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Background The standard care in patients with a painful osteoporotic vertebral compression fracture (VCF) is conservative therapy. Percutaneous vertebroplasty (PV), a minimally invasive technique, is a new treatment option. Recent randomized controlled trials (RCT) provide conflicting results: two sham-controlled studies showed no benefit of PV while an unmasked but controlled RCT (VERTOS II) found effective pain relief at acceptable costs. The objective of this study is to compare pain relief after PV with a sham intervention in selected patients with an acute osteoporotic VCF using the same strict inclusion criteria as in VERTOS II. Secondary outcome measures are back pain related disability and quality of life. Methods The VERTOS IV study is a prospective, multicenter RCT with pain relief as primary endpoint. Patients with a painful osteoporotic VCF with bone edema on MR imaging, local back pain for 6 weeks or less, osteopenia and aged 50 years or older, after obtaining informed consent, are included and randomized for PV or a sham intervention. In total 180 patients will be enrolled. Follow-up is at regular intervals during a 1-year period with a standard Visual Analogue Scale (VAS) score for pain and pain medication. Necessary additional therapies and complications are recorded. Discussion The VERTOS IV study is a methodologically sound RCT designed to assess pain relief after PV compared to a sham intervention in patients with an acute osteoporotic VCF selected on strict inclusion criteria. Trial registration This study is registered at ClinicalTrials.gov., NCT01200277 .

Introduction The aim of this study is to evaluate the long-term stabilizing–healing effectiveness and influence on adjacent intact vertebral bodies of a new injectable partly resorbable calcium sulfate (60 wt.%)/hydroxyapatite (40 wt.%) bone substitute employed in vertebral augmentation of osteoporotic collapses. Methods From April 2009 to April 2011, 80 patients underwent vertebral augmentation. Patients enrolling criteria were age >20 years and symptomatic osteoporotic vertebral collapse from low-energy trauma encompassed between levels T5 to L5. Preoperative and postoperative imaging studies consisted of computed tomography, plain X-ray, dual X-ray absorptiometry scanning, and magnetic resonance. Pain intensity has been evaluated by an 11-point visual analog scale (VAS) and physical and quality of life compromise assessments have been evaluated by Oswestry Disability Questionnaire (ODI). All procedures have been performed fluoroscopically guided by left unilateral approach under local anesthesia and mild sedation. Results VAS-based pain trend over the 12-month follow-up has shown a statistically significant ( p  < 0.001) decrease, starting from 7.68 (SD 1.83) preoperatively with an immediate first day decrease at 3.51 (SD 2.16) and 0.96 (SD 0.93) at 12 months. ODI score dropped significantly from 54.78% to 20.12% at 6 months. No device-related complication has been reported. In no case a new incidental adjacent fracture has been reported. Conclusion Data show how this injectable partly resorbable ceramic cement could be a nontoxic and lower stiffness alternative to polymethylmethacrylate for immediate and long-term stabilization of osteoporotic collapsed vertebral bodies.

Purpose. To compare percutaneous transpedicular vertebroplasty using calcium phosphate cement (CPC) versus conservative treatment for osteoporotic vertebral fractures. Methods. Eight men and 28 women aged 61 to 99 (mean, 80) years with osteoporotic vertebral fractures underwent percutaneous transpedicular vertebroplasty using CPC. During the same period, 6 men and 32 women aged 53 to 93 (mean, 77) years underwent conservative treatment. The indication for vertebroplasty was a painful unstable fracture, with mobility of the vertebral body shown on flexion and extension lateral radiographs. Fractures without mobility despite deformity were treated conservatively. Results. In the vertebroplasty group, all patients benefited from reduced back pain immediately after surgery, and pain relief was maintained at the latest follow-up. However, correction loss continued until one month after the operation. The mean visual analogue score for pain decreased significantly from preoperation to one day after surgery (9.3 vs. 6.2, p=0.02), and further decreased to 2.8 (p=0.04) on day 3 or 4 when ambulation began, and to 1.5 at the one-month follow-up and 1.4 at the final follow-up (mean, 14 months). The mean duration of analgesic treatment was significantly shorter in the vertebroplasty than conservatively treated group (10.2 vs. 63.5 days). All patients in the vertebroplasty group achieved bone union, with no adjacent vertebral fractures. However, in patients having conservative treatment, there were 2 adjacent vertebral fractures and 4 pseudarthroses, and the collapse continued for several months. Conclusion. Percutaneous transpedicular vertebroplasty using CPC achieves immediate pain relief and reduces the risk of vertebral body collapse and pseudarthrosis among elderly patients with osteoporotic vertebral compression fractures.

Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA) 1 , 2 and risk predictors like the Fracture Risk Assessment Tool (FRAX) 3 – 6 , are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50–90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012–2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates 7 , 8 . A retrospective analysis of existing computed tomography scans shows the feasibility of an automated process for evaluating osteoporotic fracture risk that could be used as an initial screening tool when FRAX inputs are unavailable.

Vertebral compression fracture is a common complication of osteoporosis. It is often triggered by ordinary behaviors such as turning in bed, coughing, and sneezing, but traumatic or metastatic etiologies are also possible. Although patients with vertebral compression fractures are often asymptomatic, they can present with back pain that worsens with postural movement and the Valsalva maneuver, potentially impairing function. Long term, these fractures can cause kyphosis, decreased vertebral height, muscle atrophy, and further bone mineral density loss. Anteroposterior and lateral projection radiography of the spine should be the initial imaging modality, and magnetic resonance imaging can be used to confirm suspicious but radiography-negative cases. Conservative measures are the mainstay of treatment, with physical rehabilitation and pharmacotherapy for pain relief. In addition to nonsteroidal anti-inflammatory drugs and acetaminophen, several anti-osteoporotic medications can improve pain after fracture. Bracing and nerve root blocks have very limited evidence of short-term benefit. Surgical intervention with vertebroplasty or kyphoplasty can be considered when pain persists for 6 weeks despite conservative intervention. Prevention of low bone mineral density is critical for avoiding vertebral compression fractures.

With the increasing prevalence of vertebral fractures, accurate diagnosis and prognostication are essential. This study assesses the effectiveness of AI in diagnosing and predicting vertebral fractures through a systematic review and meta-analysis. A comprehensive search across major databases selected studies utilizing AI for vertebral fracture diagnosis or prognosis. Out of 14,161 studies initially identified, 79 were included, with 40 undergoing meta-analysis. Diagnostic models were stratified by pathology: non-pathological vertebral fractures, osteoporotic vertebral fractures, and vertebral compression fractures. The primary outcome measure was AUROC. AI showed high accuracy in diagnosing and predicting vertebral fractures: predictive AUROC = 0.82, osteoporotic vertebral fracture diagnosis AUROC = 0.92, non-pathological vertebral fracture diagnosis AUROC = 0.85, and vertebral compression fracture diagnosis AUROC = 0.87, all significant (p < 0.001). Traditional models had the highest median AUROC (0.90) for fracture prediction, while deep learning models excelled in diagnosing all fracture types. High heterogeneity (I² > 99%, p < 0.001) indicated significant variation in model design and performance. AI technologies show considerable promise in improving the diagnosis and prognostication of vertebral fractures, with high accuracy. However, observed heterogeneity and study biases necessitate further research. Future efforts should focus on standardizing AI models and validating them across diverse datasets to ensure clinical utility.

The objective of this study was to introduce and validate a novel developed scoring system tailored specifically for osteoporotic vertebral compression fractures (OVCFs), aiming to provide guidance for treatment selection. A retrospective analysis spanning from March 2016 to March 2021 was conducted on 208 patients diagnosed with osteoporotic vertebral compression fractures (OVCFs) who received conservative treatment. Patients were categorized into low-score (47 cases), medium-score (98 cases), and high-score (63 cases) groups based on the Novel Assessment System for OVCFs (NASOVCF) scores. Comparative analyses of radiographic and clinical data were performed, and logistic regression analysis was used to determine the risk factors for bone non-union and progressive kyphosis. The high-score group exhibited significantly inferior outcomes, characterized by higher Visual Analog Scale (VAS) and Oswestry Disability Index (ODI) scores ( P  < 0.05), increased vertebral height loss, and kyphosis angle differences compared to the low and medium-score groups ( P  < 0.05). Notably, a bone union rate of 38.1% (24/63) was observed in the high-score group, significantly lower than that of the low-score group (97.9%, 46/47). Furthermore, the progressive kyphosis rate was 47.6% (30/63) in the high-score group, significantly higher than the 17.3% (17/98) observed in the medium-score group and the 2.2% (1/46) observed in the low-score group. In multivariate analysis, higher NASOVCF score emerged as an independent risk factor for bone non-union (OR = 1.713, 95% CI 1.458–2.013, P  < 0.001). Similarly, higher NASOVCF score (OR = 1.373, 95% CI 1.203–1.568, P  < 0.001), along with female gender and higher pre-treatment ODI score, were identified as independent risk factors for progressive kyphosis. The area under the curve (AUC) for bone non-union and progressive kyphosis were 0.895 and 0.835, respectively, indicating robust discriminative performances. Higher NASOVCF score was identified as a significant risk factor for non-union and progressive kyphosis following conservative treatment in OVCFs. NASOVCF score emerged as a crucial predictor for adverse outcomes in patients at high risk who underwent conservative management. Surgical interventions such as vertebral augmentation may represent a potentially superior option for individuals with high NASOVCF scores.

The use of stereotactic body radiotherapy for metastatic spinal tumours is increasing. Serious adverse events for this treatment include vertebral compression fracture (VCF) and radiation myelopathy. Although VCF is a fairly low-risk adverse event (approximately 5% risk) after conventional radiotherapy, crude risk estimates for VCF after spinal SBRT range from 11% to 39%. In this Review, we summarise the evidence and predictive factors for VCF induced by spinal SBRT, review the pathophysiology of VCF in the metastatic spine, and discuss strategies used to prevent and manage this potentially disabling complication.

Objectives To explore an optimal machine learning (ML) model trained on MRI-based radiomic features to differentiate benign from malignant indistinguishable vertebral compression fractures (VCFs). Methods This retrospective study included patients within 6 weeks of back pain (non-traumatic) who underwent MRI and were diagnosed with benign and malignant indistinguishable VCFs. The two cohorts were retrospectively recruited from the Affiliated Hospital of Qingdao University (QUH) and Qinghai Red Cross Hospital (QRCH). Three hundred seventy-six participants from QUH were divided into the training ( n  = 263) and validation ( n  = 113) cohort based on the date of MRI examination. One hundred three participants from QRCH were used to evaluate the external generalizability of our prediction models. A total of 1045 radiomic features were extracted from each region of interest (ROI) and used to establish the models. The prediction models were established based on 7 different classifiers. Results These models showed favorable efficacy in differentiating benign from malignant indistinguishable VCFs. However, our Gaussian naïve Bayes (GNB) model attained higher AUC and accuracy (0.86, 87.61%) than the other classifiers in validation cohort. It also remains the high accuracy and sensitivity for the external test cohort. Conclusions Our GNB model performed better than the other models in the present study, suggesting that it may be more useful for differentiating indistinguishable benign form malignant VCFs. Key Points • The differential diagnosis of benign and malignant indistinguishable VCFs based on MRI is rather difficult for spine surgeons or radiologists . • Our ML models facilitate the differential diagnosis of benign and malignant indistinguishable VCFs with improved diagnostic efficacy . • Our GNB model had the high accuracy and sensitivity for clinical application .

The vertebral compression is a significant factor for determining the prognosis of osteoporotic vertebral compression fractures and is generally measured manually by specialists. The consequent misdiagnosis or delayed diagnosis can be fatal for patients. In this study, we trained and evaluated the performance of a vertebral body segmentation model and a vertebral compression measurement model based on convolutional neural networks. For vertebral body segmentation, we used a recurrent residual U-Net model, with an average sensitivity of 0.934 (± 0.086), an average specificity of 0.997 (± 0.002), an average accuracy of 0.987 (± 0.005), and an average dice similarity coefficient of 0.923 (± 0.073). We then generated 1134 data points on the images of three vertebral bodies by labeling each segment of the segmented vertebral body. These were used in the vertebral compression measurement model based on linear regression and multi-scale residual dilated blocks. The model yielded an average mean absolute error of 2.637 (± 1.872) (%), an average mean square error of 13.985 (± 24.107) (%), and an average root mean square error of 3.739 (± 2.187) (%) in fractured vertebral body data. The proposed algorithm has significant potential for aiding the diagnosis of vertebral compression fractures.