Malignant Fractures Research Articles

Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis

PurposeTo evaluate the diagnostic accuracy of machine learning (ML) in detecting vertebral fractures, considering varying fracture classifications, patient populations, and imaging approaches. MethodA systematic review and meta-analysis were conducted by searching PubMed, Embase, Cochrane Library, and Web of Science up to December 31, 2023, for studies using ML for vertebral fracture diagnosis. Bias risk was assessed using QUADAS-2. A bivariate mixed-effects model was used for the meta-analysis. Meta-analyses were performed according to five task types (vertebral fractures, osteoporotic vertebral fractures, differentiation of benign and malignant vertebral fractures, differentiation of acute and chronic vertebral fractures, and prediction of vertebral fractures). Subgroup analyses were conducted by different ML models (including ML and DL) and modeling methods (including CT, X-ray, MRI, and clinical features). ResultsEighty-one studies were included. ML demonstrated a diagnostic sensitivity of 0.91 and specificity of 0.95 for vertebral fractures. Subgroup analysis showed that DL (SROC 0.98) and CT (SROC 0.98) performed best overall. For osteoporotic fractures, ML showed a sensitivity of 0.93 and specificity of 0.96, with DL (SROC 0.99) and X-ray (SROC 0.99) performing better. For differentiating benign from malignant fractures, ML achieved a sensitivity of 0.92 and specificity of 0.93, with DL (SROC 0.96) and MRI (SROC 0.97) performing best. For differentiating acute from chronic vertebral fractures, ML showed a sensitivity of 0.92 and specificity of 0.93, with ML (SROC 0.96) and CT (SROC 0.97) performing best. For predicting vertebral fractures, ML had a sensitivity of 0.76 and specificity of 0.87, with ML (SROC 0.80) and clinical features (SROC 0.86) performing better. ConclusionsML, especially DL models applied to CT, MRI, and X-ray, shows high diagnostic accuracy for vertebral fractures. ML also effectively predicts osteoporotic vertebral fractures, aiding in tailored prevention strategies. Further research and validation are required to confirm ML’s clinical efficacy.

Novel Use of Fractal Analysis for Quantifying Polymethylmethacrylate Distribution Patterns in Osteoporotic and Malignant Vertebral Compression Fractures Following Vertebroplasty.

Purpose: Fractal analysis is a mathematical tool which allows the evaluation of complex microstructural features within materials that cannot be expressed in traditional geometric terms. The purpose of this study is to quantify the differences in polymethylmethacrylate intravertebral cement spatial distribution patterns following vertebroplasty using fractal analysis through the examination of osteoporotic and malignant compression fractures. Methods: Frontal and lateral post-vertebroplasty radiographs were evaluated from 29 patients with osteoporotic and malignant compression fractures who underwent vertebroplasty. The individually treated vertebra were divided into osteoporotic (n = 35) and malignant groups (n = 41). Images underwent segmentation, thresholding, and binarization prior to fractal analysis. Fractal dimension and lacunarity values were derived from the region of interest in treated vertebrae using the "box-counting" and "gliding-box" techniques respectively using ImageJ. The mean values of both parameters were compared between the 2 groups. Results: The mean fractal dimension was significantly higher in the malignant vertebral compression fracture group (1.53 ± 0.08) compared to the osteoporotic group (1.34 ± 0.17; P < .001). Similarly, mean lacunarity values were significantly higher in the malignant fracture group (0.50 ± 0.09) compared to the osteoporotic group (0.37 ± 0.10; P < .001). Conclusions: Fractal dimension and lacunarity values of cement spatial distribution patterns obtained from the post-vertebroplasty radiographs can differentiate between benign osteoporotic and malignant vertebral compression fractures. This novel technique may be useful for evaluating cement spatial distribution patterns in spine augmentation procedures, although further research is warranted in this area.

Diagnostic significance of multisequence MRI radiomics models in distinguishing benign and malignant spinal fractures

PurposeTo investigate the diagnostic value of multi-sequence magnetic resonance imaging (MRI) in benign and malignant spinal fractures. Materials and methodsMRI data of patients with pathologically confirmed malignant vertebral fractures were retrospectively collected and compared with those with benign vertebral fractures. The image omics features of T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI) were extracted respectively, and were randomly divided into training and test sets at 8:2. The training set adopted recursive feature elimination method and minimum absolute contraction and selection operator (LASSO) regression to screen the variables. Three image omics models, T1WI, T2WI and the combination of two sequences, were constructed by logistic regression, and the diagnostic efficiency of each model was verified by the test set. The receiver operating curve evaluated the diagnostic efficiency of the model. ResultsA total of 111 vertebrae with fracture were included in 97 patients, 55 with benign fracture and 56 with malignant fracture. In the training set, the AUC, sensitivity and specificity of the T1WI model were 0.892, 88.9% and 81.4%, respectively. In T2WI model, the results were 0.924, 82.2% and 95.3%, respectively. The combined models of the two sequences were 0.934, 90.0% and 87.5%, respectively. In the test set, the AUC, sensitivity and specificity of T1WI model were 0.877, 80.0% and 92.3%, respectively. In T2WI model, the results were 0.923, 90.0% and 92.3%, respectively. The combined models were 0.933, 100.0% and 87.5%, respectively. The diagnostic efficiency of the two-sequence combined image omics model was better than that of the radiologist. ConclusionThe combined model, incorporating T1WI and T2WI, outperformed single-sequence models in terms of diagnostic accuracy. The integration of multisequence radiomic features provided enhanced texture information, contributing to superior accuracy and efficiency in diagnosing malignant spinal fractures.

Deep Learning to Differentiate Benign and Malignant Vertebral Fractures at Multidetector CT.

Background Differentiating between benign and malignant vertebral fractures poses diagnostic challenges. Purpose To investigate the reliability of CT-based deep learning models to differentiate between benign and malignant vertebral fractures. Materials and Methods CT scans acquired in patients with benign or malignant vertebral fractures from June 2005 to December 2022 at two university hospitals were retrospectively identified based on a composite reference standard that included histopathologic and radiologic information. An internal test set was randomly selected, and an external test set was obtained from an additional hospital. Models used a three-dimensional U-Net encoder-classifier architecture and applied data augmentation during training. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with that of two residents and one fellowship-trained radiologist using the DeLong test. Results The training set included 381 patients (mean age, 69.9 years ± 11.4 [SD]; 193 male) with 1307 vertebrae (378 benign fractures, 447 malignant fractures, 482 malignant lesions). Internal and external test sets included 86 (mean age, 66.9 years ± 12; 45 male) and 65 (mean age, 68.8 years ± 12.5; 39 female) patients, respectively. The better-performing model of two training approaches achieved AUCs of 0.85 (95% CI: 0.77, 0.92) in the internal and 0.75 (95% CI: 0.64, 0.85) in the external test sets. Including an uncertainty category further improved performance to AUCs of 0.91 (95% CI: 0.83, 0.97) in the internal test set and 0.76 (95% CI: 0.64, 0.88) in the external test set. The AUC values of residents were lower than that of the best-performing model in the internal test set (AUC, 0.69 [95% CI: 0.59, 0.78] and 0.71 [95% CI: 0.61, 0.80]) and external test set (AUC, 0.70 [95% CI: 0.58, 0.80] and 0.71 [95% CI: 0.60, 0.82]), with significant differences only for the internal test set (P < .001). The AUCs of the fellowship-trained radiologist were similar to those of the best-performing model (internal test set, 0.86 [95% CI: 0.78, 0.93; P = .39]; external test set, 0.71 [95% CI: 0.60, 0.82; P = .46]). Conclusion Developed models showed a high discriminatory power to differentiate between benign and malignant vertebral fractures, surpassing or matching the performance of radiology residents and matching that of a fellowship-trained radiologist. © RSNA, 2024 See also the editorial by Booz and D'Angelo in this issue.

Machine Learning and Deep Learning in Spinal Injury: A Narrative Review of Algorithms in Diagnosis and Prognosis

Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.

The role of diffusion-weighted MRI in the accurate diagnosis of vertebral compression fractures: A comparative study

IntroductionAccurately distinguishing between benign and malignant vertebral compression fractures is crucial for clinical management. This study evaluated the predictive accuracy of diffusion-weighted imaging (DWI) in differentiating the cause of vertebral fractures using MRI. MethodsA longitudinal cross-over study was conducted at Jinnah Postgraduate Medical Centre (JPMC) Karachi from July 2018 to January 2021. Patients with vertebral compression fractures underwent T1-weighted, T2-weighted, and DWI imaging with ADC mapping on a 1.5 T MRI scanner. Imaging findings were compared with histopathologic results and clinical follow-up. Sensitivity, specificity, and ROC curve analyses were performed. ResultsThe study enrolled 303 patients with a mean age of 43.6 ± 10.9 years, of whom 118 were male. DWI demonstrated high accuracy in predicting the cause of vertebral compression fractures, with a sensitivity of 96.2 %, a specificity of 76.2 %, and an area under the ROC curve of 0.857. The optimal ADC cut-off value was 0.82 × 10˄-3 mm˄2/s, which yielded a positive predictive value of 79.7 % and a negative predictive value of 95.4 %. ConclusionsDWI is a safe and non-invasive imaging modality with excellent predictive accuracy in differentiating between benign and malignant vertebral compression fractures. Iso- or hypointensity of collapsed vertebral bodies on DWI suggests a benign lesion, while T2-weighted hyperintensity is highly indicative of malignancy. Low signal on ADC is also highly indicative of malignant vertebral fractures. Incorporating DWI improves accuracy in assessing vertebral lesions, especially when standard sequences are inconclusive. Implications for practiceDWI revolutionizes vertebral compression fracture diagnosis, distinguishing between benign and malignant cases. This precision guides treatment decisions, minimizing the necessity for invasive procedures like biopsy. As a safe and reliable imaging method, DWI elevates patient care, ensuring accurate diagnostics and improved outcomes.

A case of emphysematous osteomyelitis in the pediatric practice

Introduction. Emphysematous osteomyelitis is a life-threatening and rare disease in the clinical practice. A distinctive feature of the disease is gas in bones and in surrounding soft tissues on the background of predisposing factors which significantly worsen patient’s general condition (malignant neoplasms, diabetes mellitus, immunodeficiency, injuries and fractures). The authors present a clinical observation of diagnostics and management of emphysematous osteomyelitis in the right femur head in a 12-year-old child. The disease developed two months after a closed right-sided fracture of the femoral trochanter. In the literature, one can meet not more than 50 cases of emphysematous osteomyelitis.&#x0D; Relevance. In the available medical literature, we have not found any description of emphysematous osteomyelitis in children, so we considered it appropriate to publish a rare clinical case.&#x0D; Purpose. To analyze a rare case of emphysematous osteomyelitis in a child and to identify possible diagnostic and therapeutic errors occurred in the discussed case in Children’s Republic Clinical Hospital in Saransk.&#x0D; Conclusion. Currently, hematogenous osteomyelitis in children has become less common, and therefore there is no alertness for this pathology in physicians. We also have faced emphysematous osteomyelitis in our practice for the first time. Therefore, only when child’s condition worsened on day 4 after hospitalization, additional diagnostic tools were added, namely, X-ray of the knee and CT of the hip joint. Antibacterial therapy was started as well.

Value of 18F-FDG-PET/CT radiomics combined with clinical variables in the differential diagnosis of malignant and benign vertebral compression fractures

BackgroundVertebral compression fractures (VCFs) are common clinical problems that arise from various reasons. The differential diagnosis of benign and malignant VCFs is challenging. This study was designed to develop and validate a radiomics model to predict benign and malignant VCFs with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG-PET/CT).ResultsTwenty-six features (9 PET features and 17 CT features) and eight clinical variables (age, SUVmax, SUVpeak, SULmax, SULpeak, osteolytic destruction, fracture line, and appendices/posterior vertebrae involvement) were ultimately selected. The area under the curve (AUCs) of the radiomics and clinical–radiomics models were significantly different from that of the clinical model in both the training group (0.986, 0.987 vs. 0.884, p < 0.05) and test group (0.962, 0.948 vs. 0.858, p < 0.05), while there was no significant difference between the radiomics model and clinical–radiomics model (p > 0.05). The accuracies of the radiomics and clinical–radiomics models were 94.0% and 95.0% in the training group and 93.2% and 93.2% in the test group, respectively. The three models all showed good calibration (Hosmer–Lemeshow test, p > 0.05). According to the decision curve analysis (DCA), the radiomics model and clinical–radiomics model exhibited higher overall net benefit than the clinical model.ConclusionsThe PET/CT-based radiomics and clinical–radiomics models showed good performance in distinguishing between malignant and benign VCFs. The radiomics method may be valuable for treatment decision-making.

Applications of Diffusion-Weighted MRI to the Musculoskeletal System.

Diffusion-weighted imaging (DWI) is an established MRI technique that can investigate tissue microstructure at the scale of a few micrometers. Musculoskeletal tissues typically have a highly ordered structure to fulfill their functions and therefore represent an optimal application of DWI. Even more since disruption of tissue organization affects its biomechanical properties and may indicate irreversible damage. The application of DWI to the musculoskeletal system faces application-specific challenges on data acquisition including susceptibility effects, the low T2 relaxation time of most musculoskeletal tissues (2-70 msec) and the need for sub-millimetric resolution. Thus, musculoskeletal applications have been an area of development of new DWI methods. In this review, we provide an overview of the technical aspects of DWI acquisition including diffusion-weighting, MRI pulse sequences and different diffusion regimes to study tissue microstructure. For each tissue type (growth plate, articular cartilage, muscle, bone marrow, intervertebral discs, ligaments, tendons, menisci, and synovium), the rationale for the use of DWI and clinical studies in support of its use as a biomarker are presented. The review describes studies showing that DTI of the growth plate has predictive value for child growth and that DTI of articular cartilage has potential to predict the radiographic progression of joint damage in early stages of osteoarthritis. DTI has been used extensively in skeletal muscle where it has shown potential to detect microstructural and functional changes in a wide range of muscle pathologies. DWI of bone marrow showed to be a valuable tool for the diagnosis of benign and malignant acute vertebral fractures and bone metastases. DTI and diffusion kurtosis have been investigated as markers of early intervertebral disc degeneration and lower back pain. Finally, promising new applications of DTI to anterior cruciate ligament grafts and synovium are presented. The review ends with an overview of the use of DWI in clinical routine. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.

Differentiation of benign and malignant vertebral fractures using a convolutional neural network to extract CT-based texture features

PurposeTo assess the diagnostic performance of three-dimensional (3D) CT-based texture features (TFs) using a convolutional neural network (CNN)-based framework to differentiate benign (osteoporotic) and malignant vertebral fractures (VFs).MethodsA total of 409 patients who underwent routine thoracolumbar spine CT at two institutions were included. VFs were categorized as benign or malignant using either biopsy or imaging follow-up of at least three months as standard of reference. Automated detection, labelling, and segmentation of the vertebrae were performed using a CNN-based framework (https://anduin.bonescreen.de). Eight TFs were extracted: Varianceglobal, Skewnessglobal, energy, entropy, short-run emphasis (SRE), long-run emphasis (LRE), run-length non-uniformity (RLN), and run percentage (RP). Multivariate regression models adjusted for age and sex were used to compare TFs between benign and malignant VFs.ResultsSkewnessglobal showed a significant difference between the two groups when analyzing fractured vertebrae from T1 to L6 (benign fracture group: 0.70 [0.64–0.76]; malignant fracture group: 0.59 [0.56–0.63]; and p = 0.017), suggesting a higher skewness in benign VFs compared to malignant VFs.ConclusionThree-dimensional CT-based global TF skewness assessed using a CNN-based framework showed significant difference between benign and malignant thoracolumbar VFs and may therefore contribute to the clinical diagnostic work-up of patients with VFs.

Differentiation of benign versus malignant indistinguishable vertebral compression fractures by different machine learning with MRI-based radiomic features.

To explore an optimal machine learning (ML) model trained on MRI-based radiomic features to differentiate benign from malignant indistinguishable vertebral compression fractures (VCFs). This retrospective study included patients within 6weeks 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. 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. 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. • 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.

AGREEMENT ON MRI DIAGNOSIS IN COMPRESSIVE MALIGNANT VERTEBRAL FRACTURES.

Verify interobserver and intraobserver agreement of malignant compressive vertebral fractures (MCVF) diagnosis using magnetic resonance imaging (MRI). We retrospectively included a lumbar spine MRI of 63 patients with non-traumatic compressive vertebral fracture diagnoses. Each lumbar vertebra was classified as: without fracture, with fracture of benign characteristics, or with fracture of malignant characteristics. Two medical residents in radiology, one musculoskeletal radiologist fellow, one musculoskeletal radiologist, and two spine surgeons evaluated MRI exams, independently and blindly. Each observer performed two readings, with a 15-day interval between evaluations. A simple Kappa coefficient was used to calculate the intra and interobserver agreement. The reference standard classification was based on bone biopsy or clinical, and imaging follow-up of at least two years, for diagnostic performance analysis. Diagnostic performance was assessed by calculating sensitivity, specificity, accuracy, and positive and negative predictive values with a 95% confidence interval (CI). We observed substantial to perfect intraobserver agreement (kappa: 0.80 to 1.00) and substantial interobserver agreement (kappa 0.64 to 0.77). In general, the sensitivity for the detection of MCVF was moderate, except for the second-year radiology resident that achieved a lower sensitivity. The specificity, accuracy, and negative predictive value were high for all observers. MCVF diagnosis using MRI showed substantial interobserver agreement. The second-year medical resident achieved lower sensitivity but high specificity for MCVF. Regarding the seniors, there was no statistical significance between spine surgeons and the musculoskeletal radiologist. Level of Evidence III; Diagnostic.

Percutaneous mesh-container-plasty versus percutaneous kyphoplasty in the treatment of malignant thoracolumbar compression fractures: a retrospective cohort study.

This study aimed to compare the clinical and radiological results of percutaneous mesh-container-plasty versus percutaneous kyphoplasty in the treatment of malignant thoracolumbar compression fractures. Patients with malignant thoracolumbar compression fractures treated in a single tertiary care center between January 2011 and December 2020 were retrospectively reviewed and included in the study. Ninety-four patients who were diagnosed by pathological biopsy were divided into 2 groups according to the type of surgical treatment: the percutaneous kyphoplasty group (50 patients: 24 male, 26 female; mean age=73.02 ± 7.79 years) and the percutaneous mesh-container-plasty group (44 patients: 21 male, 23 female; mean age=74.68 ± 7.88 years). The epidemiological data, surgical outcomes, and clinical and radiological features were compared between the 2 groups. Cement leakage, height restoration, deformity correction, and cement distribution were calculated from the radiographs. The visual analog scale, Oswestry disability index, Karnofsky performance scale scores, and short-form 36 health survey domains role physi cal and bodily pain were calculated preoperatively, immediately, and 1 year postoperatively. The visual analog scale score improved from 5 (range=4-6) preoperatively to 2 (range=2-3) immediately postoperatively in the percutaneous kyphoplasty group and from 5 (range=4-6) preoperatively to 2 (range=2-2) immediately postoperatively in the percutane ous mesh-container-plasty group; there was a significant difference between the 2 groups (P=.018). Although Oswestry disability index, Karnofsky performance scale, short-form 36 health survey domains bodily pain and role physical significantly improved in both groups after surgery compared to the preoperative period, there was no significant difference between the 2 groups (P > .05). The mean cost in the percutaneous kyphoplasty group was lower than that in the percutaneous mesh-container-plasty group (5563 ± 439 vs. 6569 ± 344 thousand dollars, P < .05). There was no difference between the cement volume in the 2 groups, and cement distribution in the percutaneous mesh-container-plasty group was higher than that in the percutaneous kyphoplasty group (44.30% ± 10.25% vs. 32.54% ± 11.76%, P < .05). Cement leakage was found to be lesser in the percutaneous mesh-container-plasty group (7/44) than in the percutane ous kyphoplasty group (18/50, P < .05). There were no statistically significant differences in the recovery of vertebral body height and improvement of segmental kyphosis between the 2 groups (P > .05). Percutaneous kyphoplasty and percutaneous mesh-container-plasty both have significant abilities in functional recovery, height restoration, and segment kyphosis improvement in treating malignant thoracolumbar compression fractures. Percutaneous mesh container-plasty may be better able to relieve pain, inhibit cement leakage, and improve cement distribution than percutaneous kypho plasty. However, percutaneous mesh-container-plasty requires a relatively longer procedure and is more expensive than percutaneous kyphoplasty. Level III, Therapeutic Study.

Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy

Although CT radiomics has shown promising results in the evaluation of vertebral fractures, the need for manual segmentation of fractured vertebrae limited the routine clinical implementation of radiomics. Therefore, automated segmentation of fractured vertebrae is needed for successful clinical use of radiomics. In this study, we aimed to develop and validate an automated algorithm for segmentation of fractured vertebral bodies on CT, and to evaluate the applicability of the algorithm in a radiomics prediction model to differentiate benign and malignant fractures. A convolutional neural network was trained to perform automated segmentation of fractured vertebral bodies using 341 vertebrae with benign or malignant fractures from 158 patients, and was validated on independent test sets (internal test, 86 vertebrae [59 patients]; external test, 102 vertebrae [59 patients]). Then, a radiomics model predicting fracture malignancy on CT was constructed, and the prediction performance was compared between automated and human expert segmentations. The algorithm achieved good agreement with human expert segmentation at testing (Dice similarity coefficient, 0.93–0.94; cross-sectional area error, 2.66–2.97%; average surface distance, 0.40–0.54 mm). The radiomics model demonstrated good performance in the training set (AUC, 0.93). In the test sets, automated and human expert segmentations showed comparable prediction performances (AUC, internal test, 0.80 vs 0.87, p = 0.044; external test, 0.83 vs 0.80, p = 0.37). In summary, we developed and validated an automated segmentation algorithm that showed comparable performance to human expert segmentation in a CT radiomics model to predict fracture malignancy, which may enable more practical clinical utilization of radiomics.

Percutaneous reinforced cementoplasty using spindles as a palliative option for malignant fractures of the humerus

Percutaneous reinforced cementoplasty using spindles as a palliative option for malignant fractures of the humerus

Vessel-Plasty Using Bone-Filling Mesh Container for Treatment of Malignant Severe Compression Fractures in Cervical Vertebrae.

ObjectiveTo evaluate the feasibility, safety, and efficacy of vessel-plasty using bone-filling mesh container (BFMC) for malignant severe compression fractures of cervical vertebra.MethodsThis study prospectively recruited fifteen consecutive patients (eight men, seven women; mean age, 57.4 years) with severe malignant compression fractures of cervical vertebrae for vessel-plasty. Procedure duration, incidence of cement leakage and other complications, pain relief and improvement of neck function were analyzed. Pain was assessed using a visual analog scale (VAS) and function by the neck disability index (NDI), with scores recorded before the procedure and at 3 days and 1, 3, 6 and 12 months after the procedure.ResultsA total of 16 vertebrae were treated. All vertebrae had destruction of bone in more than one place as well as broken bone walls. Mean procedure duration was 42.9±13.6 minutes. Bone cement leakage occurred in two vertebrae without any symptoms. No procedure-related complications occurred. Mean VAS and NDI declined from 7.1 ± 1.4 and 63.6 ± 16.3, respectively, before the procedure to 3.5 ± 1.1 and 37.4 ± 11.0, respectively, at three days after the procedure (P < 0.01). CT images at three months after the procedure confirmed that there were no cases of refractures at the treated or adjacent levels, recurrence of vertebral collapse and mobilization of bone cement block.ConclusionVessel-plasty using BFMC appears to be effective and safe for malignant severe compression fractures in cervical vertebrae. It is effective in stabilizing vertebral body, relieving pain.

Case series study: the diagnosis and treatment of fifty tumors and pseudotumors at the proximal femur

Tumor and pseudotumor (TP) at the proximal femur (PF) can seriously affect mortality, extremity function, and body integrity. However, reports often focused on a specific tumor, not regional lesions. This study focuses on clinical findings, imaging, micro-pathology, and the treatment of all TP at the site. The study involved all patients who had a confirmed tumor or pseudotumor diagnosis at the PF. The clinical findings, X-ray, and biopsy were recorded and analyzed. Treatment was optional depending on the patient’s situation and available condition of the hospital. The functional outcome, bone healing were defined at the last examination or two years of follow-up. Fifty patients were involved in the study. Twenty-four patients had apparent tumors. TP at the PF, neck-trochanter, trochanters, and neck were 21 (42%), 16 (32%), 9 (18%), and 4 (8%) cases, respectively. There were 29 (58%) pathologic fractures. Biopsy was made for all patients. Twenty-three cases (46%) were malignant, and 8 (16%) cases were giant cell tumors. Thirty-three patients suffered from an operation. Ennerking’s functional score was excellent, good, fair, and poor in 24 (48%), 5 (10%), 1 (2%), and 20 (40%) patients, respectively. For the last outcomes of 33 operated patients, 17 healed, three unchanged, one worse, and two dead. For the PF TP, the rate of malignant and pathological fracture was high. The giant cell tumor was not rare. The resection of the TP combined with grafts using ordinary fixation devices was satisfactory.

A deep learning-based method for the diagnosis of vertebral fractures on spine MRI: retrospective training and validation of ResNet.

To improve the performance of less experienced clinicians in the diagnosis of benign and malignant spinal fracture on MRI, we applied the ResNet50 algorithm to develop a decision support system. A total of 190 patients, 50 with malignant and 140 with benign fractures, were studied. The visual diagnosis was made by one senior MSK radiologist, one fourth-year resident, and one first-year resident. The MSK radiologist also gave the binary score for 15 qualitative imaging features. Deep learning was implemented using ResNet50, using one abnormal spinal segment selected from each patient as input. The T1W and T2W images of the lesion slice and its two neighboring slices were considered. The diagnostic performance was evaluated using tenfold cross-validation. The overall reading accuracy was 98, 96, and 66% for the senior MSK radiologist, fourth-year resident, and first-year resident, respectively. Of the 15 imaging features, 10 showed a significant difference between benign and malignant groups with p < = 0.001. The accuracy achieved by using the ResNet50 deep learning model for the identified abnormal vertebral segment was 92%. Compared to the first-year resident's reading, the model improved the sensitivity from 78 to 94% (p < 0.001) and the specificity from 61 to 91% (p < 0.001). Our deep learning-based model may provide information to assist less experienced clinicians in the diagnosis of spinal fractures on MRI. Other findings away from the vertebral body need to be considered to improve the model, and further investigation is required to generalize our findings to real-world settings.

Evaluation and outcome of percutaneous vertebroplasty for multilevel osteoporotic and malignant vertebral fractures (more than two)

BackgroundPercutaneous vertebroplasty (PV) is now a popular technique used to augment and stabilize the vertebral body fracture and to reduce pain as well. AimThe aim of the study is to evaluate the outcome of percutaneous vertebroplasty of multilevel osteoporotic and malignant fractures (more than two). Study designThis is a retrospective clinical case series study. Patients and methodsThis study was conducted on 30 patients with multilevel osteoporotic and malignant compression fractures (more than two). Visual analogue scale (VAS) was used to evaluate the functional outcome. All patients were treated using percutaneous vertebroplasty. They were followed for 6 months postoperatively. ResultsThe functional state of all patients improved after percutaneous vertebroplasty. According to the visual analogue scale (VAS), the preoperative VAS score was (8.43 ± 1.19). Immediate postoperative VAS was (3.07 ± 1.20) and after six months it dropped to (1.13 ± 0.67). There was a significant improvement of pain (p < 0.001). Asymptomatic leakage in the disc space was reported in two patients. A single case of pulmonary embolism was reported who complained of dyspnea. This patient was admitted to ICU and managed with proper medications with satisfactory results. ConclusionsMultilevel PV is proved to be a safe, cost effective and successful procedure that could reduce pain significantly and improve patient’s mobility.

Vertebroplasty, Kyphoplasty, and Implant-Based Mechanical Vertebral Augmentation.

Vertebral compression fractures are a global public health issue with a quantifiable negative impact on patient morbidity and mortality. The contemporary approach to the treatment of osteoporotic fragility fractures has moved beyond first-line nonsurgical management. An improved understanding of biomechanical forces, consequential morbidity and mortality, and the drive to reduce opioid use has resulted in multidisciplinary treatment algorithms and significant advances in augmentation techniques. This review will inform musculoskeletal radiologists, interventionalists, and minimally invasive spine surgeons on the proper work-up of patients, imaging features differentiating benign and malignant pathologic fractures, high-risk fracture morphologies, and new mechanical augmentation device options, and it describes the appropriate selection of devices, complications, outcomes, and future trends.