et al [ 12 ] reported a negative correlation between HU values and age. Consistent with these findings, our study showed that as age advances, bone mass reduces, trabecular bone thins, bone density declines, and the HU values of the vertebrae decrease accordingly. The findings of our study confirm significant variability in bone density across the lumbosacral vertebrae. Specifically, we observed a gradual decline in bone density from L1 to L3, followed by an increase from L4 to S1, which aligns closely with previous research [ 10 ] . Additionally, our study demonstrated a progressive decrease in bone density from S1 to S3 within the sacral vertebrae, with the S1 vertebra displaying significantly higher density than the other lumbosacral vertebrae. This difference may be due to the substantial weight-bearing load supported by the S1 vertebra. Previous studies have established a strong correlation between lumbar vertebral BMD and that of the cervicothoracic vertebrae [ 19 ] , suggesting a similar correlation between the sacral and lumbar vertebrae. This hypothesis was supported by our results, with correlation coefficients ranging from r = 0.844 to 0.90 ( P < 0.05). In our analysis, patients were classified into three groups based on BMD measured by QCT: normal, osteopenia, and osteoporosis. Statistically significant differences were detected in the sacral vertebral HU values among these groups, a result that contrasts with findings reported by Ping Wang [ 20 ] . We speculate that these differences may be due to variations in study populations and equipment used. Vertebral HU values are not only a useful supplementary tool for diagnosing osteoporosis [ 21 – 23 ] but also for assessing high fracture risk [ 24 ] . Established diagnostic thresholds for osteoporosis in the thoracic spine are 133.01 HU, with 208.85 HU as the threshold for excluding abnormal bone mass [ 23 ] . In the lumbar spine, the threshold for diagnosing osteoporosis is 106.38 HU, with a diagnostic specificity of 92.6% [ 25 ] . A prior study that evaluated the diagnostic effectiveness of the L4-S1 vertebrae for osteoporosis using abdominal CT and DXA in 50 patients reported an AUC of 0.65 for the S1 vertebra in predicting osteoporosis, with thresholds set at 207 HU for normal and 68 HU for osteoporosis [ 26 ] . In contrast, the present study identified 165.17 HU and 130.50 HU as thresholds for S1 to exclude bone abnormalities and diagnose osteoporosis, respectively, differing notably from prior findings. These threshold differences may stem from variations in race, equipment, measurement techniques, and sample sizes across studies. In this study, we combined age, sex, and sacral vertebrae HU values to predict osteoporosis, finding that this multi-factor model provided greater predictive accuracy than using single-segment vertebrae alone (combined AUC = 0.981 vs. single vertebrae AUC = 0.909–0.977). Additionally, this study compared the predictive capabilities of single-segment sacral vertebrae with multi-vertebrae combinations for osteoporosis, showing that single-segment predictions outperformed those of multi-vertebrae unions. We hypothesize that the smaller size of certain sacral vertebrae may hinder complete elimination of vertebral cortical bone influence during manual measurement. Considering that vertebral HU values can differ between manual and fully automated methods [ 27 ] , further studies using software for precise segmentation of cortical and cancellous bone in the sacral vertebrae are recommended. Osteoporotic fractures predominantly impact the thoracic and lumbar spine, which has led to a clinical focus on thoracolumbar BMD. However, the sacral spine can be especially susceptible to bone loss due to its relatively low mobility compared to other vertebrae and the lack of mechanical loading needed for bone remodeling and repair. Osteoporosis in the sacral spine often manifests as asymptomatic lower back pain, which can easily be misinterpreted as pain from lumbar disc degeneration or genitourinary disorders [ 28 ] . Thus, assessing bone density in the sacral spine is highly significant. Like the lumbar spine, the sacral spine undergoes degenerative changes. This study demonstrates that while absolute values may vary, the pattern of bone density reduction over time is comparable across different spinal regions. Rapid and accurate identification of abnormal bone density before sacral surgery is crucial, as recent studies indicate that postoperative screw loosening in the spine is linked to low BMD, highlighting the importance of BMD assessment in the sacral spine [ 29 ] . This study has several limitations: (i) It is a retrospective, single-center and single-device study with a small sample size, potentially introducing selection bias and limiting the generalizability of our results. In the future, multi-center and multi-device investigations involving large sample sizes will be conducted.(ii) Our study focused on the predictive efficacy of specific sacral spine HU values for diagnosing osteoporosis, without examining the performance of these HU values in predicting fractures. Future studies will aim to address fracture risk among our subjects. (iii) Current vertebral BMD assessments are primarily conducted in the lumbar spine, and measuring HU values in the sacral spine serves only as an opportunistic approach for high-risk individuals. Conclusion In conclusion, given the strong correlation between sacral spine HU values and the clinically utilized mean BMD values of L1-L2, osteoporosis can be diagnosed based on sacral spine HU values. Additionally, the combined prediction model that includes age, sex, and vertebrae provides greater accuracy than assessments based on individual vertebrae alone. 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García López A, Herrero Ezquerro MT, Martínez Pérez M (2024) Risk factor analysis of persistent low back pain after microdiscectomy: A retrospective study. Heliyon 10e38549. https://doi.org/10.1016/j.heliyon.2024.e38549. Filley A, Baldwin A, Ben-Natan AR et al (2024) The influence of osteoporosis on mechanical complications in lumbar fusion surgery: a systematic review. N Am Spine Soc J 18100327. https://doi.org/10.1016/j.xnsj.2024.100327. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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01:40:21","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106457,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/dfc6ccedc4208708b3482cff.html"},{"id":94806971,"identity":"a8446cae-1850-4ab8-9a8f-37600aa746d8","added_by":"auto","created_at":"2025-10-31 01:40:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91130,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of Hounsfield unit description: (\u003cstrong\u003eA\u003c/strong\u003e and \u003cstrong\u003eB\u003c/strong\u003e) axial section of the cranial region (HU); (\u003cstrong\u003eC\u003c/strong\u003e and \u003cstrong\u003eD\u003c/strong\u003e) axial section of the middle region (HU); (\u003cstrong\u003eE \u003c/strong\u003eand \u003cstrong\u003eF\u003c/strong\u003e) axial section of the caudal region (HU). Yellow circles: region of interest (ROI)and corresponding parameters (HU value). Red/Green lines: sagittal/coronal/axial localization lines.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/285e3399f00ca3d569d663fb.png"},{"id":94806969,"identity":"697b5cd9-793d-4ac4-b77a-e3baadcad444","added_by":"auto","created_at":"2025-10-31 01:40:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142953,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of bone mineral density (BMD)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/15de96fe49933118b3eac0a2.png"},{"id":94826191,"identity":"1da91ba4-00f9-4570-bdd6-0d9ac694a9a6","added_by":"auto","created_at":"2025-10-31 06:51:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44740,"visible":true,"origin":"","legend":"\u003cp\u003eTrend distribution of BMD in the lumbosacral spine\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/5fb21b5aee3aed415ef19a8e.png"},{"id":94806959,"identity":"1eb07a58-ddeb-4dff-9c75-d1588e0b52ef","added_by":"auto","created_at":"2025-10-31 01:40:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13648,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of BMD values for males and females in different age groups\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/f9a7cf15e861bf4b67e7ac82.png"},{"id":94806949,"identity":"21a65048-d5e6-4617-98ea-58f0b69252dd","added_by":"auto","created_at":"2025-10-31 01:40:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":173432,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating curve (ROC) of HU values for prediction of osteoporosis risk\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/7ecc22b6399434d69fe73d6d.png"},{"id":103262208,"identity":"88d4928c-7fba-4954-9c8e-bf33b9d4f7d6","added_by":"auto","created_at":"2026-02-23 18:25:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1269988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7716661/v1/64a059b8-bf3a-4188-b798-9b2c439189aa.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of osteoporosis at the sacrum using opportunistic CT of the abdomen and pelvis: a retrospective feasibility study in 277 patients comparing CT and QCT data","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoporosis is a systemic metabolic bone disease characterized by reduced bone mass and microstructural deterioration, leading to increased bone fragility. This condition has a significant impact on patients' quality of life and can pose direct and indirect risks to life\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. The prevalence of osteoporosis is rising rapidly due to an aging population and lifestyle changes, evolving into a global health concern that now affects over 200\u0026nbsp;million people\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Early screening for osteoporosis is essential for timely diagnosis and treatment, which can help lower the risk of fractures among high-risk individuals. However, current osteoporosis diagnosis rates remain low, with many cases only being detected at advanced stages, when fractures have already developed\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDual-energy X-ray absorptiometry (DXA) is recommended by the World Health Organization (WHO) as the gold standard for the diagnosis of osteoporosis\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, DXA results may be influenced by factors such as osteomalacia and vascular calcification, and a growing body of research has highlighted the limitations of relying exclusively on DXA for osteoporosis screening and fracture risk assessment\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Quantitative computed tomography (QCT) offers a widely used method of bone densitometry, enabling separate measurements of bone density in cortical and cancellous bone, which provides greater accuracy than DXA\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Despite these benefits, QCT requires additional scans and results in a higher radiation dose. With the extensive use of computed tomography (CT) and rising healthcare costs, the application of conventional CT imaging for opportunistic screening in at-risk populations has gained broad acceptance\u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. By measuring the Hounsfield units (HU) of vertebrae, bone density can be indirectly estimated, which vary considerably across spinal regions (cervicothoracic-lumbar spine). Previous studies have demonstrated the utility of routine chest and abdominal CT for opportunistic osteoporosis screening in various populations\u003csup\u003e[\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In clinical settings, however, pelvic CT scans that focus primarily on the sacral spine limit the comprehensive use of opportunistic BMD measurements. For example, patients presenting with lower back pain, often due to suspected gynecological conditions or urinary stones, typically undergo additional pelvic imaging, frequently excluding the sacral spine from assessment for opportunistic osteoporosis evaluation.\u003c/p\u003e\u003cp\u003eAs a result, this study had two objectives: (1) to examine the pattern of changes in BMD of the lumbosacral spine and evaluate the correlation between sacral spine HU values acquired from conventional CT, sacral spine BMD, and the mean BMD of L1-L2; and (2) to identify a potential critical HU threshold for diagnosing osteoporosis and excluding bone abnormalities in the sacral spine, using the clinically established mean BMD of L1-L2 as the reference standard.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003eThis study retrospectively analyzed patients undergoing CT scans at the Department of Radiology at the First Hospital of Chongqing Medical University from September 2022 to September 2024. The inclusion criteria were: 1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, and 2) completion of at least one CT examination encompassing the lumbosacral region. Exclusion criteria included: 1) presence of spinal tuberculosis, bone tumors, ankylosing spondylitis, dense osteitis, or diffuse idiopathic osteomalacia; 2) history of lumbosacral vertebral fractures or surgeries; 3) other conditions affecting bone metabolism; and 4) anatomical variations of the lumbosacral vertebrae. This retrospective study strictly adhered to the Declaration of Helsinki and was approved by the institutional review board of chongqing medical university. Written informed consent was not required from patients.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eCT protocol and HU values measurements\u003c/h3\u003e\n\u003cp\u003eAll CT examinations were conducted using a Siemens SOMATOM Force CT scanner. The scanning parameters were set as follows: tube voltage at 120 kV, automatic tube current, slice thickness of 5 mm, and reconstruction of 1 mm thin-slice images. To maintain the accuracy of measurements, the scanners were regularly calibrated using standard musculoskeletal module protocols. Picture Archiving and Communication System (PACS, Siemens) was used for CT measurements, using both axial and sagittal imaging positions. The measurement range included the L1-S3 vertebrae, and the CT measurement methodology involved manually outlining the region of interest (ROI) in the axial position, with subsequent adjustments in the sagittal position. The ROIs were selected at three specific levels of the vertebral body: just below the upper endplate, at the midpoint of the vertebral body, and just above the lower endplate. Efforts were made to exclude the bone cortex, isolated bone structures, hardened regions due to osteochondrosis, and the posterior central venous groove of the vertebral body. The ROI was defined to encompass the cancellous bone region as thoroughly as possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For each vertebra, HU values were measured at the three axial levels, and an average HU value was then calculated and recorded in Hounsfield units (HU). The individual conducting the measurements was blinded to the subjects' bone densitometry results to prevent any potential subjective bias in data collection.\u003c/p\u003e\n\u003ch3\u003eBMD measurement\u003c/h3\u003e\n\u003cp\u003eQCT measurements were conducted using the American Mindways (MW) QCT PRO V6.1 bone density analysis software. The 1-mm thin-slice CT images of the lumbosacral vertebrae were imported into the MW software. Using multiplanar reformation, axial images of the vertebral body at the central level were selected with reference to both coronal and sagittal planes. Regions of interest (ROIs) were manually outlined to exclude bone cortex, isolated bone structures, hardened areas such as osteophytes, and the posterior central venous grooves, ensuring the ROIs covered as much cancellous bone as possible (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The MW software then measured the BMD of vertebral cancellous bone from L1 to S3, automatically generating a BMD value for each vertebra in mg/cm\u0026sup3;. Based on the diagnostic criteria for osteoporosis using lumbar spine QCT\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, patients were categorized into three groups on the basis of their BMD values: osteoporosis (BMD\u0026thinsp;\u0026le;\u0026thinsp;80 mg/cm\u0026sup3;), osteopenia (80 mg/cm\u0026sup3; \u0026lt; BMD\u0026thinsp;\u0026lt;\u0026thinsp;120 mg/cm\u0026sup3;), and normal (BMD\u0026thinsp;\u0026ge;\u0026thinsp;120 mg/cm\u0026sup3;).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eThe statistical analysis was performed using SPSS version 29.0 software. Data normality was evaluated with the Shapiro-Wilk (S-W) test, which indicated that some of the sacral spine BMD and HU values did not follow a normal distribution; these values were thus reported as medians and interquartile ranges [M (P25, P75)]. Spearman's correlation coefficient was used to analyze the relationship between the HU values of the sacral vertebrae, sacral spine BMD, and the mean BMD of L1-L2. Logistic regression analysis was conducted to develop a model incorporating multiple indicators for predicting osteoporosis and excluding bone abnormalities. Optimal thresholds for excluding bone abnormalities and diagnosing osteoporosis were identified for each sacral vertebra using receiver operating characteristic (ROC) curves. The predictive value of sacral vertebrae HU values for normal and osteoporosis was assessed based on the area under the curve (AUC), with a \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDifferences in BMD and HU Values of the Lumbosacral Spine Among Patients with Normal, Osteopenia, and Osteoporosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter applying the inclusion and exclusion criteria, 277 patients in all, aged 19 to 81 years, with a mean age of 51.98\u0026thinsp;\u0026plusmn;\u0026thinsp;12.21 years, were included in the study. This cohort consisted of 235 females and 42 males. Based on QCT diagnostic criteria, participants were classified into three groups: normal (141 cases), osteopenia (83 cases), and osteoporosis (53 cases).\u003c/p\u003e\u003cp\u003eThe bone density of the lumbosacral vertebrae showed a gradual decline from L1 to L3, an increase from L4 to S1, and a subsequent decrease from S1 to S3. The highest bone density was observed in the S1 vertebra at 176.43 (132.77, 214.91) mg/cm\u0026sup3;, as illustrated in (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). BMD values of the lumbosacral vertebrae decreased with age. Although males generally exhibited higher BMD than females across various age groups, particularly after age 50, the differences were not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the HU values of the sacral spine across the subgroups defined by QCT as normal, osteopenia, and osteoporosis. A statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was observed in the cancellous bone HU values of the sacral spine among the three groups, with the HU values of the S1-S3 vertebral bodies exceeding those of the corresponding BMD values. In addition, the percentage of females gradually increased in the three groups, and the average age also rose progressively within each group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe HU values and BMD values of Sacral Spine based on QCT bone density classification\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProjects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eClassification based on QCT bone density (277)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNormal (141)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOsteopenia (83)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOsteoporosis (53)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSex (n)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12(14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6(11%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117(83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71(86%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47(89%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAge/year\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.00(37.00,52.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57.00(52.00,61.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64.00(58.00,68.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eS1-HU values/HU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e246.00(219.67,276.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e168.67(151.33,189.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e120.33(101.33,142.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eS2-HU values/HU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129.41(108.40,59.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e74.60(56.25,94.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41.68(30.79,54.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eS3-HU values/HU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.00(82.76,136.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.67(27.33,62.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.33(-8.50,35.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eS1-BMD values/(mg/cm\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e212.10(187.62,234.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e151.63(130.30,174.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100.38(80.20,127.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eS2-BMD values/(mg/cm\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e128.67(108.17,162.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.00(48.67,82.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.67(14.67,50.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eS3-BMD values/(mg/cm\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.54(65.77,119.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.02(19.44,68.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.74(-2.15,32.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHU average values/HU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158.44(142.00,190.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e93.56(78.56,109.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e58.67(41.83,71.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eBMD average values/(mg/cm\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e143.38(96.92,199.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.43(49.83,130.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.51(19.03,88.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: HU-Hounsfield units, BMD-bone mineral density, HU average-mean HU values based on CT measurements for S1-S3 vertebrae, BMD average-mean BMD values based on QCT measurements for S1-S3 vertebrae.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCorrelation analysis between HU values of the sacral vertebrae and age, as well as the BMD of the sacral vertebrae and the mean BMD values of L1-L2\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe HU values of each sacral vertebra demonstrated a significant negative correlation with age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), consistent with the established inverse relationship between BMD and age. Additionally, a strong positive correlation was observed between the HU values of the sacral vertebrae and both the BMD values of the sacral vertebrae and the mean BMD values of L1-L2. The highest correlation was found between the HU values of S1 and the mean BMD values of L1-L2 (r\u0026thinsp;=\u0026thinsp;0.905, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while the weakest correlation was noted between the HU values of S3 and the mean BMD values of L1-L2 (r\u0026thinsp;=\u0026thinsp;0.844, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, the correlation between the HU values of S2 and BMD was particularly strong (r\u0026thinsp;=\u0026thinsp;0.900, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation between HU values of sacral spine with age, sacral spine BMD values and mean L1-L2 BMD values\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHU values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSacral spine BMD values\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eL1-L2 BMD average values\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.742\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.890\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.905\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.670\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.900\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.666\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.830\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.844\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: **P\u0026lt;0.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePrediction of osteoporosis and exclusion of bone abnormalities through logistic regression analysis of HU values in sacral vertebrae\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn individual vertebrae, the AUC for using HU values to predict osteoporosis ranged from 0.909 to 0.977, while the AUC for excluding bone abnormalities ranged from 0.933 to 0.950 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The S1 vertebra displayed the highest AUC for both predicting osteoporosis and excluding bone abnormalities, exceeding the values observed for the S2 and S3 vertebrae. The optimal HU threshold for diagnosing osteoporosis at the S1 level was determined to be 130.50 HU, with a specificity of 90% and a sensitivity of 96.6%. In contrast, the optimal threshold for excluding bone abnormalities was identified as 165.17 HU, yielding a specificity of 91.5% and a sensitivity of 83.0%. Furthermore, when incorporating gender, age, and the S1-S3 vertebrae into the regression model for osteoporosis prediction, the combined model including age, gender, and the S1 vertebra produced the highest AUC value of 0.981, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eROC analysis to identify patients with or without osteoporosis and to detect the presence of bone abnormalities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClassifications\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMark\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCut-off value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eAUC\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSpecificity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSensitivity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eJordon index\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLower limit\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eUp limit\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOsteoporosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;S1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.968\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.958\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.800\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.889\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.689\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebone abnormalities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.926\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003eS3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.960\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile both QCT and DXA are valuable tools for measuring BMD and predicting osteoporosis, each has its limitations. Opportunistic CT, on the other hand, utilizes existing clinical imaging data to assess bone density and screen for osteoporosis, without incurring additional costs or radiation exposure for patients, thus supporting its broader use in clinical practice\u003csup\u003e[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Routine abdominopelvic CT scans, commonly employed for diagnosing and managing various diseases, generate images that offer meaningful insights into vertebral quality. This study evaluated the use of sacral vertebral HU values from abdominopelvic CT to predict BMD and established a threshold HU value for diagnosing osteoporosis in the sacral vertebrae. Our findings demonstrated a strong correlation between sacral spine HU values measured through opportunistic CT and the clinically accepted diagnostic criterion of mean BMD for L1-L2. Moreover, sacral spine HU values proved to be an effective metric for identifying patients with osteoporosis.\u003c/p\u003e\u003cp\u003eIn this study, BMD was typically lower for females than for males within the same age group, with significant differences becoming apparent after age 50. The proportion of females increased progressively across the groups from normal to osteopenia and, ultimately, to osteoporosis. This observation aligns with the well-known susceptibility of women over 50 to osteoporosis \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, which is often attributed to the postmenopausal decline in estrogen levels that negatively impacts bone mineral density in females\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Additionally, Jang et al\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e reported a negative correlation between HU values and age. Consistent with these findings, our study showed that as age advances, bone mass reduces, trabecular bone thins, bone density declines, and the HU values of the vertebrae decrease accordingly.\u003c/p\u003e\u003cp\u003eThe findings of our study confirm significant variability in bone density across the lumbosacral vertebrae. Specifically, we observed a gradual decline in bone density from L1 to L3, followed by an increase from L4 to S1, which aligns closely with previous research\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Additionally, our study demonstrated a progressive decrease in bone density from S1 to S3 within the sacral vertebrae, with the S1 vertebra displaying significantly higher density than the other lumbosacral vertebrae. This difference may be due to the substantial weight-bearing load supported by the S1 vertebra. Previous studies have established a strong correlation between lumbar vertebral BMD and that of the cervicothoracic vertebrae\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, suggesting a similar correlation between the sacral and lumbar vertebrae. This hypothesis was supported by our results, with correlation coefficients ranging from r\u0026thinsp;=\u0026thinsp;0.844 to 0.90 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In our analysis, patients were classified into three groups based on BMD measured by QCT: normal, osteopenia, and osteoporosis. Statistically significant differences were detected in the sacral vertebral HU values among these groups, a result that contrasts with findings reported by Ping Wang \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. We speculate that these differences may be due to variations in study populations and equipment used.\u003c/p\u003e\u003cp\u003eVertebral HU values are not only a useful supplementary tool for diagnosing osteoporosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e but also for assessing high fracture risk\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Established diagnostic thresholds for osteoporosis in the thoracic spine are 133.01 HU, with 208.85 HU as the threshold for excluding abnormal bone mass\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In the lumbar spine, the threshold for diagnosing osteoporosis is 106.38 HU, with a diagnostic specificity of 92.6%\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. A prior study that evaluated the diagnostic effectiveness of the L4-S1 vertebrae for osteoporosis using abdominal CT and DXA in 50 patients reported an AUC of 0.65 for the S1 vertebra in predicting osteoporosis, with thresholds set at 207 HU for normal and 68 HU for osteoporosis\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In contrast, the present study identified 165.17 HU and 130.50 HU as thresholds for S1 to exclude bone abnormalities and diagnose osteoporosis, respectively, differing notably from prior findings. These threshold differences may stem from variations in race, equipment, measurement techniques, and sample sizes across studies. In this study, we combined age, sex, and sacral vertebrae HU values to predict osteoporosis, finding that this multi-factor model provided greater predictive accuracy than using single-segment vertebrae alone (combined AUC\u0026thinsp;=\u0026thinsp;0.981 vs. single vertebrae AUC\u0026thinsp;=\u0026thinsp;0.909\u0026ndash;0.977). Additionally, this study compared the predictive capabilities of single-segment sacral vertebrae with multi-vertebrae combinations for osteoporosis, showing that single-segment predictions outperformed those of multi-vertebrae unions. We hypothesize that the smaller size of certain sacral vertebrae may hinder complete elimination of vertebral cortical bone influence during manual measurement. Considering that vertebral HU values can differ between manual and fully automated methods \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, further studies using software for precise segmentation of cortical and cancellous bone in the sacral vertebrae are recommended.\u003c/p\u003e\u003cp\u003eOsteoporotic fractures predominantly impact the thoracic and lumbar spine, which has led to a clinical focus on thoracolumbar BMD. However, the sacral spine can be especially susceptible to bone loss due to its relatively low mobility compared to other vertebrae and the lack of mechanical loading needed for bone remodeling and repair. Osteoporosis in the sacral spine often manifests as asymptomatic lower back pain, which can easily be misinterpreted as pain from lumbar disc degeneration or genitourinary disorders\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Thus, assessing bone density in the sacral spine is highly significant. Like the lumbar spine, the sacral spine undergoes degenerative changes. This study demonstrates that while absolute values may vary, the pattern of bone density reduction over time is comparable across different spinal regions. Rapid and accurate identification of abnormal bone density before sacral surgery is crucial, as recent studies indicate that postoperative screw loosening in the spine is linked to low BMD, highlighting the importance of BMD assessment in the sacral spine\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThis study has several limitations: (i) It is a retrospective, single-center and single-device study with a small sample size, potentially introducing selection bias and limiting the generalizability of our results. In the future, multi-center and multi-device investigations involving large sample sizes will be conducted.(ii) Our study focused on the predictive efficacy of specific sacral spine HU values for diagnosing osteoporosis, without examining the performance of these HU values in predicting fractures. Future studies will aim to address fracture risk among our subjects. (iii) Current vertebral BMD assessments are primarily conducted in the lumbar spine, and measuring HU values in the sacral spine serves only as an opportunistic approach for high-risk individuals.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, given the strong correlation between sacral spine HU values and the clinically utilized mean BMD values of L1-L2, osteoporosis can be diagnosed based on sacral spine HU values. Additionally, the combined prediction model that includes age, sex, and vertebrae provides greater accuracy than assessments based on individual vertebrae alone. We therefore recommend that measuring sacral spine HU values during routine pelvic CT scans be adopted as a predictive tool for osteoporosis risk. This approach can assist clinicians in selecting at-risk patients for more precise BMD examinations, supporting early osteoporosis diagnosis and the prevention of fragility fractures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eHuman Ethics and Consent to Participate declarations: not applicable.\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u0026nbsp;\u003c/strong\u003eThis research did not receive funding.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang J, Yang G, Liu S et al (2024) A machine learning method for precise detection of spine bone mineral density. Alexandria Engineering Journal, 98, 290-301. https://doi.org/10.1016/j.aej.2024.04.059.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003ePickhardt PJ, Graffy PM, Zea R et al (2020) Automated CT bio markers for opportunistic prediction of future cardiovascular events and mortality in an asymptomatic screening population: a retrospective cohort study. Lancet Digit Heal 2(4):e192\u0026ndash;e200.\u0026nbsp;https://doi: 10.1016/S2589-7500(20)30025-X.\u003c/li\u003e\n \u003cli\u003eYu JS, Krishna NG, Fox MG et al (2022) ACR Appropriateness Criteria\u0026reg; Osteoporosis and Bone Mineral Density: 2022 Update. J Am Coll Radiol 19S417-S432. https://doi.org/10.1016/j.jacr.2022.09.007.\u003c/li\u003e\n \u003cli\u003eLeslie WD, Giangregorio LM, Yogendran M et al (2012) A population-based analysis of the post-fracture care gap 1996-2008: the situation is not improving. Osteoporos Int, 23(5), 1623-1629. https://doi.org/10.1007/s00198-011-1630-1.\u003c/li\u003e\n \u003cli\u003eMorgan SL, Prater GL (2017) Quality in dual-energy X-ray absorptiometry scans. Bone 10413-28. https://doi.org/10.1016/j.bone.2017.01.033.\u003c/li\u003e\n \u003cli\u003eRitter J, Alimy AR, Simon A et al (2024) Patients with Periprosthetic Femoral Hip Fractures are Commonly Classified as Having Osteoporosis Based on DXA Measurements. Calcif Tissue Int 115142-149. https://doi.org/10.1007/s00223-024-01237-w.\u003c/li\u003e\n \u003cli\u003eGregson CL, Armstrong DJ, Bowden J et al (2022) UK clinical guideline for the prevention and treatment of osteoporosis. Arch Osteoporos 1758. https://doi.org/10.1007/s11657-022-01061-5.\u003c/li\u003e\n \u003cli\u003eEngelke K, Lang T, Khosla S et al (2015) Clinical Use of Quantitative Computed Tomography-Based Advanced Techniques in the Management of Osteoporosis in Adults: the 2015 ISCD Official Positions-Part III. J Clin Densitom 18393-407. https://doi.org/10.1016/j.jocd.2015.06.010.\u003c/li\u003e\n \u003cli\u003eKim YW, Kim JH, Yoon SH et al (2017) Vertebral bone attenuation on low-dose chest CT: quantitative volumetric analysis for bone fragility assessment. Osteoporos Int 28329-338. https://doi.org/10.1007/s00198-016-3724-2.\u003c/li\u003e\n \u003cli\u003eYang J, Liao M, Wang Y et al (2022) Opportunistic osteoporosis screening using chest CT with artificial intelligence. Osteoporos Int 332547-2561. https://doi.org/10.1007/s00198-022-06491-y.\u003c/li\u003e\n \u003cli\u003eSebro R, De la Garza-Ramos C (2023) Opportunistic screening for osteoporosis and osteopenia from CT scans of the abdomen and pelvis using machine learning. Eur Radiol 331812-1823. https://doi.org/10.1007/s00330-022-09136-0.\u003c/li\u003e\n \u003cli\u003eJang S, Graffy PM, Ziemlewicz TJ, Lee SJ, Summers RM, Pickhardt PJ (2019) Opportunistic Osteoporosis Screening at Routine Abdominal and Thoracic CT: Normative L1 Trabecular Attenuation Values in More than 20 000 Adults. Radiology 291360-367. https://doi.org/10.1148/radiol.2019181648.\u003c/li\u003e\n \u003cli\u003eShan T, Hanqing L, Qiuchi A et al (2023) Guidance for dysmorphic sacrum fixation with upper sacroiliac screw based on imaging anatomy study: techniques and indications. BMC Musculoskelet Disord 24536. https://doi.org/10.1186/s12891-023-06655-9.\u003c/li\u003e\n \u003cli\u003eBoutin RD, Lenchik L (2020) Value-Added Opportunistic CT: Insights Into Osteoporosis and Sarcopenia. AJR Am J Roentgenol 215582-594. https://doi.org/10.2214/AJR.20.22874.\u003c/li\u003e\n \u003cli\u003eNawa T, Fukui K, Nakayama T et al (2019) A population-based cohort study to evaluate the effectiveness of lung cancer screening using low-dose CT in Hitachi city, Japan. Jpn J Clin Oncol 49130-136. https://doi.org/10.1093/jjco/hyy185.\u003c/li\u003e\n \u003cli\u003eBott KN, Matheson BE, Smith A, Tse JJ, Boyd SK, Manske SL (2023) Addressing Challenges of Opportunistic Computed Tomography Bone Mineral Density Analysis. Diagnostics (Basel) 132572. https://doi.org/10.3390/diagnostics13152572.\u003c/li\u003e\n \u003cli\u003eClynes MA, Harvey NC, Curtis EM, Fuggle NR, Dennison EM, Cooper C (2020) The epidemiology of osteoporosis. Br Med Bull 133105-117. https://doi.org/10.1093/bmb/ldaa005\u003c/li\u003e\n \u003cli\u003eLindsay R (1996) The menopause and osteoporosis. Obstet Gynecol 8716S-19S. https://doi.org/10.1016/0029-7844(95)00430-0.\u003c/li\u003e\n \u003cli\u003eR\u0026uuml;hling S, Scharr A, Sollmann N et al (2022) Proposed diagnostic volumetric bone mineral density thresholds for osteoporosis and osteopenia at the cervicothoracic spine in correlation to the lumbar spine. Eur Radiol 326207-6214. https://doi.org/10.1007/s00330-022-08721-7.\u003c/li\u003e\n \u003cli\u003eWang P, She W, Mao Z et al (2021) Use of routine computed tomography scans for detecting osteoporosis in thoracolumbar vertebral bodies. Skeletal Radiol 50371-379. https://doi.org/10.1007/s00256-020-03573-y.\u003c/li\u003e\n \u003cli\u003ePu M, Zhang B, Zhu Y, Zhong W, Shen Y, Zhang P (2023) Hounsfield Unit for Evaluating Bone Mineral Density and Strength: Variations in Measurement Methods. World Neurosurg 180e56-e68. https://doi.org/10.1016/j.wneu.2023.07.146.\u003c/li\u003e\n \u003cli\u003eLiang X, Liu Q, Xu J, Ding W, Wang H (2022) Hounsfield Unit for Assessing Bone Mineral Density Distribution Within Cervical Vertebrae and Its Correlation With the Intervertebral Disc Degeneration. Front Endocrinol (Lausanne) 13920167.\u0026nbsp;https://doi.org/10.3389/fendo.2022.920167.\u003c/li\u003e\n \u003cli\u003eVanier AT, Colantonio D, Saxena SK, Rodkey D, Wagner S (2023) Computed Tomography of the Chest as a Screening Tool for Low Bone Mineral Density. Mil Med 188665-669. https://doi.org/10.1093/milmed/usab519.\u003c/li\u003e\n \u003cli\u003eLee SJ, Graffy PM, Zea RD, Ziemlewicz TJ, Pickhardt PJ (2018) Future Osteoporotic Fracture Risk Related to Lumbar Vertebral Trabecular Attenuation Measured at Routine Body CT. J Bone Miner Res 33860-867. https://doi.org/10.1002/jbmr.3383.\u003c/li\u003e\n \u003cli\u003eYang G, Wang H, Wu Z, Shi Y, Zhao Y (2022) Prediction of osteoporosis and osteopenia by routine computed tomography of the lumbar spine in different regions of interest. J Orthop Surg Res 17454. https://doi.org/10.1186/s13018-022-03348-2.\u003c/li\u003e\n \u003cli\u003eBerger-Groch J, Thiesen DM, Ntalos D, Hennes F, Hartel MJ (2020) Assessment of bone quality at the lumbar and sacral spine using CT scans: a retrospective feasibility study in 50 comparing CT and DXA data. Eur Spine J 291098-1104. https://doi.org/10.1007/s00586-020-06292-z.\u003c/li\u003e\n \u003cli\u003eSmith AD (2019) Screening of Bone Density at CT: An Overlooked Opportunity. Radiology 291368-369. https://doi.org/10.1148/radiol.2019190434.\u003c/li\u003e\n \u003cli\u003eGarc\u0026iacute;a L\u0026oacute;pez A, Herrero Ezquerro MT, Mart\u0026iacute;nez P\u0026eacute;rez M (2024) Risk factor analysis of persistent low back pain after microdiscectomy: A retrospective study. Heliyon 10e38549. https://doi.org/10.1016/j.heliyon.2024.e38549.\u003c/li\u003e\n \u003cli\u003eFilley A, Baldwin A, Ben-Natan AR et al (2024) The influence of osteoporosis on mechanical complications in lumbar fusion surgery: a systematic review. N Am Spine Soc J 18100327. https://doi.org/10.1016/j.xnsj.2024.100327.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Computed tomography, QCT, BMD, Osteoporosis, sacrum, Hounsfield unit(HU)","lastPublishedDoi":"10.21203/rs.3.rs-7716661/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7716661/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives \u003c/strong\u003eTo examine the distribution pattern of bone density in the L1-S3 vertebrae using opportunistic abdominopelvic imaging.QCT was employed as a reference to establish HU thresholds for the sacral vertebrae facilitating the prediction of osteoporosis and the exclusion of bone abnormalities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA total of 277 subjects aged 19 to 81 years who underwent abdominopelvic CT were evaluated. Bone mineral density (BMD) measurements for the L1-S3 vertebrae and HU values for the S1-S3 vertebrae were collected. The study analyzed the correlation between sacral spine HU values and sacral spine BMD, along with the clinically utilized mean BMD for L1-L2, was analyzed. Receiver operating characteristic (ROC) curves were generated to identify the optimal diagnostic thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe BMD of the lumbosacral vertebrae displayed a gradual decrease from L1 to L3, followed by an increase from L4 to S1, and a subsequent decline from S1 to S3. HU values of the sacral vertebrae across all planes were strongly correlated with both sacral spine BMD and the mean BMD values for L1-L2( r＝0.830 to 0.905, \u003cem\u003eP\u003c/em\u003e＜0.05）. For individual vertebrae, the area under the curve（AUC） of HU values for predicting osteoporosis ranged from 0.909 to 0.977, while the AUC for excluding bone abnormalities ranged from 0.933 to 0.950, with S1 demonstrating the highest predictive efficacy. The optimal threshold for S1 was \u0026gt;165.17 HU, yielding a specificity of 91.5% and a sensitivity of 83.0% for excluding bone abnormalities. Conversely, an S1 threshold of \u0026lt;130.50 HU resulted in a diagnostic specificity of 90.0% and a sensitivity of 96.6% for osteoporosis. Additionally, a predictive model that incorporated sex, age, and vertebral cancellous bone HU values achieved an AUC of 0.981.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eOur data demonstrate a strong correlation between the HU values of the sacral spine and the clinically used BMD values for L1-L2, supporting the prediction of osteoporosis based on sacral spine HU values. Moreover, a predictive model that includes sex, age, and vertebral measurements offers improved diagnostic accuracy.\u003c/p\u003e","manuscriptTitle":"Prediction of osteoporosis at the sacrum using opportunistic CT of the abdomen and pelvis: a retrospective feasibility study in 277 patients comparing CT and QCT data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 01:40:14","doi":"10.21203/rs.3.rs-7716661/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1df5432-4d3b-4321-8929-12b7dc11064d","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T18:24:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-31 01:40:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7716661","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7716661","identity":"rs-7716661","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}