MRI-based vertebral bone quality score as a novel bone status marker of patients with adolescent idiopathic scoliosis

MRI-based vertebral bone quality score as a novel bone status marker of patients with adolescent idiopathic scoliosis | 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 Article MRI-based vertebral bone quality score as a novel bone status marker of patients with adolescent idiopathic scoliosis Dan-dan Yang, Yi Li, Jiang-yu Tian, Ya Li, Jian Liu, Yun-song Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3848226/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Purpose: To evaluate the application of MRI-based L1-L4 vertebral bone quality (VBQ) score in assessing bone mineral density (BMD) for patients with adolescent idiopathic scoliosis (AIS). Methods: We reviewed the data of AIS patients between January 2021 and October 2023 with MRI, whole-spine plain radiographs, quantitative computed tomography (QCT) and general information. VBQ L1-L4 score was calculated using T1-weighted MRI. Univariate analysis was applied to present the differences between variables of patients with normal group (Z-score>-2.0) and low-BMD group (Z-score≤-2.0). The correlation between the VBQ score and QCT Z-score was analyzed with Pearson correlation test. A multivariate logistic regression model was used to determine the independent factors related to low BMD. Receiver operating characteristic curve (ROC) was drawn to analyze the diagnostic performance of VBQ L1-L4 score in distinguishing low BMD. Results: A total of 136 AIS patients (mean age was 14.84±2.10 years) were included, of which 41 had low BMD. The low-BMD group had a significantly higher VBQ L1-L4 score than that in normal group (3.48±0.85 vs 2.62±0.62, P < 0.001). The VBQ L1-L4 score was significantly negative correlated with QCT Z score (r = − 0.454, P < 0.001). On multivariate analysis, VBQ L1-L4 score was independently associated with low BMD (OR: 4.134, 95% CI: 2.136–8.000, P<0.001). The area under the ROC curve indicated that the diagnostic accuracy of the VBQ L1-L4 score for predicting low BMD was 81%. A sensitivity of 65.9% with a specificity of 88.4% could be achieved for distinguishing low BMD by setting the VBQ L1-L4 score cutoff as 3.18. Conclusions: The novel VBQ L1-L4 score was a promising tool in distinguishing low BMD in patients with AIS and could be useful as opportunistic assessment for screening and complementary evaluation to QCT before surgery. Health sciences/Biomarkers/Diagnostic markers Health sciences/Anatomy/Musculoskeletal system Health sciences/Health care/Medical imaging/Magnetic resonance imaging Adolescent Idiopathic Scoliosis Vertebral Bone Quality Bone Mineral Density Quantitative Computed Tomography Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Adolescent idiopathic scoliosis (AIS) is the most common spinal deformity in children and adolescents aged from 10 to 18 years old [ 1 ]. It is a complex three-dimensional spinal deformity with a risk of progression and has unknown etiology [ 2 , 3 ]. Poor bone quality is one of the risk factors for worsening scoliotic curve [ 4 – 6 ]. Surgical operation is the ultimate intervention for patients with AIS, and its outcomes and postoperative complications depend on many important factors, including bone status. For these reasons, accurate bone mineral density (BMD) measurement is integral to effective preoperative optimization of potentially low BMD or osteoporotic patients. Dual-energy X-ray absorptiometry (DXA) has been widely used clinically to evaluate the bone mass due to its low radiation exposure, low cost, and strong applicability. However, DXA may incorrectly estimate the BMD in patients with severe obesity, sclerosing disease or scoliosis [ 7 , 8 ]. Conversely, three-dimensional scanning with quantitative computed tomography (QCT) can overcome the deficiencies of DXA by providing accurate volumetric BMD measurement but requires the higher radiation dose. In 2019, Ehresman et al. [ 9 ] proposed a novel magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score to evaluate bone mineral density. The VBQ score can be used to evaluate detrimental fat infiltration within the vertebral body based on MRI T1-weighted imaging of lumbar spine. Since then, many studies have confirmed that the VBQ score was correlated with DXA T-scores, and has high predictive value for vertebral fragility fractures, making it a novel and reproducible tool for radiation-free bone density screening [ 10 – 12 ]. To our knowledge, to date the VBQ score has not been used to evaluate bone quality in patients with AIS. Therefore, considering MRI is a customary preoperative assessment for AIS patients undergoing surgery, the study aimed to (1) evaluate the correlation between VBQ score and QCT-based BMD in patients with AIS, (2) assess the predictive performance of VBQ for low BMD. Methods Patients Patients diagnosed with AIS between January 2021 and October 2023 were reviewed retrospectively. AIS is diagnosed by a coronal plane angle (measured by the Cobb method) of more than 10 degrees [ 13 ], without neuromuscular, congenital, or other etiology. In the unit, patients with AIS were screened in a one-stop shop, including X-ray film, computer tomography (CT) and MRI. The exclusion criteria were: (1) patients who received brace treatment or surgery, (2) patients who received lumbar surgical intervention due to conditions other than AIS, or those who have other diseases affecting bone metabolism, (3) incomplete medical charts, (4) transfer to other institutions. QCT Scanning and BMD Measurement QCT measurements were obtained based on lumbar CT scan. CT images were analyzed using the QCT Pro v6.1 analysis software (Mindways Software, Inc.). Average BMD (mg/cm 3 ) was reported. And references for vertebral BMD Z scores based on age and sex were provided by the manufacturer of the QCT software. A low BMD was defined as a Z score of ≤ -2.0 according to the current ISCD recommendations for children and adolescent [ 14 ]. VBQ score measurement and calculation The measurement of VBQ was based on the method described by Ehresman et al. [ 9 ] with several changes and additions. On the midsagittal plane of lumbar spinal non-contrast-enhanced T1-weighted MRI, region of interests (ROIs) were placed in the medullary portions of L1-L4 vertebral bodies and in the cerebrospinal fluid space at the level of L3 and then the signal intensity (SI) of the trabecular bone from the vertebra L1 to L4 were automatically calculated. The placement of ROIs should avoid the cortical bone and the posterior vertebral venous plexus and cover as much of the trabecular region as possible while not requiring a consistent ROI size. In case when the midsagittal slice exhibited abnormalities, the ROIs should put in the midsagittal slice for each individual spine level. The measurement schematic is shown in Fig. 1 . The VBQ score was calculated according to the following formula: $$VBQ=\frac{ {SI}_{median(L1-L4)}}{{SI}_{{CSF}_{L3}}}$$ The measurement was accomplished by two independent researchers in order to evaluate the interrater reliability. And to evaluate the intra-rater reliability, the first researcher repeated the measurement for 40 patients randomly picked from the sample a week after the first-round measurement. During the measurement, both researchers were blinded to the data of patients. Ethics approval and consent to participate This study was approved by the institutional review board of Chengdu Third People’s Hospital. The requirement for informed consent from the study subjects was waived by the IRB of (Chengdu Third People’s Hospital of Science and Technology/Research committee) due to the retrospective study design. Only patients’ file number were extracted with the data and no names or identifiable information was included. In addition, the committee ensured that all methods used in this research was performed in accordance with relevant guidelines/regulations. Statistical Analysis Patients were categorized into two subgroups according to the QCT Z-score, that is the normal group (Z-score>-2.0) and the low-BMD group (Z-score≤-2.0). All analyses were performed using SPSS 23.0 (IBM Corp. Armonk, New York, USA) and Graphpad prsim 9 (GRAPHPAD SOFTWARE, LLC, California, USA). Descriptive statistics constituted medians or means ± standard derivations for continuous variables and proportions for categorical variables. For continuous variables, the differences between groups were assessed using Kruskal-Wallis H test or Student's t test for statistical significance. And categorical variables were analyzed by the chi-square test. The correlation between the VBQ score and QCT Z-score was analyzed with Pearson correlation test. A multivariate logistic regression model was used to showed independent prediction performance for variables. The Youden index was used to determine cutoff values with best diagnostic performance via receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) of variables was calculated. A P value <0.05 was considered statistically significant. Results General characteristics of study population A total of 136 patients were included in this study out of which 90 (66.2%) were females, and mean Cobb’s angle for the AIS subjects was 78.89 degree. The overall mean age was 14.84±2.10 years, and the age did not differ significantly between sexes ( P >0.05). There was a total of 41 patients (30.1%) in the low-BMD group. The percentage of Lenke 1 was the highest among the all patients, followed by Lenke 3 or Lenke 5, and the same was true for the low-BMD and normal groups. There is no statistical difference between the VBQ score and Lenke classification ( P =0.058). The mean Risser sign in low-BMD group and normal group was 3.13±2.14 and 3.37±1.26, respectively. The demographic and anthropometric data are shown in Table 1. Clinical variables related to QCT Z-score The intraclass correlation coefficient (ICC) for interrater reliability was 0.84 (95% CI 0.71–0.92) and the ICC for intra-rater reliability was 0.92 (95% CI 0.86–0.94). The results showed statistically significance with regard to the median height, corrected height by Cobb’s angle, Cobb’s angle and VBQ score between the two groups ( P <0.01). The low-BMD group had a lower height, a greater Cobb’s angle and a higher VBQ score than that in normal group. The mean VBQ score in normal and low-BMD groups was 2.62±0.62 and 3.48±0.85, respectively (Figure.2). The remaining variables including age, sex, BMI, Lenke classification and Risser sign showed no significant statistical difference between the two groups (all P >0.05). The QCT Z-score was significantly correlated with the Cobb angle of the major curve (r = −0.43; P < 0.001) and VBQ score (r = − 0.454, P < 0.001). Figure 3 shown the correlation between VBQ score and QCT Z‑score. A multivariable logistic regression analysis was performed to adjusted for corrected height, Cobb’s angle and VBQ score. The model revealed that the Cobb’s angle (OR: 1.014, 95% CI: 1.002–1.026, P =0.027) and VBQ score (OR: 4.134, 95% CI: 2.136–8.000, P<0.001) could independently associated with low BMD in AIS patients. Diagnosis performance of the VBQ score According to the AUC of ROC curve shown in Figure. 4, the diagnosis accuracy of the VBQ score for predicting low BMD was 0.806. Table 2 presents the diagnosis performance of the VBQ score with various proposed thresholds (cutoff value) in distinguishing normal from low BMD. According to the maximum Youden index, a cutoff value at 3.18 had the best diagnosis performance for diagnosing low BMD in AIS, with a sensitivity of 65.9% and a specificity of 88.4%. Discussion Poor bone quality was a generalized phenomenon and a systematic disorder in AIS patients, although the reason is not yet clear. In 1982, Burner et al. [15] first reported low BMD status in patients with AIS, based on plain radiographs. Since then, several studies have corroborated these findings and the prevalence of low BMD in AIS is approximately 20-38% [16-18]. The follow-up studies indicated that low BMD in AIS patients may be a persistent phenomenon, increasing the risk of osteoporosis during adulthood [4,19]. More importantly, persistent low BMD is demonstrated to be an independent and significant prognostic factor for the curve severity and progression [4-6]. In our study, low BMD was found in 30.1% of patients, and the QCT Z-score was significantly negative correlated with the Cobb angle of the major curve, which was consistent with previous studies. Spinal fusion is the ultimate intervention for patients with AIS and its positive outcomes were influenced by the bone quality. Bone density strongly influences the screw purchase, instrumentation failure and the risk of pseudoarthrosis, screw loosening, proximal junctional kyphosis and son on [20-21]. Any of these outcomes can lead to loss of alignment and surgical failure. Therefore, preoperative assessment of bone quality is key to optimal surgical outcomes in patients with spinal pathologies undergoing instrumented fusion. Currently, DXA has been widely used clinically to evaluate BMD; however, it may not be reliable for the evaluation of BMD in patients with spinal deformities, especially scoliosis [22]. In addition, DXA is unable to take into account changes in body and skeletal size during growth, limiting its usefulness in longitudinal studies, whereas QCT can assess both volume and density of bone in the axial and appendicular skeleton, it may be therefore more useful than DXA in adolescents [23]. However, QCT cannot be a reusable tool in BMD monitoring due to its higher radiation dose. Currently, MRI has been widely used as a standard preoperative screening before AIS surgery. The BMD assessment method based on MRI could assess the fatty infiltration extent of trabecular bone, thereby reflecting the pathophysiological change of bone mass abnormalities. Unlike DXA and QCT, MRI-based VBQ score was radiation free and demonstrated generalizability across different MR systems [10]. Since the VBQ score was proposed, many studies have proved it to be a useful tool in osteoporosis research [10-12]. But it should be noted that almost all previous studies about VBQ score were of the adults. To best of our knowledge, this is the first study to investigate the association between VBQ score and BMD based on QCT in patients with AIS. The results of our study showed significantly differences in VBQ score between BMD subgroups. The low-BMD group had a higher VBQ score, which was consistent with previous studies [24,25]. VBQ score had a moderate negative linear correlation with QCT Z score. The multivariable logistic regression analysis revealed independently ability of the VBQ score to predict the presence of low BMD in AIS patients. The results of the ROC analysis showed that the VBQ score exhibited a good discriminability between AIS patients with and without low BMD (AUCs 0.806), and a cutoff point of 3.18 was obtained for identifying low BMD, based on the maximum Youden index. Some studies have pointed that the stability of the VBQ may be compromised when abnormal spinal morphology was present. Salzmann SN et al. [26] observed that the VBQ score had a sensitivity of 74.3%, a specificity of 57.0%, and an AUC of 0.707 to distinguish osteopenia/osteoporosis from normal BMD in patients undergoing lumbar spinal fusion. Roch PJ et al. [27] reported a sensitivity of 74.3% and a specificity of 60.4% with an AUC of 0.713. In contrast, our reported VBQ sensitivity of 65.9% and specificity of 88.4% was no inferior to other studies. These results suggested that the VBQ score still had good diagnostic efficacy in identifying low BMD in patients with scoliosis. Using a cutoff value of 3.42 based on our results, the sensitivity declined, and specificity improved slightly, while the AUC also declined. Based on our results, we suggest that a VBQ score < 3.18 can be used to exclude patients with low BMD. Last, the inter-rater and intra-rater reliability analysis showed a fair repeatability of VBQ score method, demonstrating its potential feasibility in clinical practice. Of note, the current study does not suggest that MRI replace DXA or QCT for the assessment of low BMD or osteoporosis. Instead, we believe that the utilization of threshold value we proposed can allow for more selective utilization of these screening methods and avoid some unnecessary radiation exposure. As a rough screening tool, VBQ score is sufficient to potentially differentiate between healthy and low BMD vertebrae with 90% specificity. This study has a few limitations. Firstly, it is a single-center investigation with a relatively limited sample size. Secondly, the AIS patients from our institution have regional characteristics and is mostly minorities. Thirdly, as the study was based on radiological findings, histopathological analysis is needed for further validation of VBQ in the future. Lastly, the signal intensity ratios of vertebral body vs cerebrospinal fluid may be influenced by the MRI vendor, field strength or other acquisition parameters, which requires further validation studies. Conclusion This is first study to investigate the diagnosis value of VBQ score based on T1-MRI images in assessing BMD for AIS patients and the correlation between VBQ score and QCT Z score. The results of the study indicated the novel VBQ score was a promising tool in distinguishing low BMD in patients with AIS, with a diagnostic precision of 81%. The VBQ score based on MRI would enable surgeons to preliminarily evaluate the bone density while assessing neurological and spinal cord conditions, with a value ≥ 3.18 indicating a high suspicion of low BMD, and further examination (e.g., using QCT) is recommended to clarify the diagnosis. Declarations Author contributions YDD and LC designed the study; YDD, LY,CXW and TJY collected the data; YDD and LC reviewed the data; LY, LJ, LYS and YDD analyzed and interpreted the data; YDD and LC drafted the manuscript; LC, CXW and LJ contributed to critical revision of the manuscript. All authors have read and approved the final manuscript. Funding support No funding was received for this research. Conflict of interest The authors declare that there is no conflict of interest regarding the publication of this paper. Data availability The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. References Lonstein JE (1994) Adolescent idiopathic scoliosis. Lancet 344:1407–1412. http://doi.org/10.1016/s0140-6736(94)90572-x . Burwell RG (2003) Aetiology of idiopathic scoliosis: current concepts. Pediatr Rehabil 6:137–170. http://doi.org/10.1080/13638490310001642757 . Wang WJ, Yeung HY, Chu WC, et al (2011) Top theories for the etiopathogenesis of adolescent. idiopathic scoliosis. J Pediatr Orthop 31:S14–S27. http://doi.org/10.1097/BPO.0b013e3181f73c12 . Nishida M, Yagi M, Suzuki S, et al (2023) Persistent low bone mineral density in adolescent idiopathic scoliosis: A longitudinal study. J Orthop Sci 28:1099–1104. http://doi.org/10.1016/j.jos.2022.07.005 . Li X, Hung VWY, Yu FWP, et al (2020) Persistent low-normal bone mineral density in adolescent idiopathic scoliosis with different curve severity: A longitudinal study from presentation to beyond skeletal maturity and peak bone mass. Bone 133:115217. http://doi.org/10.1016/j.bone.2019.115217 . Yip BHK, Yu FWP, Wang Z, et al (2016) Prognostic Value of Bone Mineral Density on Curve Progression: A Longitudinal Cohort Study of 513 Girls with Adolescent Idiopathic Scoliosis. Sci Rep 6:39220. http://doi.org/10.1038/srep39220 . Izadyar S, Golbarg S, Takavar A, Zakariaee SS (2016) The Effect of the Lumbar Vertebral Malpositioning on Bone Mineral Density Measurements of the Lumbar Spine by Dual-Energy X-Ray Absorptiometry. J Clin Densitom 19:277–281. http://doi.org/10.1016/j.jocd.2015.12.001 . Xu XM, Li N, Li K, et al (2018) Discordance in diagnosis of osteoporosis by quantitative computed tomography and dual-energy X-ray absorptiometry in Chinese elderly men. J Orthop Translat 18:59–64. http://doi.org/10.1016/j.jot.2018.11.003 Ehresman J, Schilling A, Pennington Z, et al (2019) A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis. J Neurosurg Spine 1–8. http://doi.org/10.3171/2019.9.SPINE19954 . Ehresman J, Pennington Z, Schilling A, et al (2020) Novel MRI-based score for assessment of bone density in operative spine patients. Spine J 20:556–562. http://doi.org/10.1016/j.spinee.2019.10.018 . Ehresman J, Schilling A, Yang X, et al (2021) Vertebral bone quality score predicts fragility fractures independently of bone mineral density. Spine J 21:20–27. http://doi.org/10.1016/j.spinee.2020.05.540 . Pennington Z, Ehresman J, Lubelski D, et al (2021) Assessing underlying bone quality in spine surgery patients: a narrative review of dual-energy X-ray absorptiometry (DXA) and alternatives. Spine J 21:321–331. http://doi.org/10.1016/j.spinee.2020.08.020 . Peng Y, Wang SR, Qiu GX, Zhang JG, Zhuang QY (2020) Research progress on the etiology and pathogenesis of adolescent idiopathic scoliosis. Chin Med J (Engl) 133:483–493. http://doi.org/10.1097/CM9.0000000000000652 . Adams JE, Engelke K, Zemel BS, Ward KAInternational SOCD (2014) Quantitative Computer Tomography in Children and Adolescents: The 2013 ISCD Pediatric Official Positions. J Clin Densitom 17:258–274. http://doi.org/10.1016/j.jocd.2014.01.006 . Burner WL 3rd, Badger VM, Sherman FC (1982) Osteoporosis and acquired Back deformities. J Pediatr Orthop 2:383–385. http://doi.org/10.1097/01241398-198210000-00006 . Cheng JC, Qin L, Cheung CS, et al (2000) Generalized low areal and volumetric bone mineral density in adolescent idiopathic scoliosis. J Bone Miner Res 15:1587–1595. http://doi.org/10.1359/jbmr.2000.15.8.1587 . Li XF, Li H, Liu ZD, Dai LY (2008) Low bone mineral status in adolescent idiopathic scoliosis. Eur Spine J 17:1431–1440. http://doi.org/10.1007/s00586-008-0757-z . Zhu F, Qiu Y, Yeung HY, Lee KM, Cheng CY (2009) Trabecular bone micro-architecture and bone mineral density in adolescent idiopathic and congenital scoliosis. Orthop Surg 1:78–83. http://doi.org/10.1111/j.1757-7861.2008.00014.x . Ohashi M, Hirano T, Watanabe K, et al (2018) Bone Mineral Density After Spinal Fusion Surgery for Adolescent Idiopathic Scoliosis at a Minimum 20-Year Follow-up. Spine Deform 6:170–176. http://doi.org/10.1016/j.jspd.2017.09.002 . McCoy S, Tundo F, Chidambaram S, Baaj AA (2019) Clinical considerations for spinal surgery in the osteoporotic patient: a comprehensive review. Clin Neurol Neurosurg 180:40–47. http://doi.org/10.1016/j.clineuro.2019.03.010 . Kasim KA, Ohlin A (2014) Evaluation of implant loosening following segmental pedicle screw fixation in adolescent idiopathic scoliosis: a 2-year follow-up with low-dose CT. Scoliosis 9:13. http://doi.org/10.1186/1748-7161-9-13 . Kirilov N, Kirilova E, Todorov S, Nikolov N (2020) Effect of the lumbar scoliosis on the results of dual-energy x-ray absorptiometry. Orthop Rev (Pavia) 12:8477. http://doi.org/10.4081/or.2020.8477 . Wren TA, Liu X, Pitukcheewanont P, Gilsanz V (2005) Bone densitometry in pediatric populations: discrepancies in the diagnosis of osteoporosis by DXA and CT. J Pediatr 146:776–779. http://doi.org/10.1016/j.jpeds.2005.01.028 . Pu M, Zhong W, Heng H, et al (2023) Vertebral bone quality score provides preoperative bone density assessment for patients undergoing lumbar spine surgery: a retrospective study. J Neurosurg Spine 24:1–10. http://doi.org/10.3171/2023.1.SPINE221187 . Oezel L, Okano I, Jones C, et al (2023) MRI-based vertebral bone quality score compared to quantitative computed tomography bone mineral density in patients undergoing cervical spinal surgery. Eur Spine J 32:1636–1643. http://doi.org/10.1007/s00586-023-07570-2 . Salzmann SN, Okano I, Jones C, et al (2022) Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion. Spine J 22:1301–1308. http://doi.org/10.1016/j.spinee.2022.03.006 . Roch PJ, Çelik B, Jäckle K, et al (2023) Combination of vertebral bone quality scores from different magnetic resonance imaging sequences improves prognostic value for the estimation of osteoporosis. Spine J 23:305–311. http://doi.org/10.1016/j.spinee.2022.10.013 . Tables Table 1 Demographics, MRI- and QCT-derived Measurements for the Study Population in AIS Variable Entire Cohort (n = 136) Z-score >-2 (n = 95,69.9%) Z-score ≤-2 (n = 41,30.1%) p-value Female, n, % 90(66.2) 64(67.4) 26(63.4) 0.655 Age, year 14.84 ± 2.10 14.91 ± 2.15 14.33 ± 1.75 0.778 Weight, kg 45.17 ± 9.31 46.00 ± 9.31 43.25 ± 9.15 0.114 Height, cm 150.82 ± 12.14 153.25 ± 12.59 145.17 ± 8.84 0.000 Corrected Height, cm 155.05 ± 10.86 156.77 ± 11.49 151.05 ± 8.06 0.004 Corrected BMI, kg/m2 18.70 ± 3.1 18.61 ± 2.81 18.90 ± 22.87 0.657 Lenke Classification, n, % 0.122 1 65(47.8) 50(52.6) 15(36.6) 0.086 2 10(7.4) 4(4.2) 6(14.6) 0.075 3 24(17.6) 15(15.8) 9(22.0) 0.387 4 1(0.7) 0(0.0) 1(2.4) / 5 27(19.9) 19(20.0) 8(19.5) 0.948 6 9(6.6) 7(7.4) 2(4.9) 0.873 Cobb angle major curve, degree 78.89 ± 36.81 70.87 ± 34.19 97.50 ± 36.32 0.000 Risser 3.36 ± 1.32 3.37 ± 1.26 3.17 ± 2.14 0.827 VBQ score 2.88 ± 0.80 2.62 ± 0.62 3.48 ± 0.85 0.000 QCT, mg/cm 3 139.83 ± 50.22 164.10 ± 37.74 83.59 ± 37.74 0.000 T-score -1.09 ± 1.68 -0.27 ± 1.24 -2.99 ± 0.81 0.000 Z-score -0.93 ± 1.61 -0.13 ± 1.19 -2.79 ± 0.57 0.000 BMI body mass index, VBQ vertebral bone quality, QCT quantitative computed tomography, AIS adolescent idiopathic scoliosis Table 2 Diagnostic performance of L1-L4 VBQ for distinguishing low bone mineral density in AIS Variable Cutoff value with high sensitivity (about 90%) Cutoff value with high specificity (about 90%) Cutoff value with balanced sensitivity and specificity L1-L4 VBQ 2.49 3.42 3.18 Sensitivity (95%CI), % 90.2(76.87–97.28) 51.2(35.13–67.12) 65.9(49.41–79.92) Specificity (95% CI), % 50.5(42.12–62.97) 91.6(85.41–96.99) 88.4(81.49–94.84) VBQ vertebral bone quality, CI confdence intervals, AIS adolescent idiopathic scoliosis Additional Declarations No competing interests reported. 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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-3848226","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":267616833,"identity":"b76c4955-49aa-42ae-9eeb-5a8ae57260cb","order_by":0,"name":"Dan-dan Yang","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Dan-dan","middleName":"","lastName":"Yang","suffix":""},{"id":267616834,"identity":"908037aa-82f8-45ee-b799-7038afa7fa9b","order_by":1,"name":"Yi Li","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Li","suffix":""},{"id":267616835,"identity":"1823c635-4f6f-4332-b1da-f2f57d957c7a","order_by":2,"name":"Jiang-yu Tian","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Jiang-yu","middleName":"","lastName":"Tian","suffix":""},{"id":267616836,"identity":"57fbf6f7-41cd-48b3-b2be-1d578185e684","order_by":3,"name":"Ya Li","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Ya","middleName":"","lastName":"Li","suffix":""},{"id":267616837,"identity":"3684af3f-2c61-4960-bd60-95be0e5be38a","order_by":4,"name":"Jian Liu","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Liu","suffix":""},{"id":267616838,"identity":"a305c7af-8e23-47ef-a7c6-59b0259e075e","order_by":5,"name":"Yun-song Liu","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Yun-song","middleName":"","lastName":"Liu","suffix":""},{"id":267616839,"identity":"04e37ba1-af0d-4521-a77d-a3c2502795af","order_by":6,"name":"Xin-wen Cao","email":"","orcid":"","institution":"the Third People’s Hospital of Chengdu","correspondingAuthor":false,"prefix":"","firstName":"Xin-wen","middleName":"","lastName":"Cao","suffix":""},{"id":267616840,"identity":"270065cb-e0dc-4947-9fad-5309f71fc69f","order_by":7,"name":"Chuan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACNmb2AwcSDGzq+9mbDxCnhY+9J/HAh4o0xpk9xxKI0yLHc8D44Iwzhxg33PAxINJhEgkJh3nbDjAb3OD5eOMNg52cbgNBLYkHgFrusEne7t1sOYch2djsAHG2POPhu3N2mzQPw4HEbURoMQBqOSzBcCPnGZFaeA4YAL1/2EDgRg4bkVrYexJAgZwg2XPM2HKOARF+kW9mP/wBGJUJ/OzND2+8qbCTI6gFBUjwEBk1yFpI1TEKRsEoGAUjAgAAQxpJ18VGpkwAAAAASUVORK5CYII=","orcid":"","institution":"West China Second University Hospital of Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-01-09 12:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3848226/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3848226/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49825334,"identity":"8d53acf7-42a2-4941-97e1-b511b95c11d6","added_by":"auto","created_at":"2024-01-18 15:43:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1127180,"visible":true,"origin":"","legend":"\u003cp\u003eA: a standing full-length posteroanterior plain radiography of an AIS patient with aged 16 years. B: The signal intensity of regions of interest (circles) utilized for the computation of L1-L4 vertebral bone quality (VBQ) score on sagittal non-contrast-enhanced T1-weighted MRI.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3848226/v1/60f9601dcae7af96f9518532.png"},{"id":49825335,"identity":"7d65530f-f938-4db8-9a72-3d8b1b9ca64a","added_by":"auto","created_at":"2024-01-18 15:43:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31767,"visible":true,"origin":"","legend":"\u003cp\u003eL1-L4 VBQ score differentiates between normal and low BMD in AIS patients.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3848226/v1/7924c289e254a2223942044b.png"},{"id":49825337,"identity":"9010ca9e-8457-4df7-bb1e-cfc980f1f200","added_by":"auto","created_at":"2024-01-18 15:43:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33095,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between QCT Z‑score and L1-L4 VBQ.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3848226/v1/d8948890a2100492a8b0086f.png"},{"id":49826525,"identity":"699d3130-d159-4db6-a0eb-10f8b92b28b0","added_by":"auto","created_at":"2024-01-18 15:51:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38625,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve (ROC) of L1-L4 VBQ showing the sensitivity and specificity.\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3848226/v1/b442152d9514db85f5cf5cff.png"},{"id":49828016,"identity":"f20cf0d8-668d-46ae-9141-0dc97f7cd8f1","added_by":"auto","created_at":"2024-01-18 15:59:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":742786,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3848226/v1/3fc12d56-f2af-4e64-a204-a3a97fc04774.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MRI-based vertebral bone quality score as a novel bone status marker of patients with adolescent idiopathic scoliosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAdolescent idiopathic scoliosis (AIS) is the most common spinal deformity in children and adolescents aged from 10 to 18 years old [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is a complex three-dimensional spinal deformity with a risk of progression and has unknown etiology [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Poor bone quality is one of the risk factors for worsening scoliotic curve [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Surgical operation is the ultimate intervention for patients with AIS, and its outcomes and postoperative complications depend on many important factors, including bone status. For these reasons, accurate bone mineral density (BMD) measurement is integral to effective preoperative optimization of potentially low BMD or osteoporotic patients.\u003c/p\u003e \u003cp\u003eDual-energy X-ray absorptiometry (DXA) has been widely used clinically to evaluate the bone mass due to its low radiation exposure, low cost, and strong applicability. However, DXA may incorrectly estimate the BMD in patients with severe obesity, sclerosing disease or scoliosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Conversely, three-dimensional scanning with quantitative computed tomography (QCT) can overcome the deficiencies of DXA by providing accurate volumetric BMD measurement but requires the higher radiation dose. In 2019, Ehresman et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] proposed a novel magnetic resonance imaging (MRI)-based vertebral bone quality (VBQ) score to evaluate bone mineral density. The VBQ score can be used to evaluate detrimental fat infiltration within the vertebral body based on MRI T1-weighted imaging of lumbar spine. Since then, many studies have confirmed that the VBQ score was correlated with DXA T-scores, and has high predictive value for vertebral fragility fractures, making it a novel and reproducible tool for radiation-free bone density screening [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. To our knowledge, to date the VBQ score has not been used to evaluate bone quality in patients with AIS.\u003c/p\u003e \u003cp\u003eTherefore, considering MRI is a customary preoperative assessment for AIS patients undergoing surgery, the study aimed to (1) evaluate the correlation between VBQ score and QCT-based BMD in patients with AIS, (2) assess the predictive performance of VBQ for low BMD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003ePatients diagnosed with AIS between January 2021 and October 2023 were reviewed retrospectively. AIS is diagnosed by a coronal plane angle (measured by the Cobb method) of more than 10 degrees [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], without neuromuscular, congenital, or other etiology. In the unit, patients with AIS were screened in a one-stop shop, including X-ray film, computer tomography (CT) and MRI. The exclusion criteria were: (1) patients who received brace treatment or surgery, (2) patients who received lumbar surgical intervention due to conditions other than AIS, or those who have other diseases affecting bone metabolism, (3) incomplete medical charts, (4) transfer to other institutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eQCT Scanning and BMD Measurement\u003c/h2\u003e \u003cp\u003eQCT measurements were obtained based on lumbar CT scan. CT images were analyzed using the QCT Pro v6.1 analysis software (Mindways Software, Inc.). Average BMD (mg/cm\u003csup\u003e3\u003c/sup\u003e) was reported. And references for vertebral BMD Z scores based on age and sex were provided by the manufacturer of the QCT software. A low BMD was defined as a Z score of \u0026le; -2.0 according to the current ISCD recommendations for children and adolescent [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVBQ score measurement and calculation\u003c/h2\u003e \u003cp\u003eThe measurement of VBQ was based on the method described by Ehresman et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] with several changes and additions. On the midsagittal plane of lumbar spinal non-contrast-enhanced T1-weighted MRI, region of interests (ROIs) were placed in the medullary portions of L1-L4 vertebral bodies and in the cerebrospinal fluid space at the level of L3 and then the signal intensity (SI) of the trabecular bone from the vertebra L1 to L4 were automatically calculated. The placement of ROIs should avoid the cortical bone and the posterior vertebral venous plexus and cover as much of the trabecular region as possible while not requiring a consistent ROI size. In case when the midsagittal slice exhibited abnormalities, the ROIs should put in the midsagittal slice for each individual spine level. The measurement schematic is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The VBQ score was calculated according to the following formula:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$VBQ=\\frac{ {SI}_{median(L1-L4)}}{{SI}_{{CSF}_{L3}}}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe measurement was accomplished by two independent researchers in order to evaluate the interrater reliability. And to evaluate the intra-rater reliability, the first researcher repeated the measurement for 40 patients randomly picked from the sample a week after the first-round measurement. During the measurement, both researchers were blinded to the data of patients.\u003c/p\u003e \u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional review board of Chengdu\u0026nbsp;Third People\u0026rsquo;s Hospital. The requirement for informed consent from the study subjects was waived by the IRB of (Chengdu\u0026nbsp;Third People\u0026rsquo;s Hospital\u0026nbsp;of Science and Technology/Research committee) due to the retrospective study design. Only patients\u0026rsquo; file number were extracted with the data and no names or identifiable information was included. In addition, the committee ensured that all methods used in this research was performed in accordance with relevant guidelines/regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were categorized into two subgroups according to the QCT Z-score, that is the normal group (Z-score\u0026gt;-2.0) and the low-BMD group (Z-score\u0026le;-2.0). All analyses were performed using SPSS 23.0 (IBM Corp. Armonk, New York, USA) and Graphpad prsim 9 (GRAPHPAD SOFTWARE, LLC, California, USA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDescriptive statistics constituted medians or means \u0026plusmn; standard derivations for continuous variables and proportions for categorical variables. For continuous variables, the differences between groups were assessed using Kruskal-Wallis H test or Student\u0026apos;s t test for statistical significance. And categorical variables were analyzed by the chi-square test. The correlation between the VBQ score and QCT Z-score was analyzed with Pearson correlation test. A multivariate logistic regression model was used to showed independent prediction performance for variables. The Youden index was used to determine cutoff values with best diagnostic performance via receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) of variables was calculated. A \u003cem\u003eP\u003c/em\u003e value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral characteristics of\u0026nbsp;study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 136 patients were included in this study out of which 90 (66.2%) were females, and mean Cobb\u0026rsquo;s angle for the AIS subjects was 78.89 degree. The overall mean age was 14.84\u0026plusmn;2.10 years, and the age did not differ significantly between sexes (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026gt;0.05). There was a total of 41 patients (30.1%) in the low-BMD group. The percentage of Lenke 1 was the highest among the all patients, followed by Lenke 3 or Lenke 5, and the same was true for the low-BMD and normal groups. There is no statistical difference between the VBQ score and Lenke classification (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e=0.058). The mean Risser sign in low-BMD group and normal group was 3.13\u0026plusmn;2.14 and 3.37\u0026plusmn;1.26, respectively. The demographic and anthropometric data are shown in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical variables related to QCT Z-score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe intraclass correlation coefficient (ICC) for interrater reliability was 0.84 (95% CI 0.71\u0026ndash;0.92) and the ICC for intra-rater reliability was 0.92 (95% CI 0.86\u0026ndash;0.94). The results showed statistically significance with regard to the median height, corrected height by Cobb\u0026rsquo;s angle, Cobb\u0026rsquo;s angle and VBQ score between the two groups (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). The low-BMD group had a lower height, a greater Cobb\u0026rsquo;s angle and a higher VBQ score than that in normal group. The mean VBQ score in normal and low-BMD groups was 2.62\u0026plusmn;0.62 and 3.48\u0026plusmn;0.85, respectively (Figure.2). The remaining variables including age, sex, BMI, Lenke classification and Risser sign showed no significant statistical difference between the two groups (all \u003cem\u003eP\u003c/em\u003e\u0026gt;0.05). The QCT Z-score was significantly correlated with the Cobb angle of the major curve (r = \u0026minus;0.43; P \u0026lt; 0.001) and VBQ score (r = \u0026minus; 0.454, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). Figure 3 shown the correlation between VBQ score and QCT Z‑score. A multivariable logistic regression analysis was performed to adjusted for corrected height, Cobb\u0026rsquo;s angle and VBQ score. The model revealed that the Cobb\u0026rsquo;s angle (OR: 1.014, 95% CI: 1.002\u0026ndash;1.026, \u003cem\u003eP\u003c/em\u003e=0.027) and VBQ score (OR: 4.134, 95% CI: 2.136\u0026ndash;8.000, P\u0026lt;0.001) could independently associated with low BMD in AIS patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnosis performance of\u0026nbsp;the VBQ score\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the AUC of ROC curve shown in Figure. 4, the diagnosis accuracy of the VBQ score for predicting low BMD was 0.806. Table 2 presents the diagnosis performance of the VBQ score with various proposed thresholds (cutoff value) in distinguishing normal from low BMD. According to the maximum Youden index, a cutoff value at 3.18 had the best diagnosis performance for diagnosing low BMD in AIS, with a sensitivity of 65.9% and a specificity of 88.4%.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePoor bone quality was a generalized phenomenon and a systematic disorder in AIS patients, although the reason is not yet clear. In 1982, Burner et al. [15] first reported low BMD status in patients with AIS, based on plain radiographs. Since then, several studies have corroborated these findings and the prevalence of low BMD in AIS is approximately 20-38% [16-18]. The follow-up studies indicated that low BMD in AIS patients may be a persistent phenomenon, increasing the risk of osteoporosis during adulthood [4,19]. More importantly, persistent low BMD is demonstrated to be an independent and significant prognostic factor for the curve severity and progression [4-6]. In our study, low BMD was found in 30.1% of patients, and the QCT Z-score was significantly negative correlated with the Cobb angle of the major curve, which was consistent with previous studies. Spinal fusion is the ultimate intervention for patients with AIS and its positive outcomes were influenced by the bone quality. Bone density strongly influences the screw purchase, instrumentation failure and the risk of pseudoarthrosis, screw loosening, proximal junctional kyphosis and son on [20-21]. Any of these outcomes can lead to loss of alignment and surgical failure. Therefore, preoperative assessment of bone quality is key to optimal surgical outcomes in patients with spinal pathologies undergoing instrumented fusion.\u003c/p\u003e\n\u003cp\u003eCurrently, DXA has been widely used clinically to evaluate BMD; however, it may not be reliable for the evaluation of BMD in patients with spinal deformities, especially scoliosis [22]. In addition, DXA is unable to take into account changes in body and skeletal size during growth, limiting its usefulness in longitudinal studies, whereas QCT can assess both volume and density of bone in the axial and appendicular skeleton, it may be therefore more useful than DXA in adolescents [23]. However, QCT cannot be a reusable tool in BMD monitoring due to its higher radiation dose. Currently, MRI has been widely used as a standard preoperative screening before AIS surgery. The BMD assessment method based on MRI could assess the fatty infiltration extent of trabecular bone, thereby reflecting the pathophysiological change of bone mass abnormalities. Unlike DXA and QCT, MRI-based VBQ score was radiation free and demonstrated generalizability across different MR systems [10]. Since the VBQ score was proposed, many studies have proved it to be a useful tool in osteoporosis research [10-12]. But it should be noted that almost all previous studies about VBQ score were of the adults. To best of our knowledge, this is the first study to investigate the association between VBQ score and BMD based on QCT in patients with AIS. The results of our study showed significantly differences in VBQ score between BMD subgroups. The low-BMD group had a higher VBQ score, which was consistent with previous studies\u0026nbsp;[24,25]. VBQ score had a moderate negative linear correlation with QCT Z score. The multivariable logistic regression analysis revealed independently ability of the VBQ score to predict the presence of low BMD in AIS patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the ROC analysis showed that the VBQ score exhibited a good discriminability between AIS patients with and without low BMD (AUCs 0.806), and a cutoff point of 3.18 was obtained for identifying low BMD, based on the maximum Youden index. Some studies have pointed that the stability of the VBQ may be compromised when abnormal spinal morphology was present. Salzmann SN et al. [26] observed that the VBQ score had a sensitivity of 74.3%, a specificity of 57.0%, and an AUC of 0.707 to distinguish osteopenia/osteoporosis from normal BMD in patients undergoing lumbar spinal fusion. Roch PJ et al. [27] reported a sensitivity of 74.3% and a specificity of 60.4% with an AUC of 0.713. In contrast, our reported VBQ sensitivity of 65.9% and specificity of 88.4% was no inferior to other studies. These results suggested that the VBQ score still had good diagnostic efficacy in identifying low BMD in patients with scoliosis. Using a cutoff value of 3.42 based on our results, the sensitivity declined, and specificity improved slightly, while the AUC also declined. Based on our results, we suggest that a VBQ score \u0026lt; 3.18 can be used to exclude patients with low BMD. Last, the inter-rater and intra-rater reliability analysis showed a fair repeatability of VBQ score method, demonstrating its potential feasibility in clinical practice.\u003c/p\u003e\n\u003cp\u003eOf note, the current study does not suggest that MRI replace DXA or QCT for the assessment of low BMD or osteoporosis. Instead, we believe that the utilization of threshold value we proposed can allow for more selective utilization of these screening methods and avoid some unnecessary radiation exposure. As a rough screening tool, VBQ score is sufficient to potentially differentiate between healthy and low BMD vertebrae with 90% specificity. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study has a few limitations. Firstly, it is a single-center investigation with a relatively limited sample size. Secondly, the AIS patients from our institution have regional characteristics and is mostly minorities. Thirdly, as the study was based on radiological findings, histopathological analysis is needed for further validation of VBQ in the future. Lastly, the signal intensity ratios of vertebral body vs cerebrospinal fluid may be influenced by the MRI vendor, field strength or other acquisition parameters, which requires further validation studies.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis is first study to investigate the diagnosis value of VBQ score based on T1-MRI images in assessing BMD for AIS patients and the correlation between VBQ score and QCT Z score. The results of the study indicated the novel VBQ score was a promising tool in distinguishing low BMD in patients with AIS, with a diagnostic precision of 81%. The VBQ score based on MRI would enable surgeons to preliminarily evaluate the bone density while assessing neurological and spinal cord conditions, with a value \u0026ge; 3.18 indicating a high suspicion of low BMD, and further examination (e.g., using QCT) is recommended to clarify the diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e YDD and LC designed the study; YDD, LY,CXW and TJY collected the data; YDD and LC reviewed the data; LY, LJ, LYS and YDD analyzed and interpreted the data; YDD and LC drafted the manuscript; LC, CXW and LJ contributed to critical revision of the manuscript. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest \u003c/strong\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLonstein JE (1994) Adolescent idiopathic scoliosis. Lancet 344:1407\u0026ndash;1412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/s0140-6736(94)90572-x\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(94)90572-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurwell RG (2003) Aetiology of idiopathic scoliosis: current concepts. Pediatr Rehabil 6:137\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1080/13638490310001642757\u003c/span\u003e\u003cspan address=\"10.1080/13638490310001642757\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang WJ, Yeung HY, Chu WC, et al (2011) Top theories for the etiopathogenesis of adolescent. idiopathic scoliosis. J Pediatr Orthop 31:S14\u0026ndash;S27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1097/BPO.0b013e3181f73c12\u003c/span\u003e\u003cspan address=\"10.1097/BPO.0b013e3181f73c12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishida M, Yagi M, Suzuki S, et al (2023) Persistent low bone mineral density in adolescent idiopathic scoliosis: A longitudinal study. J Orthop Sci 28:1099\u0026ndash;1104. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jos.2022.07.005\u003c/span\u003e\u003cspan address=\"10.1016/j.jos.2022.07.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Hung VWY, Yu FWP, et al (2020) Persistent low-normal bone mineral density in adolescent idiopathic scoliosis with different curve severity: A longitudinal study from presentation to beyond skeletal maturity and peak bone mass. Bone 133:115217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.bone.2019.115217\u003c/span\u003e\u003cspan address=\"10.1016/j.bone.2019.115217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYip BHK, Yu FWP, Wang Z, et al (2016) Prognostic Value of Bone Mineral Density on Curve Progression: A Longitudinal Cohort Study of 513 Girls with Adolescent Idiopathic Scoliosis. Sci Rep 6:39220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1038/srep39220\u003c/span\u003e\u003cspan address=\"10.1038/srep39220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIzadyar S, Golbarg S, Takavar A, Zakariaee SS (2016) The Effect of the Lumbar Vertebral Malpositioning on Bone Mineral Density Measurements of the Lumbar Spine by Dual-Energy X-Ray Absorptiometry. J Clin Densitom 19:277\u0026ndash;281. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jocd.2015.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jocd.2015.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu XM, Li N, Li K, et al (2018) Discordance in diagnosis of osteoporosis by quantitative computed tomography and dual-energy X-ray absorptiometry in Chinese elderly men. J Orthop Translat 18:59\u0026ndash;64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jot.2018.11.003\u003c/span\u003e\u003cspan address=\"10.1016/j.jot.2018.11.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhresman J, Schilling A, Pennington Z, et al (2019) A novel MRI-based score assessing trabecular bone quality to predict vertebral compression fractures in patients with spinal metastasis. J Neurosurg Spine 1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3171/2019.9.SPINE19954\u003c/span\u003e\u003cspan address=\"10.3171/2019.9.SPINE19954\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhresman J, Pennington Z, Schilling A, et al (2020) Novel MRI-based score for assessment of bone density in operative spine patients. Spine J 20:556\u0026ndash;562. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.spinee.2019.10.018\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2019.10.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhresman J, Schilling A, Yang X, et al (2021) Vertebral bone quality score predicts fragility fractures independently of bone mineral density. Spine J 21:20\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.spinee.2020.05.540\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2020.05.540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePennington Z, Ehresman J, Lubelski D, et al (2021) Assessing underlying bone quality in spine surgery patients: a narrative review of dual-energy X-ray absorptiometry (DXA) and alternatives. Spine J 21:321\u0026ndash;331. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.spinee.2020.08.020\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2020.08.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng Y, Wang SR, Qiu GX, Zhang JG, Zhuang QY (2020) Research progress on the etiology and pathogenesis of adolescent idiopathic scoliosis. Chin Med J (Engl) 133:483\u0026ndash;493. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1097/CM9.0000000000000652\u003c/span\u003e\u003cspan address=\"10.1097/CM9.0000000000000652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams JE, Engelke K, Zemel BS, Ward KAInternational SOCD (2014) Quantitative Computer Tomography in Children and Adolescents: The 2013 ISCD Pediatric Official Positions. J Clin Densitom 17:258\u0026ndash;274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jocd.2014.01.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jocd.2014.01.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurner WL 3rd, Badger VM, Sherman FC (1982) Osteoporosis and acquired Back deformities. J Pediatr Orthop 2:383\u0026ndash;385. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1097/01241398-198210000-00006\u003c/span\u003e\u003cspan address=\"10.1097/01241398-198210000-00006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng JC, Qin L, Cheung CS, et al (2000) Generalized low areal and volumetric bone mineral density in adolescent idiopathic scoliosis. J Bone Miner Res 15:1587\u0026ndash;1595. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1359/jbmr.2000.15.8.1587\u003c/span\u003e\u003cspan address=\"10.1359/jbmr.2000.15.8.1587\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi XF, Li H, Liu ZD, Dai LY (2008) Low bone mineral status in adolescent idiopathic scoliosis. Eur Spine J 17:1431\u0026ndash;1440. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1007/s00586-008-0757-z\u003c/span\u003e\u003cspan address=\"10.1007/s00586-008-0757-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu F, Qiu Y, Yeung HY, Lee KM, Cheng CY (2009) Trabecular bone micro-architecture and bone mineral density in adolescent idiopathic and congenital scoliosis. Orthop Surg 1:78\u0026ndash;83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1111/j.1757-7861.2008.00014.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1757-7861.2008.00014.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOhashi M, Hirano T, Watanabe K, et al (2018) Bone Mineral Density After Spinal Fusion Surgery for Adolescent Idiopathic Scoliosis at a Minimum 20-Year Follow-up. Spine Deform 6:170\u0026ndash;176. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jspd.2017.09.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jspd.2017.09.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCoy S, Tundo F, Chidambaram S, Baaj AA (2019) Clinical considerations for spinal surgery in the osteoporotic patient: a comprehensive review. Clin Neurol Neurosurg 180:40\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.clineuro.2019.03.010\u003c/span\u003e\u003cspan address=\"10.1016/j.clineuro.2019.03.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKasim KA, Ohlin A (2014) Evaluation of implant loosening following segmental pedicle screw fixation in adolescent idiopathic scoliosis: a 2-year follow-up with low-dose CT. Scoliosis 9:13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1186/1748-7161-9-13\u003c/span\u003e\u003cspan address=\"10.1186/1748-7161-9-13\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKirilov N, Kirilova E, Todorov S, Nikolov N (2020) Effect of the lumbar scoliosis on the results of dual-energy x-ray absorptiometry. Orthop Rev (Pavia) 12:8477. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.4081/or.2020.8477\u003c/span\u003e\u003cspan address=\"10.4081/or.2020.8477\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWren TA, Liu X, Pitukcheewanont P, Gilsanz V (2005) Bone densitometry in pediatric populations: discrepancies in the diagnosis of osteoporosis by DXA and CT. J Pediatr 146:776\u0026ndash;779. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.jpeds.2005.01.028\u003c/span\u003e\u003cspan address=\"10.1016/j.jpeds.2005.01.028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePu M, Zhong W, Heng H, et al (2023) Vertebral bone quality score provides preoperative bone density assessment for patients undergoing lumbar spine surgery: a retrospective study. J Neurosurg Spine 24:1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.3171/2023.1.SPINE221187\u003c/span\u003e\u003cspan address=\"10.3171/2023.1.SPINE221187\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOezel L, Okano I, Jones C, et al (2023) MRI-based vertebral bone quality score compared to quantitative computed tomography bone mineral density in patients undergoing cervical spinal surgery. Eur Spine J 32:1636\u0026ndash;1643. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1007/s00586-023-07570-2\u003c/span\u003e\u003cspan address=\"10.1007/s00586-023-07570-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalzmann SN, Okano I, Jones C, et al (2022) Preoperative MRI-based vertebral bone quality (VBQ) score assessment in patients undergoing lumbar spinal fusion. Spine J 22:1301\u0026ndash;1308. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.spinee.2022.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2022.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoch PJ, \u0026Ccedil;elik B, J\u0026auml;ckle K, et al (2023) Combination of vertebral bone quality scores from different magnetic resonance imaging sequences improves prognostic value for the estimation of osteoporosis. Spine J 23:305\u0026ndash;311. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.1016/j.spinee.2022.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.spinee.2022.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eDemographics, MRI- and QCT-derived Measurements for the Study Population in AIS\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eEntire Cohort\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;136)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eZ-score \u0026gt;-2\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;95,69.9%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eZ-score \u0026le;-2\u003c/div\u003e \u003cdiv class=\"SimplePara\"\u003e(n\u0026thinsp;=\u0026thinsp;41,30.1%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003ep-value\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eFemale, n, %\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e90(66.2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e64(67.4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e26(63.4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.655\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge, year\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e14.84\u0026thinsp;\u0026plusmn;\u0026thinsp;2.10\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e14.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e14.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.75\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.778\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eWeight, kg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e45.17\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e46.00\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e43.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.114\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eHeight, cm\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e150.82\u0026thinsp;\u0026plusmn;\u0026thinsp;12.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e153.25\u0026thinsp;\u0026plusmn;\u0026thinsp;12.59\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e145.17\u0026thinsp;\u0026plusmn;\u0026thinsp;8.84\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCorrected Height, cm\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e155.05\u0026thinsp;\u0026plusmn;\u0026thinsp;10.86\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e156.77\u0026thinsp;\u0026plusmn;\u0026thinsp;11.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e151.05\u0026thinsp;\u0026plusmn;\u0026thinsp;8.06\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.004\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCorrected BMI, kg/m2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.61\u0026thinsp;\u0026plusmn;\u0026thinsp;2.81\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e18.90\u0026thinsp;\u0026plusmn;\u0026thinsp;22.87\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.657\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eLenke Classification, n, %\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.122\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e65(47.8)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e50(52.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e15(36.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.086\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e10(7.4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e4(4.2)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e6(14.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.075\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e24(17.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e15(15.8)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e9(22.0)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.387\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e1(0.7)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e0(0.0)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1(2.4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e/\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e27(19.9)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e19(20.0)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e8(19.5)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.948\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e9(6.6)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e7(7.4)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2(4.9)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.873\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCobb angle major curve, degree\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e78.89\u0026thinsp;\u0026plusmn;\u0026thinsp;36.81\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e70.87\u0026thinsp;\u0026plusmn;\u0026thinsp;34.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e97.50\u0026thinsp;\u0026plusmn;\u0026thinsp;36.32\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eRisser\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.827\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVBQ score\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eQCT, mg/cm\u003csup\u003e3\u003c/sup\u003e\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e139.83\u0026thinsp;\u0026plusmn;\u0026thinsp;50.22\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e164.10\u0026thinsp;\u0026plusmn;\u0026thinsp;37.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e83.59\u0026thinsp;\u0026plusmn;\u0026thinsp;37.74\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eT-score\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-2.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eZ-score\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.61\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e-0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e-2.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003e0.000\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eBMI body mass index, VBQ vertebral bone quality, QCT quantitative computed tomography, AIS adolescent idiopathic scoliosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u0026ensp;Diagnostic performance of L1-L4 VBQ for distinguishing low bone mineral density in AIS\u003c/div\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 \u003cdiv class=\"SimplePara\"\u003eVariable\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eCutoff value with high sensitivity (about 90%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eCutoff value with high specificity (about 90%)\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eCutoff value with balanced sensitivity and specificity\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eL1-L4 VBQ\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.49\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.42\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3.18\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSensitivity (95%CI), %\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e90.2(76.87\u0026ndash;97.28)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e51.2(35.13\u0026ndash;67.12)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e65.9(49.41\u0026ndash;79.92)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eSpecificity (95% CI), %\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e50.5(42.12\u0026ndash;62.97)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003e91.6(85.41\u0026ndash;96.99)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e88.4(81.49\u0026ndash;94.84)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eVBQ vertebral bone quality, CI confdence intervals, AIS adolescent idiopathic scoliosis\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003cbr/\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":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adolescent Idiopathic Scoliosis, Vertebral Bone Quality, Bone Mineral Density, Quantitative Computed Tomography","lastPublishedDoi":"10.21203/rs.3.rs-3848226/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3848226/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e To evaluate the\u0026nbsp;application of MRI-based L1-L4 vertebral bone quality (VBQ) score in assessing bone mineral density (BMD) for patients with adolescent idiopathic scoliosis (AIS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We reviewed the data of AIS patients between January 2021 and October 2023 with MRI, whole-spine plain radiographs, quantitative computed tomography (QCT) and general information. VBQ \u003csub\u003eL1-L4\u003c/sub\u003e score was calculated using T1-weighted MRI. Univariate analysis was applied to present the differences between variables of patients with normal group (Z-score\u0026gt;-2.0) and low-BMD group (Z-score≤-2.0). The correlation between the VBQ score and QCT Z-score was analyzed with Pearson correlation test. A multivariate logistic regression model was used to determine the independent factors related to low BMD. Receiver operating characteristic curve (ROC) was drawn to analyze the diagnostic performance of VBQ \u003csub\u003eL1-L4\u003c/sub\u003e score in distinguishing low BMD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 136 AIS patients (mean age was 14.84±2.10 years) were included, of which 41 had low BMD. The low-BMD group had a significantly higher VBQ\u003csub\u003eL1-L4\u003c/sub\u003e score than that in normal group (3.48±0.85 vs 2.62±0.62, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). The VBQ\u003csub\u003eL1-L4\u003c/sub\u003e score was significantly negative correlated with QCT Z score (r = − 0.454, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001). On multivariate analysis, VBQ\u003csub\u003eL1-L4\u003c/sub\u003e score was independently associated with low BMD (OR: 4.134, 95% CI: 2.136–8.000, P\u0026lt;0.001). The area under the ROC curve indicated that the diagnostic accuracy of the VBQ\u003csub\u003eL1-L4\u003c/sub\u003e score for predicting low BMD was 81%. A sensitivity of 65.9% with a specificity of 88.4% could be achieved for distinguishing low BMD by setting the VBQ\u003csub\u003eL1-L4\u003c/sub\u003e score cutoff as 3.18.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe novel VBQ\u003csub\u003eL1-L4\u003c/sub\u003e score was a promising tool in distinguishing low BMD in patients with AIS and could be useful as opportunistic assessment for screening and complementary evaluation to QCT before surgery.\u003c/p\u003e","manuscriptTitle":"MRI-based vertebral bone quality score as a novel bone status marker of patients with adolescent idiopathic scoliosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-18 15:43:52","doi":"10.21203/rs.3.rs-3848226/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-25T10:30:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-22T17:30:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-16T23:40:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"588515f5-ca01-4c23-9e66-5294f6ac9eeb","date":"2024-03-13T14:02:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"d60e2005-0ca8-4a42-b464-c58b92048f11","date":"2024-02-24T12:29:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-23T04:50:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3b5423ce-c977-4f32-867f-d423ed7a1404_SNPRID","date":"2024-02-22T08:19:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-22T08:16:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-21T10:40:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-17T11:01:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-17T04:32:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-01-09T12:21:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f0d39176-61c4-49be-ab11-a75591f33f6a","owner":[],"postedDate":"January 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":28196680,"name":"Health sciences/Biomarkers/Diagnostic markers"},{"id":28196681,"name":"Health sciences/Anatomy/Musculoskeletal system"},{"id":28196682,"name":"Health sciences/Health care/Medical imaging/Magnetic resonance imaging"}],"tags":[],"updatedAt":"2024-05-29T03:36:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-18 15:43:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3848226","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3848226","identity":"rs-3848226","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}