Spine最新文献

{"title":"Automated Scoliosis Cobb Angle Classification in Biplanar Radiograph Imaging With Explainable Machine Learning Models.","authors":"Jennifer Yu, Yash S Lahoti, Kyle C McCandless, Nikan K Namiri, Matthew S Miyasaka, Hamza Ahmed, Junho Song, John J Corvi, Daniel C Berman, Samuel K Cho, Jun S Kim","doi":"10.1097/BRS.0000000000005312","DOIUrl":"10.1097/BRS.0000000000005312","url":null,"abstract":"<p><strong>Study design: </strong>Retrospective cohort study.</p><p><strong>Objective: </strong>To quantify the pathology of the spine in patients with scoliosis through one-dimensional feature analysis.</p><p><strong>Summary of background data: </strong>Biplanar radiograph (EOS) imaging is a low-dose technology offering high-resolution spinal curvature measurement, crucial for assessing scoliosis severity and guiding treatment decisions. Machine learning (ML) algorithms, utilizing one-dimensional image features, can enable automated Cobb angle classification, improving accuracy and efficiency in scoliosis evaluation while reducing the need for manual measurements, thus supporting clinical decision-making.</p><p><strong>Methods: </strong>This study used 816 annotated AP EOS spinal images with a spine segmentation mask and a 10° polynomial to represent curvature. Engineered features included the first and second derivatives, Fourier transform, and curve energy, normalized for robustness. XGBoost selected the top 32 features. The models classified scoliosis into multiple groups based on curvature degree, measured through Cobb angle. To address the class imbalance, stratified sampling, undersampling, and oversampling techniques were used, with 10-fold stratified K-fold cross-validation for generalization. An automatic grid search was used for hyperparameter optimization, with K-fold cross-validation (K=3).</p><p><strong>Results: </strong>The top-performing model was Random Forest, achieving an ROC AUC of 91.8%. An accuracy of 86.1%, precision of 86.0%, recall of 86.0%, and an F1 score of 85.1% were also achieved. Of the three techniques used to address class imbalance, stratified sampling produced the best out-of-sample results. SHAP values were generated for the top 20 features, including spine curve length and linear regression error, with the most predictive features ranked at the top, enhancing model explainability.</p><p><strong>Conclusions: </strong>Feature engineering with classical ML methods offers an effective approach for classifying scoliosis severity based on Cobb angle ranges. The high interpretability of features in representing spinal pathology, along with the ease of use of classical ML techniques, makes this an attractive solution for developing automated tools to manage complex spinal measurements.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":"E259-E267"},"PeriodicalIF":2.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pelvic Incidence-Dependent Clustering of Sagittal Spinal Alignment in Asymptomatic Middle-Aged and Elderly Adults: A Machine Learning Approach.","authors":"Qijun Wang, Dongfan Wang, Xiangyu Li, Weiguo Zhu, Peng Cui, Zheng Wang, Wei Wang, Jeffrey C Wang, Xiaolong Chen, Shibao Lu","doi":"10.1097/BRS.0000000000005441","DOIUrl":"https://doi.org/10.1097/BRS.0000000000005441","url":null,"abstract":"<p><strong>Study design: </strong>A cross-sectional cohort study.</p><p><strong>Objective: </strong>This study aimed to refine the sagittal morphological classification of the spine in asymptomatic middle-aged and elderly adult populations using the unsupervised machine learning (ML) techniques and, by leveraging these findings, to propose and validate a surgical correction reference for adult spinal deformity (ASD) patients across different morphological subtypes.</p><p><strong>Summary of background data: </strong>Restoration of sagittal alignment is the key to preventing mechanical complications and achieving good clinical outcomes in ASD surgery. However, high variations in the reported incidence of mechanical complications and clinical outcomes under current ASD realignment strategies have severely impeded the decision-making process for the optimal surgical plan.</p><p><strong>Methods: </strong>This study cross-sectionally enrolled asymptomatic middle-aged and elderly Chinese adults. Sagittal spinal morphology clusters and pelvic incidence-based correction criteria for ASD realignment surgery were derived from whole spine radiographs using unsupervised ML algorithms. To externally validate the realignment strategy identified in asymptomatic adults, a consecutive cohort of ASD patients with sagittal deformity who underwent realignment surgery was examined for postoperative mechanical complications, unplanned reoperation, unplanned readmission, and clinical outcomes during follow-up.</p><p><strong>Results: </strong>A total of 635 asymptomatic adults were enrolled for morphological stratification, and 103 ASD patients with sagittal deformity were included for validation. The unsupervised ML algorithm successfully stratified spinal morphology into four clusters. The pelvic incidence-based surgical correction criteria computed by the regression algorithm demonstrated plausible clinical relevance, evidenced by the significantly lower incidence of postoperative mechanical complications, unplanned reoperation, unplanned readmission, and superior patient-reported outcomes in the restored group (conforming to the correction criteria) during follow-up.</p><p><strong>Conclusion: </strong>In this study, unsupervised ML algorithm effectively partitioned asymptomatic sagittal spinal morphology into four distinct clusters. Using the pelvic incidence-based proportional correction criteria, ASD patients can anticipate a reduced incidence of mechanical complications and improved clinical outcomes following spinal realignment surgery.</p><p><strong>Level of evidence: </strong>Ⅲ.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discharge Hemoglobin Impacts Functional Recovery After Lumbar Fusion.","authors":"Mitsuhiro Nishizawa, Steven D Glassman, Mladen Djurasovic, Charles H Crawford, Jeffrey L Gum, John R Dimar, R Kirk Owens, Justin Mathew, Leah Y Carreon","doi":"10.1097/BRS.0000000000005440","DOIUrl":"https://doi.org/10.1097/BRS.0000000000005440","url":null,"abstract":"<p><strong>Study design: </strong>Retrospective longitudinal observational study.</p><p><strong>Objective: </strong>To identify the impact of anemia at discharge on surgical outcomes and functional recovery following lumbar fusion.</p><p><strong>Summary of background data: </strong>Acute blood loss anemia is common following lumbar spine surgery. Current protocols emphasize limiting transfusions resulting in more patients discharged with lower hemoglobin.</p><p><strong>Methods: </strong>Records of a consecutive series of patients who underwent one- or two-level transforaminal lumbar interbody fusion (TLIF) from 2013 to 2022 were reviewed. Standard demographics, surgical parameters, and complications within a year after discharge were recorded. Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS, 0-10) for back and leg pain, were collected pre-op, and at 3-month and 1 year postoperatively. Patients were categorized into three groups based on discharge hemoglobin: Moderate (8-10 g/dL), Mild (10-13 g/dL for males; 10-12 g/dL for females), and No anemia (>13 g/dL for males; >12 g/dL for females). Complications and PROMs were compared.</p><p><strong>Results: </strong>Of 576 patients, 253 were included in the analysis. The average hemoglobin at discharge was 11.1±1.6 mg/dL. Fifty-eight patients (23%) were discharged with moderate, 144 (57%) with mild, and 51 (20%) with no anemia. Complications rates were higher in patients discharged with lower hemoglobin, with significant differences observed in 90-day readmission (14%, 4%, 0%, P=0.002), infections (14%, 4%, 4%, P=0.022), stroke (5%, 0%, 2%, P=0.021), and gastrointestinal complications (13%, 0%, 8%, P<0.001). Patients with moderate anemia at discharge showed the highest ODI and NRS score of leg pain at all timepoints. There were significant differences in the trajectories of ODI (P=0.021) and leg pain (P=0.018) among the three groups. Among 180 patients with no complications, either during hospitalization or after discharge, the significant differences remained in leg pain.</p><p><strong>Conclusion: </strong>Anemia at discharge was significantly associated with complication rate and worse functional recovery following lumbar fusion surgery.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Early Identification of Malignant Vertebral Compression Fractures via 2.5D Convolutional Neural Network Model with CT Image Analysis.","authors":"Chengbin Huang, Enli Li, Jiasen Hu, Yixun Huang, Yuxuan Wu, Bingzhe Wu, Jiahao Tang, Lei Yang","doi":"10.1097/BRS.0000000000005438","DOIUrl":"https://doi.org/10.1097/BRS.0000000000005438","url":null,"abstract":"<p><strong>Study design: </strong>This study employed a retrospective data analysis approach combined with model development and validation.</p><p><strong>Objectives: </strong>The present study introduces a 2.5D convolutional neural network (CNN) model leveraging CT imaging to facilitate the early detection of malignant vertebral compression fractures (MVCFs), potentially reducing reliance on invasive biopsies.</p><p><strong>Summary of background data: </strong>Vertebral histopathological biopsy is recognized as the gold standard for differentiating between osteoporotic and malignant vertebral compression fractures (VCFs). Nevertheless, its application is restricted due to its invasive nature and high cost, highlighting the necessity for alternative methods to identify MVCFs.</p><p><strong>Methods: </strong>The clinical, imaging, and pathological data of patients who underwent vertebral augmentation and biopsy at Institution 1 and Institution 2 were collected and analyzed. Based on the vertebral CT images of these patients, 2D, 2.5D, and 3D CNN models were developed to identify the patients with osteoporotic vertebral compression fractures (OVCF) and MVCF. To verify the clinical application value of the CNN model, two rounds of reader studies were performed.</p><p><strong>Results: </strong>The 2.5D CNN model performed well, and its performance in identifying MVCF patients was significantly superior to that of the 2D and 3D CNN models. In the training dataset, the area under the receiver operating characteristic curve (AUC) of the 2.5D CNN model was 0.996 and an F1 score of 0.915. In the external cohort test, the AUC was 0.815 and an F1 score of 0.714. And clinicians' ability to identify MVCF patients has been enhanced by the 2.5D CNN model. With the assistance of the 2.5D CNN model, the AUC of senior clinicians was 0.882, and the F1 score was 0.774. For junior clinicians, the 2.5D CNN model-assisted AUC was 0.784 and the F1 score was 0.667.</p><p><strong>Conclusions: </strong>The development of our 2.5D CNN model marks a significant step towards non-invasive identification of MVCF patients,. The 2.5D CNN model may be a potential model to assist clinicians in better identifying MVCF patients.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144476788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intraoperative Conversion of Primary Cervical Total Disc Replacement to Fusion: Incidence and Reasons.","authors":"Kalin J Fisher, Scott L Blumenthal, Richard D Guyer, Jack E Zigler, Jessica L Shellock, Donna D Ohnmeiss","doi":"10.1097/BRS.0000000000005437","DOIUrl":"10.1097/BRS.0000000000005437","url":null,"abstract":"<p><strong>Study design: </strong>This was a retrospective study.</p><p><strong>Objective: </strong>The purpose of the study was to determine the incidence and reasons of intraoperative conversion from planned cervical total disc replacement (TDR) to anterior cervical discectomy and fusion (ACDF).</p><p><strong>Summary of background data: </strong>TDR currently offers an alternative treatment with results similar or superior to ACDF. Once individual surgeons become comfortable using a device, they will typically expand indications for its use, however, patient safety must remain at the forefront. In order to reduce reoperations, surgeons must use strict selection criteria to determine which patients are most appropriate to receive TDR. Despite those criteria, situations may arise when surgeons convert from TDR to ACDF intraoperatively.</p><p><strong>Methods: </strong>A comprehensive surgery log was reviewed to identify cases of planned TDR converted intraoperatively to ACDF. All cases were performed at a single institution by 28 attending surgeons beginning with the first case experience in November, 2003 and ending with cases performed in November, 2023. Retrospective chart review was performed to collect demographic data and determine reasons for conversion.</p><p><strong>Results: </strong>During the 20-year period, a total of 2,188 consecutive cases of planned TDR were examined. The total number of planned TDR levels was 2,939. The rate of intraoperative conversion of TDR to ACDF was 0.55% (12/2,188 cases), 95% confidence interval 0.30% - 0.90%. With respect to the number of operated levels, the intraoperative rate of conversion was 0.48% (14/2,939 levels), 95% confidence interval 0.30-0.80%.</p><p><strong>Conclusion: </strong>There was a very low incidence of intra-operative conversion of cervical TDR to ACDF. However, surgeons performing this procedure should be prepared for the possibility of intraoperative conversion from TDR to ACDF and educate patients of this possibility.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor regarding \"Global, Regional, and National Burden of Low Back Pain Findings From the Global Burden of Disease Study 2021 and Projections to 2050\" by Cheng et al.","authors":"Prashant Lakhe","doi":"10.1097/BRS.0000000000005439","DOIUrl":"10.1097/BRS.0000000000005439","url":null,"abstract":"","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Radiomic Signatures of Intervertebral Disc Degeneration from MRI.","authors":"Terence McSweeney, Aleksei Tiulpin, Narasimharao Kowlagi, Juhani Määttä, Jaro Karppinen, Simo Saarakkala","doi":"10.1097/BRS.0000000000005435","DOIUrl":"10.1097/BRS.0000000000005435","url":null,"abstract":"<p><strong>Study design: </strong>A retrospective analysis.</p><p><strong>Objective: </strong>The aim of this study was to identify a robust radiomic signature from deep learning segmentations for intervertebral disc (IVD) degeneration classification.</p><p><strong>Summary of data: </strong>Low back pain (LBP) is the most common musculoskeletal symptom worldwide and IVD degeneration is an important contributing factor. To improve the quantitative phenotyping of IVD degeneration from T2-weighted magnetic resonance imaging (MRI) and better understand its relationship with LBP, multiple shape and intensity features have been investigated. IVD radiomics have been less studied but could reveal sub-visual imaging characteristics of IVD degeneration.</p><p><strong>Methods: </strong>We used data from Northern Finland Birth Cohort 1966 members who underwent lumbar spine T2-weighted MRI scans at age 45-47 (n=1397). We used a deep learning model to segment the lumbar spine IVDs and extracted 737 radiomic features, as well as calculating IVD height index and peak signal intensity difference. Intraclass correlation coefficients across image and mask perturbations were calculated to identify robust features. Sparse partial least squares discriminant analysis was used to train a Pfirrmann grade classification model.</p><p><strong>Results: </strong>The radiomics model had balanced accuracy of 76.7% (73.1-80.3%) and Cohen's Kappa of 0.70 (0.67-0.74), compared to 66.0% (62.0-69.9%) and 0.55 (0.51-0.59) for an IVD height index and peak signal intensity model. 2D sphericity and interquartile range emerged as radiomics-based features that were robust and highly correlated to Pfirrmann grade (Spearman's correlation coefficients of -0.72 and -0.77 respectively).</p><p><strong>Conclusion: </strong>Based on our findings these radiomic signatures could serve as alternatives to the conventional indices, representing a significant advance in the automated quantitative phenotyping of IVD degeneration from standard-of-care MRI.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144333907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"C2 Pelvic Angle as a Predictive Measure for Decompensated Spinopelvic Alignment and Its Impact on Health-Related Quality of Life.","authors":"Jun Ouchida, Hiroaki Nakashima, Sadayuki Ito, Naoki Segi, Ippei Yamauchi, Yukihito Ode, Shiro Imagama","doi":"10.1097/BRS.0000000000005436","DOIUrl":"https://doi.org/10.1097/BRS.0000000000005436","url":null,"abstract":"<p><strong>Study design: </strong>Retrospective cohort.</p><p><strong>Objective: </strong>To evaluate the C2 pelvic angle (C2PA) as a novel compensatory parameter integrating C2 (center of gravity) and pelvic morphology, and its association with physical function and health-related quality of life (HRQOL). The T4-L1-Hip axis concept was applied to assess individualized spinal alignment.</p><p><strong>Background: </strong>Adult spinal deformity (ASD) impairs physical function and HRQOL in older adults. Pelvic retroversion compensates for spinal degeneration to maintain posture, but standardized evaluative indices linking pelvic parameters to outcomes remain limited.</p><p><strong>Methods: </strong>Cross-sectional analysis of 383 community-dwelling adults (mean age 64.1 ± 10.3 y). Whole-spine radiographs, physical function tests (Timed Up and Go, 10-meter walk), and SF-36 HRQOL scores were analyzed. C2PA mismatch (measured C2PA minus normative value [0.4 × pelvic incidence - 13]) and thoracolumbar decompensation (T4PA-L1PA ≥4°) were assessed. ROC analysis identified optimal C2PA mismatch thresholds; propensity score matching (age/sex/BMI) compared groups.</p><p><strong>Results: </strong>C2PA mismatch correlated strongly with lumbar (r=0.668) and thoracolumbar (r=0.707) mismatches. A 13° C2PA mismatch threshold optimally identified decompensated thoracolumbar alignment (AUC=0.819). Participants with>13° C2PA mismatch demonstrated decreased physical function (Timed Up and Go: 6.4 vs. 5.8 seconds; 10-meter walk: 1.4 vs. 1.5 m/s) and lower SF-36 scores (physical functioning: 65.9 vs. 72.8; bodily pain: 63.1 vs. 70.8; physical component summary: 41.8 vs. 45.7).</p><p><strong>Conclusions: </strong>C2PA is a reliable indicator of decompensated spinopelvic alignment, with>13° mismatch (measured C2PA>0.4 × pelvic incidence) The C2PA threshold correlates with decreased physical function and lower HRQOL, providing a clinical benchmark for evaluating and managing patients with spinal alignment degeneration.</p><p><strong>Level of evidence: </strong>3.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global Inequalities in the Burden of Low Back Pain from 1990 to 2021: Findings from the Global Burden of Disease Study 2021.","authors":"Liang Chen, Jian Chen","doi":"10.1097/BRS.0000000000005433","DOIUrl":"https://doi.org/10.1097/BRS.0000000000005433","url":null,"abstract":"","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patterns of Bone Density Change in L4-L5 Spondylolisthesis.","authors":"Brandon Khoo, Nathan M Cross, Scott Telfer","doi":"10.1097/BRS.0000000000005434","DOIUrl":"https://doi.org/10.1097/BRS.0000000000005434","url":null,"abstract":"<p><strong>Study design: </strong>Retrospective analysis of medical imaging data.</p><p><strong>Objective: </strong>This study aimed to determine if there are changes in localized bone density of the L4-L5 vertebrae in patients with spondylolisthesis, with these changes hypothesized to be associated with increasing severity of spinal deformities.</p><p><strong>Summary of background data: </strong>Degenerative spondylolisthesis is a common spinal pathology resulting in pain and functional limitations. The condition may lead to changes in the bone density of the affected vertebrae and subsequent fracture risk and challenges for surgical interventions. However, the specific regions where these changes may occur have not been fully investigated.</p><p><strong>Methods: </strong>Sixty-eight sets of three-dimensional L4-L5 vertebrae models were created from clinical computed tomography (CT) scans, 38 from patients with spondylolisthesis and the remainder matched controls. A calibrated opportunistic quantitative CT approach was used to produce detailed bone density estimates across each vertebrae. The effects of age, sex, and associations with radiographic measures of spinal deformity on bone density patterns were assessed.</p><p><strong>Results: </strong>There was a significant association between diagnosis of spondylolisthesis and full bone density as well as age-related changes, however these overall changes were not found to be associated with any specific measure of deformity. The localized analysis showed that with increasing anterolisthesis, bone density was affected in the anterior, medial, and lateral regions of the L5 body, and the L4-L5 facet joints.</p><p><strong>Conclusions: </strong>These findings demonstrate that there are patterns of region change to L4-L5 bone density associated with spondylolisthesis, however there remains significant variability between patients. Future work will explore if surgical planning of spinal vertebrae correction and assessment of fracture risk may be assisted through detailed bone density mapping for L4-L5 and other vertebrae.</p><p><strong>Level of evidence: </strong>Level 4.</p>","PeriodicalId":22193,"journal":{"name":"Spine","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144317894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}