Diagnostic Model for Distinguishing Fresh or Old Osteoporotic Vertebral Compression Fractures Based on Modified CT Window: A Retrospective Cohort Study

Diagnostic Model for Distinguishing Fresh or Old Osteoporotic Vertebral Compression Fractures Based on Modified CT Window: A Retrospective Cohort Study | 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 Diagnostic Model for Distinguishing Fresh or Old Osteoporotic Vertebral Compression Fractures Based on Modified CT Window: A Retrospective Cohort Study Shichu Wang, Zhenghan Han, Yiting Lei, Wenjun Liu, Tianji Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6297013/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 May, 2025 Read the published version in European Spine Journal → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose To develop a nomogram-based diagnostic model using CT imaging for rapid differentiation of fresh versus old osteoporotic vertebral compression fractures (OVCFs), particularly for patients contraindicated for MRI (e.g., those with metallic implants), emergency settings requiring immediate diagnosis, and resource-limited hospitals. Methods A retrospective analysis was conducted on OVCF patients from The First Affiliated Hospital of Chongqing Medical University (August 2022–December 2023). Modified CT window parameters (width: 400; level: 200) were applied to quantify vertebral features, including CT values, height reduction, endplate integrity, trabecular sparsity, Schmorl's nodes, and high-density shadows. Predictive variables were identified through univariate and multivariate logistic regression, followed by nomogram construction. Model performance was evaluated via ROC curves, calibration plots, Hosmer-Lemeshow test, and decision curve analysis (DCA). Results The nomogram integrated seven key imaging biomarkers, demonstrating robust discrimination with AUCs of 0.941 (training cohort) and 0.974 (validation cohort). Calibration was excellent (Hosmer-Lemeshow χ²=3.30, P = 0.95), and DCA confirmed substantial clinical net benefit across threshold probabilities. Conclusion This CT-based nomogram achieves high diagnostic accuracy for fresh OVCFs without MRI dependency, offering a practical tool for clinical decision-making in time-sensitive or resource-constrained scenarios. Osteoporotic Vertebral Compression Fractures Diagnostic Model CT Value Modified CT Window Technique Acute Lower Back Pain Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction OVCFs are the most frequent complication of osteoporosis, typically occurring in patients with decreased bone density from low-energy injuries or sometimes without any clear cause. Annually, around 1.4 million new OVCFs cases are reported worldwide, and about 40% of women will experience at least one OVCFs in their lifetime[ 1 ]. The incidence of OVCFs is projected to rise yearly with the ongoing aging of the population[ 2 ]. OVCFs are categorized into fresh and old types, vertebral augmentation surgery tends to be more effective for fresh OVCFs[ 3 ], whereas non-surgical treatments are generally adequate for old OVCFs. Thus, accurately differentiating between fresh and old OVCFs is essential in devising appropriate treatment plans. However, the current gold standard, MRI, faces several limitations in real-world clinical applications[ 4 , 5 ]. For instance, some patients are precluded from MRI due to internal metal objects or implants (such as post-aneurysm clipping, intraocular metal, magnetic internal fixation plates, vascular stents, pacemakers, vena cava filters, cochlear implants, prosthetics, artificial joints, metal drug pumps, dental fixtures, etc.) or claustrophobia[ 6 , 7 ], emergency patients cannot undergo rapid MRI scans. Additionally, MRI and emission computed tomography (ECT) equipment are not available in some remote areas or smaller hospitals. CT offers several notable advantages over MRI for diagnosing OVCFs: ①Higher availability and lower cost; ②Rapid scanning, ideal for emergency patient diagnosis[ 8 ]; ③Low sensitivity to metal and radioactive materials, suitable for patients with metal implants; ④High-resolution images for precise fracture location, shape, and severity; ⑤3D reconstruction capabilities for comprehensive fracture observation, aiding in severity assessment and treatment planning; ⑥CT values are a proven effective metric for bone density evaluation and osteoporosis diagnosis[ 9 ], with vertebral CT values showing a significant positive correlation with DXA and QCT measurements[ 10 ]. ⑦CT 's adjustable window width and level techniques offer detailed imaging of various tissues, enabling thoracic and abdominal CT scans to effectively screen for osteoporosis and vertebral fractures[ 11 ]. Based on the aforementioned context, we have developed an innovative diagnostic model that exclusively utilizes CT scans for the precise and swift differentiation between fresh and old OVCFs. Materials and Methods Patient & vertebral selection Conducted at The First Affiliated Hospital of Chongqing Medical University and Jinshan Campus, this clinical study retrospectively identified 108 patients diagnosed with OVCFs from August 2022 to December 2023. Out of the 236 vertebral bodies involved, 22 were excluded, leaving 214 vertebral bodies (109 fresh OVCFs and 105 old OVCFs) in the study. Osteoporosis was diagnosed based on WHO guidelines, with a T-score of ≤-2.5 standard deviations (SD) in the lumbar spine and hip, as measured by Dual-energy X-ray Absorptiometry (DXA). The diagnostic criteria for fresh OVCFs include the target vertebrae exhibiting low signal on T1WI, high or equal signal on T2WI, and a specific high signal on the STIR sequence. For old OVCFs, the diagnostic criteria involve the target vertebrae displaying vertebral compression changes on the MRI sagittal plane, equal or slightly high signal on T1WI, equal signal on T2WI, and low signal on the STIR sequence. Inclusion Criteria: (a) Participants must possess MRI, CT, and DXA examination records, with no more than 7 days between each examination; (b) The target vertebrae were diagnosed as OVCFs through MRI. (c) All participants should be confirmed with osteoporosis, evidenced by a DXA T-score of ≤-2.5SD. Exclusion Criteria: (a) Patients lacking complete medical or imaging records; (b) Those with fractures due to violent trauma or pathological conditions; (c) Those with previous spinal surgeries, or implants in target/adjacent vertebral bodies, or severe spinal deformities affecting accurate CT measurements. The study has been conducted and reported in accordance with the STARD (Standards for the Reporting of Diagnostic Accuracy Studies) criteria[ 12 ]. This retrospective study was carried out following the ethical guidelines set by the Ethics Office of the First Affiliated Hospital of Chongqing Medical University. All participants voluntarily enrolled in the study and gave their fully informed consent regarding the trial process. Data collection Patient demographics (age, gender), diagnosis, and lowest lumbar spine T-score from DXA were gathered using the hospital information system (HIS). MRI and CT images were reviewed, and relevant data were collected using the picture archiving and communication system (PACS). Initially, the PACS system was set to dual-view mode: the left window for sagittal plane reconstruction of the thinnest CT image layer, and the right window for the cross-sectional CT image (Fig. 1 ). Subsequently, the window width (WW) for both images in the views was adjusted to 400, and the window level (WL) was set to 200. Finally, under the modified CT window settings scrolling through the mouse facilitated the analysis of thoracolumbar CT sagittal and cross-sectional images to identify and document various aspects such as target vertebral segments, compression degree, CT values of the TV and adjacent (superior and inferior) vertebrae, TV endplate or cortical bone continuity, visible fracture lines in cancellous bone, high-density shadows, internal cavities exceeding 10mm in diameter, sclerosis or vacuum phenomena in adjacent intervertebral discs, and the presence of Schmorl's nodes in TV (Fig. 2 ). All data were gathered by three orthopedic physicians, with results being compiled collectively. In instances of differing opinions, the consensus of the majority was followed. The height of the vertebra ought to be measured at the lowest slice on the sagittal plane, focusing on the point with the least height (the height being the distance between the superior and inferior endplates, with the measurement line parallel to the posterior edge of the vertebra). Subsequently, the degree of vertebral compression can be calculated by either measuring the height of adjacent normal vertebrae or estimating the normal height of the TV. Additionally, the CT values of the TV and its adjacent superior and inferior vertebrae must be measured. During measurement, select as large a region of interest (ROI) as possible, avoiding structures such as the endplates, cortical bone, and basivertebral foramen, to automatically acquire the Hu and calculate their average, with at least three measurements per vertebra. When assessing high-density shadows in the TV, focus on distinctly brighter shadows within the cancellous bone on the sagittal plane, as compared to adjacent vertebrae, excluding those in the cortical bone or endplates. The continuity of the TV’s endplates or cortical bone, and the presence of Schmorl's nodes, should be carefully evaluated by scrolling through the sagittal plane slices to ensure no details are overlooked. Although assessing sparse cancellous texture in the TV is relatively subjective, under our modified CT window settings, accurate identification is feasible by comparing images of patients with normal bone mass to those with osteoporosis (Fig. 3 ). Statistical analysis The data for this study were analyzed and processed using SPSS version 27.0 and R Studio software. In the differential analysis of baseline data and image features, continuous variables were represented as mean ± standard deviation, and independent sample t-tests were utilized for inter-group comparisons. Categorical variables were presented as frequency or percentage, with chi-square tests applied for group comparisons. After converting the numerical variables into categorical variables using the quartile method, univariate logistic regression analysis was conducted on the training set. In this analysis, variables exhibiting p-values less than 0.06 were selected for subsequent multivariate logistic regression analysis. Model selection adhered to the Akaike Information Criterion (AIC) minimization principle, employing stepwise regression (both forward and backward). The final model was depicted as a nomogram. The Area Under the Curve (AUC) was calculated to assess diagnostic performance, while calibration was evaluated using calibration plots and the Hosmer-Lemeshow test. Additionally, DCA was used to assess the clinical utility of the model. Results Grouping and baseline A random sampling approach was employed for the 214 target vertebral bodies enrolled, dividing them into a training set (n = 152) and a validation set (n = 62) in a 7:3 ratio. The training set comprised 80 fresh OVCFs and 72 old OVCFs, while the validation set included 29 fresh OVCFs and 33 old OVCFs. The detailed process is illustrated in Fig. 4 . The training and validation sets' data on age, gender, the lowest lumbar spine T-score from DXA, and target vertebral segments are detailed in Table 1. CT characteristics comparison between the fresh and old OVCFs An intergroup analysis of the CT characteristics of fresh and old OVCFs (Table 2 ) revealed significant differences in 10 selected CT features between the groups (p < 0.05), except for the presence or absence of sclerosis or gaps in the intervertebral discs adjacent to the target vertebra. Table1 Baseline characteristics of the training set and validation set. Continuous variables are expressed as mean ± standard deviation，and compared between groups using independent sample t-tests. Categorical variables are represented by frequency and percentage, with inter-group comparisons conducted using chi-square tests. Abbreviations: OVCFs: Osteoporotic Vertebral Compression Fractures; DXA: Dual-energy X-ray Absorptiometry; TV: Target Vertebral. Training set(n=152) Validation set(n=62) Fresh OVCFs(n=80) Old OVCFs(n=72) P value Fresh OVCFs(n=29) Old OVCFs(n=33) P value Age(years) 70.83±9.54 73.44±10.07 0.102 66.66±9.89 73.15±7.83 0.006 Sex 0.359 0.845 Male, n (%) 9(11.25%) 5(6.94%) 4(13.79%) 4(12.12%) Female, n (%) 71(88.75%) 67(93.06%) 25(86.21%) 29(87.88%) Lowest DXA T-score -3.94±0.92 -4.30±0.89 0.016 -3.91±0.98 -4.29±0.98 0.124 TV Segment 0.127 0.100 T3 1(1.25%) 0(0.00%) 0(0.00%) 0(0.00%) T4 1(1.25%) 1(1.39%) 1(3.45%) 0(0.00%) T5 0(0.00%) 1(1.39%) 0(0.00%) 0(0.00%) T6 2(2.50%) 5(6.94%) 1(3.45%) 2(6.06%) T7 1(1.25%) 7(9.72%) 1(3.45%) 4(12.12%) T8 4(5.00%) 5(6.94%) 2(6.90%) 6(18.18%) T9 4(5.00%) 3(4.17%) 0(0.00%) 4(12.12%) T10 3(3.75%) 6(8.33%) 1(3.45%) 1(3.03%) T11 5(6.25%) 4(5.56%) 4(13.79%) 1(3.03%) T12 12(15.00%) 12(16.67%) 4(13.79%) 4(12.12%) L1 17(21.25%) 8(11.11%) 5(17.24%) 1(3.03%) L2 13(16.25%) 5(6.94%) 4(13.79%) 1(3.03%) L3 7(8.75%) 3(4.17%) 4(13.79%) 3(9.09%) L4 9(11.25%) 7(9.72%) 2(6.90%) 3(9.09%) L5 1(1.25%) 5(6.94%) 0(0.00%) 3(9.09%) Table 2 Analysis of differences between groups with fresh and old osteoporotic vertebral compression fractures. Fresh OVCFs(n = 109) Old OVCFs(n = 105) P value TV Compression Degree 0.40 ± 0.19 0.45 ± 0.17 < 0.05 TV Endplate or Cortex Discontinuity 90(82.57%) 29(27.62%) < 0.01 TV Sparse Trabecular Texture 58(53.21%) 95(90.48%) < 0.01 Fracture Line in TV Trabeculae 65(59.63%) 15(14.29%) < 0.01 TV Cancellous Bone High Density Shadow 89(81.65%) 30(28.57%) < 0.01 TV Cavity Over 10mm 17(15.60%) 41(39.05%) < 0.01 TV CT Value (HU) 132.13 ± 47.28 62.71 ± 40.81 < 0.01 CT Value Difference from Adjacent Vertebra Average (HU) 66.15 ± 51.16 1.89 ± 43.51 < 0.01 CT Value Ratio TV to Adjacent Average 2.69 ± 2.67 1.27 ± 1.33 < 0.01 TV with Schmorl's Nodes 3(2.75%) 24(22.86%) < 0.01 Sclerosis Vacuum Sign TV Disc 15(13.76%) 22(20.95%) 0.164 Continuous variables are expressed mean ± standard deviation, and comparisons between groups were performed using independent sample t-tests. Categorical variables are presented by frequency and percentage, with inter-group comparisons conducted using chi-square tests. Abbreviations: OVCFs: Osteoporotic Vertebral Compression Fractures; TV: Target Vertebral; HU: Hounsfield Units. Transforming numerical into categorical variables The original data included four numerical variables: degree of vertebral compression, CT value of the TV, difference in average CT value between the target and adjacent vertebrae, and the ratio of average CT values of the target to adjacent vertebrae. To improve the usability of the final nomogram model, numerical variables were transformed into categorical variables (Table 3 ). Logistic regression and model development were subsequently performed using the transformed data. Table 3 Numerical variable in the training set. Numerical variable in the training set. Range Mean 25% Quartile 50% (Median) Quartile 75% Quartile TV CT Value (HU) 2–335 98.07 56.25 90.5 133 CT Value Difference from Adjacent Vertebra Average (HU) -128.0–273.0 34.62 -2.75 24.25 67.375 CT Value Ratio TV to Adjacent Average 0.04–24.25 1.99 0.9625 1.415 2.19 TV Compression Degree 0.0–0.91 0.42 0.29 0.4 0.53 Numerical variables are categorized based on quartiles. 'TV CT Value' is divided into four groups: Group 1 ( 135). 'CT Value Difference from Adjacent Vertebra Average' is categorized into four groups: Group 1 ( 65). 'CT Value Ratio TV to Adjacent Average' is classified into four groups: Group 1 ( 2). 'TV Compression Degree' is classified into four groups: Group 1 ( 0.5). Abbreviations: TV: Target Vertebral; HU: Hounsfield Units Logistic regression and nomogram on training data Univariate logistic regression was applied to all variables in the training set, targeting the identification of either fresh or old OVCFs as the outcome variable. Risk factors with P-values < 0.06 in univariate logistic regression were selected for inclusion in multivariate logistic regression. Multivariate logistic regression was performed using a stepwise approach to identify the variable combination that most accurately distinguished between fresh and old OVCFs. The final model comprised 7 variables selected for their lowest AIC of 108.493. These variables included the CT value of the target vertebra, the ratio of average CT values between the target and adjacent vertebrae, the degree of target vertebral compression, the presence of high-density shadows in the cancellous bone, the continuity of the TV endplate or cortical bone, sparse cancellous texture of the TV, and TV with Schmorl's Nodes. The nomogram for the model was created using R Studio software. Results of the logistic regression are presented in Table 4 , with the nomogram illustrated in Fig. 5 . Table 4 Logistic regression analysis of the training set data. Univariate analysis Multivariate analysis OR (95%CI) P value OR (95%CI) P value TV Compression Degree 0.76(0.57-1) 0.052 2.42(1.24–4.72) 0.01 TV Endplate or Cortex Discontinuity 8.52(4.07–17.84) 0 6.43(1.74–23.82) 0.005 TV Sparse Trabecular Texture 0.14(0.06–0.34) 0 0.22(0.03–1.6) 0.136 Fracture Line in TV Trabeculae 8.77(4-19.2) 0 TV Cancellous Bone High Density Shadow 10.71(4.99–23.01) 0 1.56(0.88–2.76) 0.125 TV Cavity Over 10mm 0.29(0.14–0.64) 0.002 TV CT Value (HU) 4.91(3.05–7.9) 0 5.02(1.64–15.41) 0.005 CT Value Difference from Adjacent Vertebra Average (HU) 4.06(2.64–6.24) 0 CT Value Ratio TV to Adjacent Average 3.25(2.23–4.72) 0 0.37(0.22–0.64) 0 TV with Schmorl's Nodes 0.14(0.04–0.49) 0.002 0.34(0.08–1.39) 0.132 Sclerosis Vacuum Sign TV Disc 0.57(0.25–1.3) 0.18 Abbreviations: OR: Odds Ratio; CI: Confidence Interval; TV: Target Vertebral; HU: Hounsfield Units. Model Validation ROC analysis revealed that the nomogram model had an AUC of 0.941 (95% CI: 0.904–0.978) in the training set and 0.974 (95% CI: 0.938-1.000) in the validation set (Figs. 6 a and 6 b, respectively), demonstrating the model's strong discriminatory capacity. Moreover, the calibration plots for both training and validation sets showed slopes close to the ideal diagonal line (Figs. 6 c and 6 d). Hosmer-Lemeshow statistical tests in these sets resulted in (χ2 = 7.09, p = 0.63) and (χ2 = 3.30, p = 0.95) respectively; The results suggest excellent calibration of the model. Finally, DCA curves were plotted for both the training and validation sets (Fig. 7 ). The model's curves consistently remained above the curves for the "treat all" and "treat none" strategies, indicating its superiority across the entire threshold probability range, thus ensuring valuable clinical decision support in a wide array of clinical scenarios. Discussion Upon validation, our model demonstrated remarkable discriminatory ability and calibration performance. It comprises seven factors; three continuous variable factors were transformed into four categorical variables using the quartile method, while the remaining four are binary variables. Variables positively correlated with the diagnosis of fresh OVCFs include the CT value of the TV, CT value ratio of TV to adjacent average, TV cancellous bone high-density shadow, and TV endplate or cortex discontinuity. Conversely, variables negatively correlated include TV compression degree, TV sparse cancellous texture, and TV with Schmorl's nodes. Our detailed analysis of the factors influencing these variables reveals the following: The CT values of the vertebrae with fresh fractures increase due to the heightened bone density resulting from bleeding and edema in the vertebral bone marrow cavity during the acute fracture phase[ 13 ]; this is also evident as a prominent high-density shadow in the cancellous bone of the fractured vertebrae. In old fractures, the vertebral body undergoes further compression due to the patient not strictly adhering to bed rest post-injury, making the degree of compression more severe than in fresh fractures. On a foundation of osteoporosis, as the fracture heals and with prolonged reconstruction of trabecular bone structure, the old fracture vertebrae, despite being more compressed, tend to have sparser trabecular architecture[ 14 ]. After prolonged compression of the fractured endplates, the intervertebral discs may protrude into the vertebral body, forming Schmorl's nodes[ 15 ]. Conventional CT bone windows, which optimize the brightness and contrast of bone images, can clearly display skeletal details. Typically, the window width for these settings ranges from 800 to 2000 Hounsfield Units (Hu), and the window level is approximately between 250 and 500 Hu. These standard settings, however, may exhibit minor variations across different hospitals and scanning apparatus. In the context of patients with OVCFs, characterized by reduced bone density, an adjustment and refinement of the conventional CT window settings might be requisite. Kyung Joon Kim and colleagues[ 16 ] elucidated that setting a lumbar spine CT value threshold at 146 Hu enhances the diagnostic sensitivity and specificity for osteoporosis. In this vein, we modified the WL to 200 Hu and constricted the WW to 400 Hu. Such a modification decreases the CT value per grayscale from the standard window's 125 Hu (2000/16) to the modified window's 25 Hu (400/16), yielding a display range from 0 to 400 Hu. This enhancement in contrast facilitates a more distinct demarcation between bone and soft tissue, while the reduction in grayscale range aids in accentuating image details. Within our study, all vertebral images were exclusively examined and measured utilizing the modified CT window setting (WW: 400, WL: 200). Observations under these modified settings revealed that the contrast between cortical and cancellous bones was markedly amplified, and the visualization of soft tissues and intervertebral discs adjacent to the vertebrae was significantly enhanced. Notably, in osteoporotic vertebrae, the rarity of trabeculae was distinctly observable, and in instances of fresh compressive fractures, high-density shadows within the cancellous bone were more readily discernible (Fig. 8 ). When utilizing CT imaging for diagnosis, physicians can scroll through the images on both sagittal and axial planes to clearly and comprehensively observe the TV. Additionally, CT three-dimensional reconstruction technology enables an accurate assessment of the patient's spinal morphology and the specific condition of the TV. This information aids in determining the insertion angle of the puncture needle in subsequent vertebral augmentation surgery and in predicting the direction of potential bone cement leakage. Considering that OVCFs often recur, with the coexistence of both fresh and old fractures, our data collection excluded only those vertebrae with embedded materials in adjacent vertebrae. Cases with either fresh or old vertebral fractures in adjacent vertebrae were not excluded, thereby enhancing the model's extensive applicability. Elderly patients frequently present with multiple comorbidities. This model is versatile, suitable not only for thoracic and lumbar spine CT images but also for chest and abdominal CT scans[ 17 ]. In cases of elderly patients who are hospitalized in other departments, or have undergone recent CT examinations and seek orthopedic consultation for lower back pain, the model can utilize existing CT images for preliminary screening of fresh OVCFs. This approach potentially reduces the financial burden on these patients. In recent years, the advancement of Artificial Intelligence (AI) and Deep Learning (DL) has led to the development of numerous radiomics models for diagnosing fresh OVCFs. Weijuan Chen[ 18 ] created a DL model using X-rays for identifying fresh VCFs. Burns[ 19 ] designed an DL model utilizing CT scans to automate the detection and classification of OVCFs, as well as assessing bone density through CT value measurements. Xiao Hu[ 20 ]developed an DL model to predict OVCFs likelihood using CT. Yuan Li[ 21 ] created an DL model relying on CT images for distinguishing OVCFs from vertebral tumors. Jun Zhang[ 22 ] improved accuracy by employing an DL model in conjunction with clinical baseline data to differentiate between fresh and old OVCFs. However, in clinical practice, the use of radiomics DL models involves complex processes such as prior installation, computer environment setup, and downloading and processing patient images. Moreover, some models demand specific computer hardware, and hospitals' internal networks, used for data security, add to the inconvenience of deploying deep learning models in clinical settings. In contrast, clinical prediction models are established based on clinical data using logistic regression, a basic form of DL algorithm. Nomograms, being easy to use and without hardware prerequisites, are more readily adoptable compared to radiomics. The primary limitations of this model are as follows: ① It is derived from a single-center study with a limited sample size and lacks an external validation set. Future research could involve multi-center studies with larger sample sizes, incorporating images from diverse CT equipment to refine and enhance the model's robustness. ② The study's experimental design encompassed a limited number of independent variables. After a thorough literature review and team discussions, 11 CT image features were selected as independent variables, which might have led to the exclusion of other critical variables, such as CT observations of soft tissue injuries. ③ The model is not equipped to differentiate pathological fractures, including vertebral tumors and infections. Therefore, in clinical practice, it should be employed alongside patient symptoms and laboratory test results to avoid misdiagnosis or overlooked diagnoses. ④ The model is unsuitable for patients with implants in adjacent vertebrae, such as bone cement and pedicle screws, due to the challenges in accurately measuring CT values. This limitation restricts its use in patients who have undergone surgery and experience postoperative adjacent vertebral re-fractures. Conclusion Our modified CT window setting significantly enhances the visualization of bone marrow bleeding and edema associated with fresh OVCFs. The nomogram diagnostic model we developed, which combines this CT window setting with vertebral CT values, enables accurate differentiation between fresh and old OVCFs based solely on the patient's CT images. This model presents a valuable diagnostic tool, particularly beneficial for emergency physicians, patients with contraindications to MRI, and hospitals without MRI facilities. Declarations Acknowledgments The authors are grateful to all the colleagues who helped in the preparation of this article. Ethics approval and consent to participate Ethical approval from the institutional review board of the First Affiliated Hospital of Chongqing Medical University was obtained (K2023-131) and all patients signed informed consent forms. This study was conducted in accordance with the World Medical Association Declaration of Helsinki.Our study has passed the review of the Chinese Clinical Trial Registry, with the registration number ChiCTR2400083010. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding none. Authors' contributions Data extraction, Shichu Wang, Zhenghan Han; quality assessments, Yiting Lei; data analysis, Zhenghan Han, Tianji Huang; writing-origin draft, Zhenghan Han; writing-review and editing, Bo Liu and Yiting Lei. 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Prediction of subsequent osteoporotic vertebral compression fracture on CT radiography via deep learning. View . 2022;3(6):20220012. doi:10.1002/VIW.20220012 Li Y, Zhang Y, Zhang E, et al. Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning. Eur Radiol . 2021;31(12):9612-9619. doi:10.1007/s00330-021-08014-5 Zhang J, Liu F, Xu J, et al. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography. Frontiers in Endocrinology . 2023;14:1132725. doi:10.3389/fendo.2023.1132725 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 May, 2025 Read the published version in European Spine Journal → Version 1 posted Editorial decision: Revision requested 19 Apr, 2025 Reviews received at journal 19 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 02 Apr, 2025 Reviewers invited by journal 01 Apr, 2025 Editor assigned by journal 01 Apr, 2025 Submission checks completed at journal 01 Apr, 2025 First submitted to journal 24 Mar, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6297013","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":445074840,"identity":"65a4d080-d93f-490f-b810-463cb2b12380","order_by":0,"name":"Shichu Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shichu","middleName":"","lastName":"Wang","suffix":""},{"id":445074841,"identity":"6569304e-01e7-47ae-8c43-44b8860a9879","order_by":1,"name":"Zhenghan Han","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhenghan","middleName":"","lastName":"Han","suffix":""},{"id":445074842,"identity":"550ad6cc-b57b-41dd-9d6b-7fc9ae9b7d28","order_by":2,"name":"Yiting Lei","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Lei","suffix":""},{"id":445074843,"identity":"459c98a1-11e3-4348-ad15-6bddc4b39a64","order_by":3,"name":"Wenjun Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjun","middleName":"","lastName":"Liu","suffix":""},{"id":445074844,"identity":"1aead1c4-05b0-48e8-988c-22659f37c309","order_by":4,"name":"Tianji Huang","email":"","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tianji","middleName":"","lastName":"Huang","suffix":""},{"id":445074845,"identity":"e6be1a96-2f64-477e-8e09-df2df664b645","order_by":5,"name":"Bo Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACAwbmhgMMFQeATMYGBoYCmDgbPi2MQC1nwFoaG4Bc4rQwMLaBtIBYxGgxZ29sPMw7746c/Ozm9gc/DBjk+a6dMWD4UHaYgX92A1Ytlj0HGw7zbntmbHDnYGNjjwGD4czbOQaMM84dZpC4cwC7w24kgrQcTtwgkdjYwGPAkGAA1MLM23aYwUAiAY+WOYcT589IbGz8A9Pyl6CWhsOJDTcSG5vhtjDi0QLyy8E5xw6D/TJbxkAC6Je0goM959J5JG5g12LO3nz4w5uaw8AQa3/w8U2FjTzf7eSND36UWcvxz8CuBQEkYOQBMGLgIaAeroUBon4UjIJRMApGARIAAJGgabg64y9bAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-03-24 15:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6297013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6297013/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00586-025-08923-9","type":"published","date":"2025-05-21T15:58:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82009878,"identity":"f96e31db-709f-488f-bcc6-b163673ed390","added_by":"auto","created_at":"2025-05-06 01:24:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166003,"visible":true,"origin":"","legend":"\u003cp\u003eDual-window observation of CT images, featuring a sagittal reconstruction on the left and an axial cross-section on the right, with automatic correlation between the two. The collection of CT values necessitates measurement at three levels—upper, middle, and lower—of the vertebral body to acquire Hounsfield units (HU). The mean HU value is then calculated. It is critical to select the largest possible region of interest, while meticulously avoiding cortical bone, degenerative structures, and basivertebral foramen\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/bf1be7db806054344e3401fd.png"},{"id":82009914,"identity":"fcef08cb-7d1c-478b-ad95-32914ef3ee7a","added_by":"auto","created_at":"2025-05-06 01:24:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35825100,"visible":true,"origin":"","legend":"\u003cp\u003ea: Distinct fracture line in the cancellous bone with a gap larger than 10mm within the vertebral body. b: High-density shadows in the fractured vertebra, accompanied by a vacuum phenomenon in the adjacent intervertebral disc. c: Vertebral body exhibiting a Schmorl's nodes\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/d90141cafae7aebd77a802a7.png"},{"id":82009880,"identity":"a4e833d4-afef-49b1-8e88-3d46ce2aba34","added_by":"auto","created_at":"2025-05-06 01:24:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":223036,"visible":true,"origin":"","legend":"\u003cp\u003eModified CT Window Imaging. Panel a display the image of a patient with normal bone mass (lowest lumbar DXA T-score of 0.5) using a modified CT window setting (Window: 400, Center: 200). Panel b illustrates the image of a patient with osteoporosis (lowest lumbar DXA T-score of -2.9) under the same modified CT window setting (Window: 400, Center: 200). A comparative analysis clearly reveals sparse trabeculation in the patient with osteoporosis\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/2ea03d719ecd87fbb74db341.png"},{"id":82009904,"identity":"47ee2bb7-6fbc-4e70-9aa2-4b96fba090e7","added_by":"auto","created_at":"2025-05-06 01:24:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15266002,"visible":true,"origin":"","legend":"\u003cp\u003eResearch Flowchart\u003c/p\u003e\n\u003cp\u003eAbbreviation: TV: Target Vertebral body; OVCFs: Osteoporotic Vertebral Compression Fractures\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/9adb68806b99e11e6fbe2652.png"},{"id":82009889,"identity":"d3049435-e064-4f96-bc9e-25ccb8cec862","added_by":"auto","created_at":"2025-05-06 01:24:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4489479,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram Model for Diagnosing Fresh OVCFs. For all patients, the scores of all seven indicators identified on the scoring chart are summed. This sum is then plotted on the 'Total Score' axis, from which the corresponding probability of fresh OVCFs is deduced as the diagnostic probability\u003c/p\u003e\n\u003cp\u003eAbbreviation: TV: Target Vertebral body\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/6aac76a782e9f77b47978369.png"},{"id":82010526,"identity":"ceca0363-b538-4f8d-9d9d-f704e01751d0","added_by":"auto","created_at":"2025-05-06 01:32:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4705528,"visible":true,"origin":"","legend":"\u003cp\u003eROC and Calibration Curves for the Training and Validation Sets. Panels a and b display the ROC curves for the training and validation sets, respectively, with AUCs of 0.941 (95% CI: 0.904-0.978) and 0.947 (95% CI: 0.938-1.000). The optimal cutoff values are 0.605 for the training set and 0.448 for the validation set, with the corresponding 95% confidence intervals being (0.863,0.917) and (0.931,0.939), respectively. Panels c and d: The diagnostic model in both the training and validation sets shows a slope shape close to the ideal diagonal, with p-values of 0.63 and 0.95, respectively\u003c/p\u003e\n\u003cp\u003eAbbreviation: ROC: \u0026nbsp;Receiver Operating Characteristic; AUC: Area Under the Curve; CI: Confidence Interval\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/21928e011fcdf3386fd92f36.png"},{"id":82009885,"identity":"497db17f-dbda-4fb5-bfbf-c4020cf670c0","added_by":"auto","created_at":"2025-05-06 01:24:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1541336,"visible":true,"origin":"","legend":"\u003cp\u003eDCA for the Training and Validation Sets. The model's curve consistently remains above the strategy lines of 'all' and 'none', indicating the model has a higher net benefit and good cli Decision Curve Analysis nical applicability.\u003c/p\u003e\n\u003cp\u003eAbbreviation: DCA=Decision Curve Analysis\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/e2273c549959950df5d06bfb.png"},{"id":82010523,"identity":"d1a948ed-3173-4c2c-842b-a36b54065740","added_by":"auto","created_at":"2025-05-06 01:32:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":244074,"visible":true,"origin":"","legend":"\u003cp\u003eFresh Osteoporotic Vertebral Compression Fractures (OVCFs) in the L2 Vertebral Body. Panel a: Lumbar MRI indicating a fresh compressive fracture in the L2 vertebral body. Panels b and d: Images of the patient under conventional bone window settings (Window: 2000, Center: 800). Panels c and e: Images of the same patient using modified CT window settings (Window: 400, Center: 200). Comparative analysis demonstrates that the modified CT\u003c/p\u003e\n\u003cp\u003ewindow significantly enhances image contrast, thereby augmenting the visual recognition of trabeculae and bone marrow edema resulting from hemorrhage\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6297013/v1/4b85353c311453cdb2018d54.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diagnostic Model for Distinguishing Fresh or Old Osteoporotic Vertebral Compression Fractures Based on Modified CT Window: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOVCFs are the most frequent complication of osteoporosis, typically occurring in patients with decreased bone density from low-energy injuries or sometimes without any clear cause. Annually, around 1.4\u0026nbsp;million new OVCFs cases are reported worldwide, and about 40% of women will experience at least one OVCFs in their lifetime[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The incidence of OVCFs is projected to rise yearly with the ongoing aging of the population[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOVCFs are categorized into fresh and old types, vertebral augmentation surgery tends to be more effective for fresh OVCFs[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], whereas non-surgical treatments are generally adequate for old OVCFs. Thus, accurately differentiating between fresh and old OVCFs is essential in devising appropriate treatment plans. However, the current gold standard, MRI, faces several limitations in real-world clinical applications[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For instance, some patients are precluded from MRI due to internal metal objects or implants (such as post-aneurysm clipping, intraocular metal, magnetic internal fixation plates, vascular stents, pacemakers, vena cava filters, cochlear implants, prosthetics, artificial joints, metal drug pumps, dental fixtures, etc.) or claustrophobia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], emergency patients cannot undergo rapid MRI scans. Additionally, MRI and emission computed tomography (ECT) equipment are not available in some remote areas or smaller hospitals.\u003c/p\u003e \u003cp\u003eCT offers several notable advantages over MRI for diagnosing OVCFs: ①Higher availability and lower cost; ②Rapid scanning, ideal for emergency patient diagnosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; ③Low sensitivity to metal and radioactive materials, suitable for patients with metal implants; ④High-resolution images for precise fracture location, shape, and severity; ⑤3D reconstruction capabilities for comprehensive fracture observation, aiding in severity assessment and treatment planning; ⑥CT values are a proven effective metric for bone density evaluation and osteoporosis diagnosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], with vertebral CT values showing a significant positive correlation with DXA and QCT measurements[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. ⑦CT 's adjustable window width and level techniques offer detailed imaging of various tissues, enabling thoracic and abdominal CT scans to effectively screen for osteoporosis and vertebral fractures[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on the aforementioned context, we have developed an innovative diagnostic model that exclusively utilizes CT scans for the precise and swift differentiation between fresh and old OVCFs.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient \u0026amp; vertebral selection\u003c/h2\u003e \u003cp\u003eConducted at The First Affiliated Hospital of Chongqing Medical University and Jinshan Campus, this clinical study retrospectively identified 108 patients diagnosed with OVCFs from August 2022 to December 2023. Out of the 236 vertebral bodies involved, 22 were excluded, leaving 214 vertebral bodies (109 fresh OVCFs and 105 old OVCFs) in the study.\u003c/p\u003e \u003cp\u003e Osteoporosis was diagnosed based on WHO guidelines, with a T-score of \u0026le;-2.5 standard deviations (SD) in the lumbar spine and hip, as measured by Dual-energy X-ray Absorptiometry (DXA). The diagnostic criteria for fresh OVCFs include the target vertebrae exhibiting low signal on T1WI, high or equal signal on T2WI, and a specific high signal on the STIR sequence. For old OVCFs, the diagnostic criteria involve the target vertebrae displaying vertebral compression changes on the MRI sagittal plane, equal or slightly high signal on T1WI, equal signal on T2WI, and low signal on the STIR sequence.\u003c/p\u003e \u003cp\u003eInclusion Criteria: (a) Participants must possess MRI, CT, and DXA examination records, with no more than 7 days between each examination; (b) The target vertebrae were diagnosed as OVCFs through MRI. (c) All participants should be confirmed with osteoporosis, evidenced by a DXA T-score of \u0026le;-2.5SD. Exclusion Criteria: (a) Patients lacking complete medical or imaging records; (b) Those with fractures due to violent trauma or pathological conditions; (c) Those with previous spinal surgeries, or implants in target/adjacent vertebral bodies, or severe spinal deformities affecting accurate CT measurements.\u003c/p\u003e \u003cp\u003eThe study has been conducted and reported in accordance with the STARD (Standards for the Reporting of Diagnostic Accuracy Studies) criteria[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e This retrospective study was carried out following the ethical guidelines set by the Ethics Office of the First Affiliated Hospital of Chongqing Medical University. All participants voluntarily enrolled in the study and gave their fully informed consent regarding the trial process.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003ePatient demographics (age, gender), diagnosis, and lowest lumbar spine T-score from DXA were gathered using the hospital information system (HIS). MRI and CT images were reviewed, and relevant data were collected using the picture archiving and communication system (PACS). Initially, the PACS system was set to dual-view mode: the left window for sagittal plane reconstruction of the thinnest CT image layer, and the right window for the cross-sectional CT image (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Subsequently, the window width (WW) for both images in the views was adjusted to 400, and the window level (WL) was set to 200. Finally, under the modified CT window settings scrolling through the mouse facilitated the analysis of thoracolumbar CT sagittal and cross-sectional images to identify and document various aspects such as target vertebral segments, compression degree, CT values of the TV and adjacent (superior and inferior) vertebrae, TV endplate or cortical bone continuity, visible fracture lines in cancellous bone, high-density shadows, internal cavities exceeding 10mm in diameter, sclerosis or vacuum phenomena in adjacent intervertebral discs, and the presence of Schmorl's nodes in TV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All data were gathered by three orthopedic physicians, with results being compiled collectively. In instances of differing opinions, the consensus of the majority was followed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe height of the vertebra ought to be measured at the lowest slice on the sagittal plane, focusing on the point with the least height (the height being the distance between the superior and inferior endplates, with the measurement line parallel to the posterior edge of the vertebra). Subsequently, the degree of vertebral compression can be calculated by either measuring the height of adjacent normal vertebrae or estimating the normal height of the TV. Additionally, the CT values of the TV and its adjacent superior and inferior vertebrae must be measured. During measurement, select as large a region of interest (ROI) as possible, avoiding structures such as the endplates, cortical bone, and basivertebral foramen, to automatically acquire the Hu and calculate their average, with at least three measurements per vertebra. When assessing high-density shadows in the TV, focus on distinctly brighter shadows within the cancellous bone on the sagittal plane, as compared to adjacent vertebrae, excluding those in the cortical bone or endplates. The continuity of the TV\u0026rsquo;s endplates or cortical bone, and the presence of Schmorl's nodes, should be carefully evaluated by scrolling through the sagittal plane slices to ensure no details are overlooked. Although assessing sparse cancellous texture in the TV is relatively subjective, under our modified CT window settings, accurate identification is feasible by comparing images of patients with normal bone mass to those with osteoporosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data for this study were analyzed and processed using SPSS version 27.0 and R Studio software. In the differential analysis of baseline data and image features, continuous variables were represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and independent sample t-tests were utilized for inter-group comparisons. Categorical variables were presented as frequency or percentage, with chi-square tests applied for group comparisons. After converting the numerical variables into categorical variables using the quartile method, univariate logistic regression analysis was conducted on the training set. In this analysis, variables exhibiting p-values less than 0.06 were selected for subsequent multivariate logistic regression analysis. Model selection adhered to the Akaike Information Criterion (AIC) minimization principle, employing stepwise regression (both forward and backward). The final model was depicted as a nomogram. The Area Under the Curve (AUC) was calculated to assess diagnostic performance, while calibration was evaluated using calibration plots and the Hosmer-Lemeshow test. Additionally, DCA was used to assess the clinical utility of the model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGrouping and baseline\u003c/h2\u003e \u003cp\u003eA random sampling approach was employed for the 214 target vertebral bodies enrolled, dividing them into a training set (n\u0026thinsp;=\u0026thinsp;152) and a validation set (n\u0026thinsp;=\u0026thinsp;62) in a 7:3 ratio. The training set comprised 80 fresh OVCFs and 72 old OVCFs, while the validation set included 29 fresh OVCFs and 33 old OVCFs. The detailed process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The training and validation sets' data on age, gender, the lowest lumbar spine T-score from DXA, and target vertebral segments are detailed in Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCT characteristics comparison between the fresh and old OVCFs\u003c/h2\u003e \u003cp\u003eAn intergroup analysis of the CT characteristics of fresh and old OVCFs (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed significant differences in 10 selected CT features between the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for the presence or absence of sclerosis or gaps in the intervertebral discs adjacent to the target vertebra.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable1\u003c/strong\u003e Baseline characteristics of the training set and validation set.\u003c/p\u003e\n\u003cp\u003eContinuous variables are expressed as mean \u0026plusmn; standard deviation，and compared between groups using independent sample t-tests. Categorical variables are represented by frequency and percentage, with inter-group comparisons conducted using chi-square tests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: OVCFs: Osteoporotic Vertebral Compression Fractures; DXA: Dual-energy X-ray Absorptiometry; TV: Target Vertebral.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"645\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 256px;\"\u003e\n \u003cp\u003eTraining set(n=152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 265px;\"\u003e\n \u003cp\u003eValidation set(n=62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"27\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eFresh OVCFs(n=80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eOld OVCFs(n=72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003eFresh OVCFs(n=29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003eOld OVCFs(n=33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"45\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e70.83\u0026plusmn;9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e73.44\u0026plusmn;10.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.102\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e66.66\u0026plusmn;9.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e73.15\u0026plusmn;7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.006\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.845\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e9(11.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4(13.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4(12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e71(88.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e67(93.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e25(86.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e29(87.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLowest DXA T-score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e-3.94\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-4.30\u0026plusmn;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.016\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e-3.91\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-4.29\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.124\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eTV Segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.127\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1(1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1(1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1(1.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1(3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1(1.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2(2.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1(3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e2(6.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1(1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7(9.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1(3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4(12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4(5.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2(6.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e6(18.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"21\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4(5.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3(4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4(12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3(3.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6(8.33%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1(3.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1(3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e5(6.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4(5.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4(13.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1(3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e12(15.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12(16.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4(13.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e4(12.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e17(21.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8(11.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5(17.24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1(3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e13(16.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4(13.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1(3.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eL3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e7(8.75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3(4.17%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e4(13.79%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3(9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eL4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e9(11.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7(9.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2(6.90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3(9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eL5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1(1.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5(6.94%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e0(0.00%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e3(9.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of differences between groups with fresh and old osteoporotic vertebral compression fractures.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFresh OVCFs(n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOld OVCFs(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Compression Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Endplate or Cortex Discontinuity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(82.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(27.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Sparse Trabecular Texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58(53.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95(90.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture Line in TV Trabeculae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65(59.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(14.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Cancellous Bone\u003c/p\u003e \u003cp\u003eHigh Density Shadow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(81.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(28.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Cavity Over 10mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(15.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(39.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV CT Value (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e132.13\u0026thinsp;\u0026plusmn;\u0026thinsp;47.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.71\u0026thinsp;\u0026plusmn;\u0026thinsp;40.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Value Difference from Adjacent \u003c/p\u003e \u003cp\u003eVertebra Average (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.15\u0026thinsp;\u0026plusmn;\u0026thinsp;51.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u0026thinsp;\u0026plusmn;\u0026thinsp;43.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Value Ratio TV to Adjacent Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV with Schmorl's Nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(22.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSclerosis Vacuum Sign TV Disc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(13.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(20.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are expressed mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and comparisons between groups were performed using independent sample t-tests. Categorical variables are presented by frequency and percentage, with inter-group comparisons conducted using chi-square tests.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: OVCFs: Osteoporotic Vertebral Compression Fractures; TV: Target Vertebral; HU: Hounsfield Units.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTransforming numerical into categorical variables\u003c/h3\u003e\n\u003cp\u003eThe original data included four numerical variables: degree of vertebral compression, CT value of the TV, difference in average CT value between the target and adjacent vertebrae, and the ratio of average CT values of the target to adjacent vertebrae. To improve the usability of the final nomogram model, numerical variables were transformed into categorical variables (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Logistic regression and model development were subsequently performed using the transformed data.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumerical variable in the training set.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNumerical variable in the training set.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25% Quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50% (Median) Quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75% Quartile\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV CT Value (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Value Difference from Adjacent Vertebra Average (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-128.0\u0026ndash;273.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.375\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Value Ratio TV to Adjacent Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u0026ndash;24.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Compression Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNumerical variables are categorized based on quartiles. 'TV CT Value' is divided into four groups: Group 1 (\u0026lt;\u0026thinsp;55), Group 2 (55\u0026ndash;90), Group 3 (90\u0026ndash;135), and Group 4 (\u0026gt;\u0026thinsp;135). 'CT Value Difference from Adjacent Vertebra Average' is categorized into four groups: Group 1 (\u0026lt;\u0026thinsp;0), Group 2 (0\u0026ndash;25), Group 3 (25\u0026ndash;65), and Group 4 (\u0026gt;\u0026thinsp;65). 'CT Value Ratio TV to Adjacent Average' is classified into four groups: Group 1 (\u0026lt;\u0026thinsp;1), Group 2 (1-1.5), Group 3 (1.5-2), and Group 4 (\u0026gt;\u0026thinsp;2). 'TV Compression Degree' is classified into four groups: Group 1 (\u0026lt;\u0026thinsp;0.3), Group 2 (0.3\u0026ndash;0.4), Group 3 (0.4\u0026ndash;0.5), and Group 4 (\u0026gt;\u0026thinsp;0.5).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: TV: Target Vertebral; HU: Hounsfield Units\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eLogistic regression and nomogram on training data\u003c/h3\u003e\n\u003cp\u003eUnivariate logistic regression was applied to all variables in the training set, targeting the identification of either fresh or old OVCFs as the outcome variable. Risk factors with P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.06 in univariate logistic regression were selected for inclusion in multivariate logistic regression. Multivariate logistic regression was performed using a stepwise approach to identify the variable combination that most accurately distinguished between fresh and old OVCFs. The final model comprised 7 variables selected for their lowest AIC of 108.493. These variables included the CT value of the target vertebra, the ratio of average CT values between the target and adjacent vertebrae, the degree of target vertebral compression, the presence of high-density shadows in the cancellous bone, the continuity of the TV endplate or cortical bone, sparse cancellous texture of the TV, and TV with Schmorl's Nodes. The nomogram for the model was created using R Studio software. Results of the logistic regression are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e, with the nomogram 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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of the training set data.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Compression Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.76(0.57-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.42(1.24\u0026ndash;4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Endplate or Cortex Discontinuity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.52(4.07\u0026ndash;17.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.43(1.74\u0026ndash;23.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Sparse Trabecular Texture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14(0.06\u0026ndash;0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22(0.03\u0026ndash;1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFracture Line in TV Trabeculae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.77(4-19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \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\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Cancellous Bone\u003c/p\u003e \u003cp\u003eHigh Density Shadow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.71(4.99\u0026ndash;23.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.56(0.88\u0026ndash;2.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV Cavity Over 10mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29(0.14\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \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\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV CT Value (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.91(3.05\u0026ndash;7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.02(1.64\u0026ndash;15.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Value Difference from Adjacent \u003c/p\u003e \u003cp\u003eVertebra Average (HU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.06(2.64\u0026ndash;6.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \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\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT Value Ratio TV to Adjacent Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.25(2.23\u0026ndash;4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37(0.22\u0026ndash;0.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTV with Schmorl's Nodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14(0.04\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34(0.08\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSclerosis Vacuum Sign TV Disc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57(0.25\u0026ndash;1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \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\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: OR: Odds Ratio; CI: Confidence Interval; TV: Target Vertebral; HU: Hounsfield Units.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eModel Validation\u003c/h2\u003e \u003cp\u003eROC analysis revealed that the nomogram model had an AUC of 0.941 (95% CI: 0.904\u0026ndash;0.978) in the training set and 0.974 (95% CI: 0.938-1.000) in the validation set (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, respectively), demonstrating the model's strong discriminatory capacity. Moreover, the calibration plots for both training and validation sets showed slopes close to the ideal diagonal line (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed). Hosmer-Lemeshow statistical tests in these sets resulted in (χ2\u0026thinsp;=\u0026thinsp;7.09, p\u0026thinsp;=\u0026thinsp;0.63) and (χ2\u0026thinsp;=\u0026thinsp;3.30, p\u0026thinsp;=\u0026thinsp;0.95) respectively; The results suggest excellent calibration of the model. Finally, DCA curves were plotted for both the training and validation sets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The model's curves consistently remained above the curves for the \"treat all\" and \"treat none\" strategies, indicating its superiority across the entire threshold probability range, thus ensuring valuable clinical decision support in a wide array of clinical scenarios.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUpon validation, our model demonstrated remarkable discriminatory ability and calibration performance. It comprises seven factors; three continuous variable factors were transformed into four categorical variables using the quartile method, while the remaining four are binary variables. Variables positively correlated with the diagnosis of fresh OVCFs include the CT value of the TV, CT value ratio of TV to adjacent average, TV cancellous bone high-density shadow, and TV endplate or cortex discontinuity. Conversely, variables negatively correlated include TV compression degree, TV sparse cancellous texture, and TV with Schmorl's nodes. Our detailed analysis of the factors influencing these variables reveals the following: The CT values of the vertebrae with fresh fractures increase due to the heightened bone density resulting from bleeding and edema in the vertebral bone marrow cavity during the acute fracture phase[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; this is also evident as a prominent high-density shadow in the cancellous bone of the fractured vertebrae. In old fractures, the vertebral body undergoes further compression due to the patient not strictly adhering to bed rest post-injury, making the degree of compression more severe than in fresh fractures. On a foundation of osteoporosis, as the fracture heals and with prolonged reconstruction of trabecular bone structure, the old fracture vertebrae, despite being more compressed, tend to have sparser trabecular architecture[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. After prolonged compression of the fractured endplates, the intervertebral discs may protrude into the vertebral body, forming Schmorl's nodes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConventional CT bone windows, which optimize the brightness and contrast of bone images, can clearly display skeletal details. Typically, the window width for these settings ranges from 800 to 2000 Hounsfield Units (Hu), and the window level is approximately between 250 and 500 Hu. These standard settings, however, may exhibit minor variations across different hospitals and scanning apparatus. In the context of patients with OVCFs, characterized by reduced bone density, an adjustment and refinement of the conventional CT window settings might be requisite. Kyung Joon Kim and colleagues[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] elucidated that setting a lumbar spine CT value threshold at 146 Hu enhances the diagnostic sensitivity and specificity for osteoporosis. In this vein, we modified the WL to 200 Hu and constricted the WW to 400 Hu. Such a modification decreases the CT value per grayscale from the standard window's 125 Hu (2000/16) to the modified window's 25 Hu (400/16), yielding a display range from 0 to 400 Hu. This enhancement in contrast facilitates a more distinct demarcation between bone and soft tissue, while the reduction in grayscale range aids in accentuating image details. Within our study, all vertebral images were exclusively examined and measured utilizing the modified CT window setting (WW: 400, WL: 200). Observations under these modified settings revealed that the contrast between cortical and cancellous bones was markedly amplified, and the visualization of soft tissues and intervertebral discs adjacent to the vertebrae was significantly enhanced. Notably, in osteoporotic vertebrae, the rarity of trabeculae was distinctly observable, and in instances of fresh compressive fractures, high-density shadows within the cancellous bone were more readily discernible (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen utilizing CT imaging for diagnosis, physicians can scroll through the images on both sagittal and axial planes to clearly and comprehensively observe the TV. Additionally, CT three-dimensional reconstruction technology enables an accurate assessment of the patient's spinal morphology and the specific condition of the TV. This information aids in determining the insertion angle of the puncture needle in subsequent vertebral augmentation surgery and in predicting the direction of potential bone cement leakage. Considering that OVCFs often recur, with the coexistence of both fresh and old fractures, our data collection excluded only those vertebrae with embedded materials in adjacent vertebrae. Cases with either fresh or old vertebral fractures in adjacent vertebrae were not excluded, thereby enhancing the model's extensive applicability. Elderly patients frequently present with multiple comorbidities. This model is versatile, suitable not only for thoracic and lumbar spine CT images but also for chest and abdominal CT scans[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In cases of elderly patients who are hospitalized in other departments, or have undergone recent CT examinations and seek orthopedic consultation for lower back pain, the model can utilize existing CT images for preliminary screening of fresh OVCFs. This approach potentially reduces the financial burden on these patients.\u003c/p\u003e \u003cp\u003eIn recent years, the advancement of Artificial Intelligence (AI) and Deep Learning (DL) has led to the development of numerous radiomics models for diagnosing fresh OVCFs. Weijuan Chen[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] created a DL model using X-rays for identifying fresh VCFs. Burns[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] designed an DL model utilizing CT scans to automate the detection and classification of OVCFs, as well as assessing bone density through CT value measurements. Xiao Hu[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]developed an DL model to predict OVCFs likelihood using CT. Yuan Li[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] created an DL model relying on CT images for distinguishing OVCFs from vertebral tumors. Jun Zhang[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] improved accuracy by employing an DL model in conjunction with clinical baseline data to differentiate between fresh and old OVCFs. However, in clinical practice, the use of radiomics DL models involves complex processes such as prior installation, computer environment setup, and downloading and processing patient images. Moreover, some models demand specific computer hardware, and hospitals' internal networks, used for data security, add to the inconvenience of deploying deep learning models in clinical settings. In contrast, clinical prediction models are established based on clinical data using logistic regression, a basic form of DL algorithm. Nomograms, being easy to use and without hardware prerequisites, are more readily adoptable compared to radiomics.\u003c/p\u003e \u003cp\u003eThe primary limitations of this model are as follows: ① It is derived from a single-center study with a limited sample size and lacks an external validation set. Future research could involve multi-center studies with larger sample sizes, incorporating images from diverse CT equipment to refine and enhance the model's robustness. ② The study's experimental design encompassed a limited number of independent variables. After a thorough literature review and team discussions, 11 CT image features were selected as independent variables, which might have led to the exclusion of other critical variables, such as CT observations of soft tissue injuries. ③ The model is not equipped to differentiate pathological fractures, including vertebral tumors and infections. Therefore, in clinical practice, it should be employed alongside patient symptoms and laboratory test results to avoid misdiagnosis or overlooked diagnoses. ④ The model is unsuitable for patients with implants in adjacent vertebrae, such as bone cement and pedicle screws, due to the challenges in accurately measuring CT values. This limitation restricts its use in patients who have undergone surgery and experience postoperative adjacent vertebral re-fractures.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur modified CT window setting significantly enhances the visualization of bone marrow bleeding and edema associated with fresh OVCFs. The nomogram diagnostic model we developed, which combines this CT window setting with vertebral CT values, enables accurate differentiation between fresh and old OVCFs based solely on the patient's CT images. This model presents a valuable diagnostic tool, particularly beneficial for emergency physicians, patients with contraindications to MRI, and hospitals without MRI facilities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to all the colleagues who helped in the preparation of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003eEthical approval from the institutional review board of the First Affiliated Hospital of Chongqing Medical University was obtained (K2023-131) and all patients signed informed consent forms. This study was conducted in accordance with the World Medical Association Declaration of Helsinki.Our study has passed the review of the Chinese Clinical Trial Registry, with the registration number ChiCTR2400083010.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e none.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003eData extraction, Shichu Wang, Zhenghan Han; quality assessments, Yiting Lei; data analysis, Zhenghan Han, Tianji Huang; writing-origin draft, Zhenghan Han; writing-review and editing, Bo Liu and Yiting Lei. All authors read and approved the final manuscript. All the authors have agreed on the manuscript that will be submitted, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSon HJ, Park SJ, Kim JK, Park JS. Mortality risk after the first occurrence of osteoporotic vertebral compression fractures in the general population: A nationwide cohort study. \u003cem\u003ePLoS One\u003c/em\u003e. 2023;18(9):e0291561. doi:10.1371/journal.pone.0291561\u003c/li\u003e\n\u003cli\u003eAlsoof D, Anderson G, McDonald CL, Basques B, Kuris E, Daniels AH. Diagnosis and Management of Vertebral Compression Fracture. \u003cem\u003eAm J Med\u003c/em\u003e. 2022;135(7):815-821. doi:10.1016/j.amjmed.2022.02.035\u003c/li\u003e\n\u003cli\u003eGe C, Chen Z, Cao P. Efficacy of percutaneous kyphoplasty on vertebral compression fractures with different bone mineral densities: a retrospective study. \u003cem\u003eBmc Musculoskelet Di\u003c/em\u003e. 2023;24(1):276. doi:10.1186/s12891-023-06341-w\u003c/li\u003e\n\u003cli\u003eZhang Y, Qi H, Zhang Y, Wang J, Xue J. Vertebral bone marrow edema in magnetic resonance imaging correlates with bone healing histomorphometry in (sub)acute osteoporotic vertebral compression fracture. \u003cem\u003eEur Spine J\u003c/em\u003e. 2021;30(9):2708-2717. doi:10.1007/s00586-021-06814-3\u003c/li\u003e\n\u003cli\u003eLiu B, Tan C, Yang H, Meng B. The application of SPECT in the diagnosis of osteoporotic vertebral compression fractures. \u003cem\u003eJournal of Intensive and Critical Care\u003c/em\u003e. 2016;2(3). doi:10.21767/2471-8505.100051\u003c/li\u003e\n\u003cli\u003eLi X, Su F, Yuan Q, Chen Y, Liu CY, Fan Y. Advances in differential diagnosis of cerebrovascular diseases in magnetic resonance imaging: A narrative review. \u003cem\u003eQuant Imaging Med Surg\u003c/em\u003e. 2023;13(4):2712-2734. doi:10.21037/qims-22-750\u003c/li\u003e\n\u003cli\u003eJin CB, Kim H, Liu M, et al. Deep CT to MR synthesis using paired and unpaired data. \u003cem\u003eSens (Basel Switz)\u003c/em\u003e. 2019;19(10):2361. doi:10.3390/s19102361\u003c/li\u003e\n\u003cli\u003eKim J, Bar-Ness D, Si-Mohamed S, et al. Assessment of candidate elements for development of spectral photon-counting CT specific contrast agents. \u003cem\u003eSci Rep\u003c/em\u003e. 2018;8(1):12119. doi:10.1038/s41598-018-30570-y\u003c/li\u003e\n\u003cli\u003eGraffy PM, Lee SJ, Ziemlewicz TJ, Pickhardt PJ. 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Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning. \u003cem\u003eEur Radiol\u003c/em\u003e. 2021;31(12):9612-9619. doi:10.1007/s00330-021-08014-5\u003c/li\u003e\n\u003cli\u003eZhang J, Liu F, Xu J, et al. Automated detection and classification of acute vertebral body fractures using a convolutional neural network on computed tomography. \u003cem\u003eFrontiers in Endocrinology\u003c/em\u003e. 2023;14:1132725. doi:10.3389/fendo.2023.1132725\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Osteoporotic Vertebral Compression Fractures, Diagnostic Model, CT Value, Modified CT Window Technique, Acute Lower Back Pain","lastPublishedDoi":"10.21203/rs.3.rs-6297013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6297013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo develop a nomogram-based diagnostic model using CT imaging for rapid differentiation of fresh versus old osteoporotic vertebral compression fractures (OVCFs), particularly for patients contraindicated for MRI (e.g., those with metallic implants), emergency settings requiring immediate diagnosis, and resource-limited hospitals.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on OVCF patients from The First Affiliated Hospital of Chongqing Medical University (August 2022\u0026ndash;December 2023). Modified CT window parameters (width: 400; level: 200) were applied to quantify vertebral features, including CT values, height reduction, endplate integrity, trabecular sparsity, Schmorl's nodes, and high-density shadows. Predictive variables were identified through univariate and multivariate logistic regression, followed by nomogram construction. Model performance was evaluated via ROC curves, calibration plots, Hosmer-Lemeshow test, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe nomogram integrated seven key imaging biomarkers, demonstrating robust discrimination with AUCs of 0.941 (training cohort) and 0.974 (validation cohort). Calibration was excellent (Hosmer-Lemeshow χ\u0026sup2;=3.30, P\u0026thinsp;=\u0026thinsp;0.95), and DCA confirmed substantial clinical net benefit across threshold probabilities.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis CT-based nomogram achieves high diagnostic accuracy for fresh OVCFs without MRI dependency, offering a practical tool for clinical decision-making in time-sensitive or resource-constrained scenarios.\u003c/p\u003e","manuscriptTitle":"Diagnostic Model for Distinguishing Fresh or Old Osteoporotic Vertebral Compression Fractures Based on Modified CT Window: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 01:24:05","doi":"10.21203/rs.3.rs-6297013/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-19T08:29:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-19T07:59:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11122563055596227341388286252188649460","date":"2025-04-07T05:58:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T11:55:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41914497147351131500937571641130953991","date":"2025-04-04T02:52:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168795787860703776392506813438507905308","date":"2025-04-02T12:46:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T02:33:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T10:27:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T10:25:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Spine Journal","date":"2025-03-24T15:40:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-spine-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esjo","sideBox":"Learn more about [European Spine Journal](http://link.springer.com/journal/586)","snPcode":"586","submissionUrl":"https://submission.springernature.com/new-submission/586/3","title":"European Spine Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3a932da9-dd2f-435c-be78-a7b83ae99712","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:07:30+00:00","versionOfRecord":{"articleIdentity":"rs-6297013","link":"https://doi.org/10.1007/s00586-025-08923-9","journal":{"identity":"european-spine-journal","isVorOnly":false,"title":"European Spine Journal"},"publishedOn":"2025-05-21 15:58:18","publishedOnDateReadable":"May 21st, 2025"},"versionCreatedAt":"2025-05-06 01:24:05","video":"","vorDoi":"10.1007/s00586-025-08923-9","vorDoiUrl":"https://doi.org/10.1007/s00586-025-08923-9","workflowStages":[]},"version":"v1","identity":"rs-6297013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6297013","identity":"rs-6297013","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}