Review of the Imaging Features of Benign Osteoporotic and Malignant Vertebral Compression Fractures

Whole-body MRI Versus [18F]FDG PET/CT in Diagnosing and Monitoring Plasmacytomas: A Comparative Study

BACKGROUND/OBJECTIVES

Current guidelines recommend [18F]FDG PET/CT as the preferred imaging modality for suspected extramedullary bone plasmacytomas, while whole-body magnetic resonance imaging (WB-MRI) is indicated for solitary bone plasmacytomas. Despite these recommendations, the available evidence comparing the diagnostic efficacy of both techniques remains limited. The aim of this study was to compare the diagnostic efficacy of WB-MRI and [18F]FDG PET/CT in the initial evaluation of plasmacytomas.

METHODS

We performed a multicenter, observational, and retrospective analysis of patients diagnosed with plasmacytoma who underwent WB-MRI and/or [18F]FDG PET/CT as part of their diagnostic workup. Lesions identified were categorized by anatomic location, and concordance between WB-MRI and [18F]FDG PET/CT findings was assessed. The McNemar test and Pearson χ2 test were used to compare detection rates between WB-MRI and [18F]FDG PET/CT.

RESULTS

The study included 73 patients (33 men) recruited between 2012 and 2023, age range 30-94 years (mean 63.4 ± 12.2 y). Of these, 56 patients underwent both diagnostic tests. Diagnoses revealed solitary plasmacytoma in 16 patients, concurrent multiple myeloma (MM) and plasmacytoma in 18 patients, and plasmacytoma in 22 patients with a prior MM history. Out of the 56 plasmacytomas, 40 were osseous and 16 were extramedullary. WB-MRI detected 98.2% of plasmacytomas compared with 83.9% for [18F]FDG PET/CT, with a statistically significant difference of OR 9 (95% CI: 1.2-394.5), P=0.021. Concordance was very high for osseous plasmacytomas but moderate for extramedullary plasmacytomas.

CONCLUSIONS

These findings suggest WB-MRI is an alternative to [18F]FDG PET/CT for detecting plasmacytomas. A comprehensive clinical and radiologic assessment is essential for the optimal evaluation of patients with plasmacytoma.

MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures

Objective

Vertebral compression fractures (VCFs) represent a prevalent clinical problem, yet distinguishing acute benign variants from malignant pathological fractures constitutes a persistent diagnostic dilemma. To develop and validate a MRI-based nomogram combining clinical and deep learning radiomics (DLR) signatures for the differentiation of benign versus malignant vertebral compression fractures (VCFs).

Methods

A retrospective cohort study was conducted involving 234 VCF patients, randomly allocated to training and testing sets at a 7:3 ratio. Radiomics (Rad) features were extracted using traditional Rad techniques, while 2.5-dimensional (2.5D) deep learning (DL) features were obtained using the ResNet50 model. These features were combined through feature fusion to construct deep learning radiomics (DLR) models. Through a feature fusion strategy, this study integrated eight machine learning architectures to construct a predictive framework, ultimately establishing a visualized risk assessment scale based on multimodal data (including clinical indicators and Rad features).The performance of the various models was evaluated using the receiver operating characteristic (ROC) curve.

Results

The standalone Rad model using ExtraTrees achieved AUC=0.801 (95%CI:0.693-0.909) in testing, while the DL model an AUC value of 0.805 (95% CI: 0.690-0.921) in the testing cohort. Compared with the Rad model and DL model, the performance superiority of the DLR model was demonstrated. Among all these models, the DLR model that employed ExtraTrees algorithm performed the best, with area under the curve (AUC) values of 0.971 (95% CI: 0.948-0.995) in the training dataset and 0.828 (95% CI: 0.727-0.929) in the testing dataset. The performance of this model was further improved when combined with clinical and MRI features to form the DLR nomogram (DLRN), achieving AUC values of 0.981 (95% CI: 0.964-0.998) in the training dataset and 0.871 (95% CI: 0.786-0.957) in the testing dataset.

Conclusion

Our study integrates handcrafted radiomics, 2.5D deep learning features, and clinical data into a nomogram (DLRN). This approach not only enhances diagnostic accuracy but also provides superior clinical utility. The novel 2.5D DL framework and comprehensive feature fusion strategy represent significant advancements in the field, offering a robust tool for radiologists to differentiate benign from malignant VCFs.

Comprehensive Review of the Utility of Dynamic Contrast-Enhanced MRI for the Diagnosis and Treatment Assessment of Spinal Benign and Malignant Osseous Disease

Conventional MRI is currently the preferred imaging technique for detection and evaluation of malignant spinal lesions. However, this technique is limited in its ability to assess tumor viability. Unlike conventional MRI, dynamic contrast-enhanced (DCE) MRI provides insight into the physiologic and hemodynamic characteristics of malignant spinal tumors and has been utilized in different types of spinal diseases. DCE has been shown to be especially useful in the cancer setting; specifically, DCE can discriminate between malignant and benign vertebral compression fractures as well as between atypical hemangiomas and metastases. DCE has also been shown to differentiate between different types of metastases. Furthermore, DCE can be useful in the assessment of radiation therapy for spinal metastases, including the prediction of tumor recurrence. This review considers data analysis methods utilized in prior studies of DCE-MRI data acquisition and clinical implications.

Surgical Considerations in Osteoporotic Dorso-Lumbar Spine Fractures

Background

Osteoporotic vertebral fractures are exponentially impacting health-care systems globally with the rapid increase in geriatric population. These fractures are seen most commonly in the dorso-lumbar spine. Lack of timely diagnosis and adequate treatment contributes significantly to morbidity and mortality. It has become vital to thoroughly evaluate these patients clinically, investigate them, optimise them, plan conservative and / or surgical treatment and provide comprehensive pre, peri and post-operative counselling and support.

Content

Historically, geriatric patients with multiple comorbidities with vertebral fractures were considered poor candidates for surgical treatment due to high anaesthetic and surgical risk. Those who were offered surgery were not adequately optimised pre-operatively and the fractures were under-stabilised surgically. Better understanding of the biomechanics of an osteoporotic vertebral column with dorso-lumbar fractures, combined with advances in anaesthetic and surgical techniques, implants and technologies facilitate successful surgeries on high-risk geriatric patients significantly reducing morbidity and improving quality of life. This article discusses the pathophysiology of dorso-lumbar osteoporotic vertebral fractures, their clinical presentation, investigative work-up, pre-operative optimization, indications for surgical intervention, various surgical modalities, techniques and technologies for optimal surgical outcomes, post-operative care and follow-up of patients.

Implications

In this article, the authors aim to provide an overview of the various pre, peri and postoperative considerations while dealing with patients of osteoporotic dorso-lumbar vertebral fractures. This review provides a comprehensive set of guidelines for the medical optimization and surgical management of these patients with an overview of current techniques, strategies and technologies designed to address the challenges associated with spine surgery in geriatric comorbid osteoporotic patients.

Sources

Content for this article has been sourced from routinely cited articles available via PubMed, National Institute of Health, census reports from United Nations, from previous articles by the authors and from the protocols established by the authors in their clinical practice based on experience and detailed case reviews.

Physical Therapy for Patients with Thoracolumbar Vertebral Fractures

Vertebral fractures are a common cause of back pain and pain-related functional impairments in elderly patients. Despite their widespread occurrence, vertebral fractures frequently remain underdiagnosed, often leading to suboptimal management and poor clinical outcomes. This review specifically examines the role of physical therapy (PT) in managing vertebral fractures, describing current literature and evidence-based guidelines from the American Physical Therapy Association and the American Academy of Orthopaedic Surgeons. PT following vertebral fractures has been shown to significantly improve back pain and patient-reported outcomes, with studies even showing a correlation between resistance and aerobic training with improved bone mineral density. These findings highlight the need for interdisciplinary care and comprehensive PT interventions to address the growing burden of vertebral fractures as their incidence rises with the aging population.

Incidence, epidemiology, radiology, and classification of metastatic spine tumors: WFNS Spine Committee recommendations

Spinal metastasis (SMs) are the most encountered tumors of the spine. Their occurrence is expected roughly around one to two years after primary tumor diagnosis. Since the advent of Magnetic Resonance Imaging (MRI), this technology has been considered the gold standard for SMs diagnosis and characterization due to its precise ability to comprehend the rate of soft tissue compression/invasion (dural sac/nervous tissue), which is one of the main drivers of management strategies. Computed Tomography (CT) remains unbeatable when a detailed bony anatomy and instability assessment is searched. Nuclear medicine technologies may have a role in diagnosis when standard MR or CT study findings are inconclusive or when the extent of the systemic metastatic disease is studied. The main objective of this study is to offer an update on the epidemiology and radiology of spinal metastasis (SMs), endorsed by the WFNS Spine Committee. A systematic review of the literature of the last ten years gave 1531 results with "spine/spinal metastatic tumors/metastasis AND radiology OR imaging OR classification" as search strings in all fields, of which 56 papers were fully analyzed. The results were discussed and voted on in two consensus meetings of the WFNS (World Federation of Neurosurgical Societies) Spine Committee, reaching a positive or negative consensus using the Delphi method. The committee stated nine recommendations on two main topics: (1) Incidence and epidemiology of SMs; (2) Radiology and classifications of SMs.

Machine learning models based on CT radiomics features for distinguishing benign and malignant vertebral compression fractures in patients with malignant tumors

BACKGROUND

Radiomics has become an important tool for distinguishing benign and malignant vertebral compression fractures (VCFs). It is more clinically significant to concentrate on patients who have malignant tumors and differentiate between benign and malignant VCFs.

PURPOSE

To explore the value of multiple machine learning (ML) models based on CT radiomics features for differentiating benign and malignant VCFs in patients with malignant tumors.

MATERIAL AND METHODS

This study retrospectively analyzed 78 patients with malignant tumors accompanied by VCFs, 45 patients with benign VCFs, and 33 patients with malignant VCFs. A total of 140 lesions (86 benign lesions, 54 malignant lesions) were ultimately included in this study. All patients were divided into training sets (n = 98) and validation sets (n = 42) according to the 7:3 ratio. The radiomics features were screened and dimensioned, and multiple radiomics ML models were constructed. The receiver operating characteristic (ROC) curve was performed to assess the diagnostic performance.

RESULTS

Five radiomics features were included in the model. All the ML models built have good diagnostic efficiency, among which the support vector machine (SVM) model performs better. The area under the curve (AUC), sensitivity, specificity, and accuracy in the training set were 0.908, 0.816, 0.883, and 0.857, respectively, while those in the validation set were 0.911, 0.647, 0.92, and 0.81, respectively.

CONCLUSION

A variety of ML models built based on CT radiomics features have good value for differentiating benign and malignant VCFs in malignant tumor patients, and the SVM model has a better performance.

Radiological Diagnosis and Advances in Imaging of Vertebral Compression Fractures

Vertebral compression fractures (VCFs) affect 1.4 million patients every year, especially among the globally aging population, leading to increased morbidity and mortality. Often characterized with symptoms of sudden onset back pain, decreased vertebral height, progressive kyphosis, and limited mobility, VCFs can significantly impact a patient's quality of life and are a significant public health concern. Imaging modalities in radiology, including radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) studies and bone scans, play crucial and evolving roles in the diagnosis, assessment, and management of VCFs. An understanding of anatomy, and the extent to which each imaging modality serves to elucidate that anatomy, is crucial in understanding and providing guidance on fracture severity, classification, associated soft tissue injuries, underlying pathologies, and bone mineral density, ultimately guiding treatment decisions, monitoring treatment response, and predicting prognosis and long-term outcomes. This article thus explores the important role of radiology in illuminating the underlying anatomy and pathophysiology, classification, diagnosis, treatment, and management of patients with VCFs. Continued research and advancements in imaging technologies will further enhance our understanding of VCFs and pave the way for personalized and effective management strategies.

Differential diagnostic value of radiomics models in benign versus malignant vertebral compression fractures: A systematic review and meta-analysis

PURPOSE

Early diagnosis of benign and malignant vertebral compression fractures by analyzing imaging data is crucial to guide treatment and assess prognosis, and the development of radiomics made it an alternative option to biopsy examination. This systematic review and meta-analysis was conducted with the purpose of quantifying the diagnostic efficacy of radiomics models in distinguishing between benign and malignant vertebral compression fractures.

METHODS

Searching on PubMed, Embase, Web of Science and Cochrane Library was conducted to identify eligible studies published before September 23, 2023. After evaluating for methodological quality and risk of bias using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), we selected studies providing confusion matrix results to be included in random-effects meta-analysis.

RESULTS

A total of sixteen articles, involving 1,519 vertebrae with pathological-diagnosed tumor infiltration, were included in our meta-analysis. The combined sensitivity and specificity of the top-performing models were 0.92 (95 % CI: 0.87-0.96) and 0.93 (95 % CI: 0.88-0.96), respectively. Their AUC was 0.97 (95 % CI: 0.96-0.99). By contrast, radiologists' combined sensitivity was 0.90 (95 %CI: 0.75-0.97) and specificity was 0.92 (95 %CI: 0.67-0.98). The AUC was 0.96 (95 %CI: 0.94-0.97). Subsequent subgroup analysis and sensitivity test suggested that part of the heterogeneity might be explained by differences in imaging modality, segmentation, deep learning and cross-validation.

CONCLUSION

We found remarkable diagnosis potential in correctly distinguishing vertebral compression fractures in complex clinical contexts. However, the published radiomics models still have a great heterogeneity, and more large-scale clinical trials are essential to validate their generalizability.

Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.

Clinical Oversight and Delayed Diagnosis of a Pathological Compression Fracture Causing Paraplegia

While osteoporosis is the primary cause of vertebral compression fractures (VCFs), it's crucial to promptly recognize pathological fractures through comprehensive diagnostic tests, including vertebral biopsies, to determine the exact etiology. For instance, a 66-year-old male with osteoporosis experienced worsening lower limb weakness and back pain after an initial vertebroplasty for a T12 compression fracture. Subsequent MRI revealed severe circumferential extradural compression at T12, leading to further surgeries that eventually uncovered metastatic adenocarcinoma from a pancreatic tumor. This case highlights the importance of precise diagnosis through vertebral biopsy and the necessity of sufficient ventral decompression or corpectomy, coupled with extensive laminectomy, to address severe neurological impairments like paraplegia. Prompt and accurate interventions can significantly improve patient outcomes and quality of life.

High rate of progression to symptomatic multiple myeloma in patients with smoldering myeloma and isolated osteoporotic vertebral fracture

Multiple myeloma (MM) frequently causes vertebral fractures (VF). Some are lytic lesions and others have the aspect of benign osteoporotic fractures not requiring anti-myeloma treatment. We explored outcome of these patients with smoldering myeloma (SM) and osteoporotic VF. In this retrospective bi-centric study, patients were identified using a systematic keyword search on electronic medical records. Patients with SM and isolated VF of osteoporotic aspect without indications for myeloma-specific therapy were included. Overall, 13 (7 %) of the 184 identified patients had SM and VF confirmed to be osteoporotic (median number of VF was 3). During follow-up, 12 (92 %) patients evolved to symptomatic MM, 7 (54 %) of them within 18 months (early progressors). Myeloma defining events were new lytic bone lesions in 7 patients (53.8 %). The serum calcium level was significantly higher in the early progressor group (median 2.35 IQR [2.31-2.38] and 2.28 IQR [2.21-2.29] respectively, p = 0.003). Early progressors had a higher number of VF at diagnosis (3.0 [2.0-5.5] vs 1.0 [1.0-2.5], p = 0.18) and more frequently evolved to symptomatic MM because of lytic bone lesions (5 [71 %] vs 2 [33 %], p = 0.13) compared to late progressors. VF of osteoporotic appearance in the context of SM is a rare situation but at high risk of rapid progression to symptomatic MM, suggesting that they may represent bone fragility linked to MM infiltration rather than solely osteoporotic fractures. Further studies are needed to assess if earlier treatment might be beneficial in this population.

Assessing osteoporosis in postmenopausal women: preliminary results using a novel lumbar spine phantom-based MRI scoring method

OBJECTIVE

To develop a novel magnetic resonance imaging (MRI) phantom for producing F-score (for fat) and W-score (for water) and to evaluate the performance of these scores in assessing osteoporosis and related vertebral fractures.

MATERIALS AND METHODS

First, a real-time phantom consisting of oil and water tubes was manufactured. Then, 30 female volunteers (age: 62.3 ± 6.3 years) underwent lumbar spine examination with MRI (using a novel phantom) and dual-energy X-ray absorptiometry (DXA), following ethical approval. MRI phantom-based F-score and W-score were defined by normalizing the vertebral signal intensities (SIs) by the oil and water SIs of the phantom on T1- and T2-weighted images, respectively. The diagnostic performances of the new scores for assessing osteoporosis and vertebral fractures were examined using receiver operating characteristic analysis and compared with DXA-measured areal bone mineral density (DXA-aBMD).

RESULTS

The F-score and W-score were greater in the osteoporotic patients (3.93 and 2.29) than the non-osteoporotic subjects (3.05 and 1.79) and achieved AUC values of 0.85 and 0.74 (p < 0.05), respectively, when detecting osteoporosis. Similarly, F-score and W-score had greater values for the fracture patients (3.94 and 2.53) than the non-fracture subjects (3.14 and 1.69) and produced better AUC values (0.90 for W-score and 0.79 for F-score) compared to DXA-aBMD (AUC: 0.27, p < 0.05). In addition, the F-score and W-score had a strong correlation (r = 0.77; p < 0.001).

CONCLUSION

A novel real-time lumber spine MRI phantom was developed, based upon which newly defined F-score and W-score were able to detect osteoporosis and demonstrated an improved ability over DXA-aBMD in differentiating patients with vertebral fractures.

The Need for Comprehensive Medical Management in Pyogenic Spondylodiscitis: A Review Article

The incidence of spontaneous or primary spondylodiscitis has been increasing over the years, affecting the aging population with multiple comorbidities. Several conditions influencing treatment outcomes stand out, such as diabetes mellitus, renal insufficiency, cardiovascular and respiratory dysfunction, and malnutrition. Due to these, the question arises regarding properly managing their current conditions and pre-existing disease states. Treatment plans must consider all concomitant comorbidities rather than just the infectious process. This can be done with the help of multidisciplinary teams to provide comprehensive care for patients with pyogenic spondylodiscitis. To date, there is no article regarding comprehensive medicine for spontaneous pyogenic spondylodiscitis; hence, this paper reviews the evidence available in current literature, recognizes knowledge gaps, and suggests comprehensive care for treating patients with spinal infections. Pre-requisites for implementing multidisciplinary teams include leadership, administrative support, and team dynamics. This group comprises an appointed leader, coordinator, and different subspecialists, such as orthopedic surgeons, infectious disease specialists, internists, rehabilitation doctors, psychiatrists, microbiologists, radiologists, nutritionists, pharmacologists, nurses, and orthotists working together with mutual trust and respect. Employing collaborative teams allows faster time for diagnosis and improves clinical outcomes, better quality of life, and patient satisfaction. Forefront communication is clear and open between all team members to provide holistic patient care. With these in mind, the need for employing multidisciplinary teams and the feasibility of its implementation emerges, showing a promising and logical path toward providing comprehensive care in managing multimorbid patients with pyogenic spondylodiscitis.

Deep learning-based automated high-accuracy location and identification of fresh vertebral compression fractures from spinal radiographs: a multicenter cohort study

Background

Digital radiography (DR) is a common and widely available examination. However, spinal DR cannot detect bone marrow edema, therefore, determining vertebral compression fractures (VCFs), especially fresh VCFs, remains challenging for clinicians.

Methods

We trained, validated, and externally tested the deep residual network (DRN) model that automated the detection and identification of fresh VCFs from spinal DR images. A total of 1,747 participants from five institutions were enrolled in this study and divided into the training cohort, validation cohort and external test cohorts (YHDH and BMUH cohorts). We evaluated the performance of DRN model based on the area under the receiver operating characteristic curve (AUC), feature attention maps, sensitivity, specificity, and accuracy. We compared it with five other deep learning models and validated and tested the model internally and externally and explored whether it remains highly accurate for an external test cohort. In addition, the influence of old VCFs on the performance of the DRN model was assessed.

Results

The AUC was 0.99, 0.89, and 0.88 in the validation, YHDH, and BMUH cohorts, respectively, for the DRN model for detecting and discriminating fresh VCFs. The accuracies were 81.45% and 72.90%, sensitivities were 84.75% and 91.43%, and specificities were 80.25% and 63.89% in the YHDH and BMUH cohorts, respectively. The DRN model generated correct activation on the fresh VCFs and accurate peak responses on the area of the target vertebral body parts and demonstrated better feature representation learning and classification performance. The AUC was 0.90 (95% confidence interval [CI] 0.84-0.95) and 0.84 (95% CI 0.72-0.93) in the non-old VCFs and old VCFs groups, respectively, in the YHDH cohort (p = 0.067). The AUC was 0.89 (95% CI 0.84-0.94) and 0.85 (95% CI 0.72-0.95) in the non-old VCFs and old VCFs groups, respectively, in the BMUH cohort (p = 0.051).

Conclusion

In present study, we developed the DRN model for automated diagnosis and identification of fresh VCFs from spinal DR images. The DRN model can provide interpretable attention maps to support the excellent prediction results, which is the key that most clinicians care about when using the model to assist decision-making.

Surgical management of lower limb radiculopathy following acute singe-level osteoporotic vertebral fracture of lower lumbar spine in geriatric patient: a retrospective study

BACKGROUND

Radiculopathy of the lower limb after acute osteoporotic vertebral fractures (OVFs) in the lower lumbar spine is uncommon in geriatric patients. Moreover, surgical intervention is generally recommended in patients who are irresponsive to conservative treatment. Determining an optimum surgical strategy is challenging considering the poor general condition of this population. Thus, herein, we established an algorithm for surgically managing this clinical scenario, hoping to provide a reference for making a surgical decision.

METHODS

We retrospectively studied patients who suffered from new-onset radiculopathy of the lower limb after acute single-level OVFs in the lower lumbar spine and eventually underwent surgical intervention at our department. Information on the demographics, bone quality, AO spine classification of the vertebral fracture, pre-existing degenerative changes, including foraminal stenosis and lumbar disc herniation, and surgical intervention type was collected. Additionally, clinical outcomes, including preoperative and postoperative visual analog scale (VAS) scores for back and leg pain, Oswestry disability index (ODI), and MacNab criterion for response to surgery, were evaluated.

RESULTS

From September 2019 to December 2021, a total of 22 patients with a mean age of 68.59 ± 9.74 years were analyzed. The most involved vertebra was L5 (54.5%), followed by L4 (27.3%) and L3 (18.2%). Among the 22 patients, 15 (68.2%) were diagnosed with the A1 type fracture of AO classification, and among them, 11 (73.3%) were characterized by the collapse of the inferior end plate (IEP). Three patients (13.6%) suffered from A2-type fractures, whereas four patients (18.2%) suffered from A3-type fractures. Pre-existing degenerative changes were observed in 12 patients (54.5%) of the patients. A total of 16 patients (72.7%) were treated by percutaneous kyphoplasty (PKP). Additionally, three patients underwent posterior instrumentation and fusion, two patients underwent a secondary endoscopic foraminoplasty, and one patient underwent a secondary radiofrequency ablation. The mean follow-up period was 17.42 ± 9.62 months. The mean VAS scores for leg and back pain and ODI decreased significantly after the surgery (P < 0.05). The total satisfaction rate at the last follow-up was 90.9% per the Macnab criterion.

CONCLUSION

Patients with OVFs in the IEP are predisposed to suffer from radiculopathy of the lower limb. PKP alone or in combination with other minimally invasive surgical strategies is safe and effective in treating stable fractures. Additionally, aggressive surgical intervention should be considered in patients with unstable fractures or severe foraminal encroachment.

Imaging of Multiple Myeloma: Present and Future

Multiple myeloma (MM) is the second most common adult hematologic malignancy, and early intervention increases survival in asymptomatic high-risk patients. Imaging is crucial for the diagnosis and follow-up of MM, as the detection of bone and bone marrow lesions often dictates the decision to start treatment. Low-dose whole-body computed tomography (CT) is the modality of choice for the initial assessment, and dual-energy CT is a developing technique with the potential for detecting non-lytic marrow infiltration and evaluating the response to treatment. Magnetic resonance imaging (MRI) is more sensitive and specific than 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for the detection of small focal lesions and diffuse marrow infiltration. However, FDG-PET/CT is recommended as the modality of choice for follow-up. Recently, diffusion-weighted MRI has become a new technique for the quantitative assessment of disease burden and therapy response. Although not widespread, we address current proposals for structured reporting to promote standardization and diminish variations. This review provides an up-to-date overview of MM imaging, indications, advantages, limitations, and recommended reporting of each technique. We also cover the main differential diagnosis and pitfalls and discuss the ongoing controversies and future directions, such as PET-MRI and artificial intelligence.

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

INTRODUCTION

Accurately 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.

METHODS

A 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.

RESULTS

The 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 %.

CONCLUSIONS

DWI 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 PRACTICE

DWI 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.

Preoperative Prediction of New Vertebral Fractures after Vertebral Augmentation with a Radiomics Nomogram

The occurrence of new vertebral fractures (NVFs) after vertebral augmentation (VA) procedures is common in patients with osteoporotic vertebral compression fractures (OVCFs), leading to painful experiences and financial burdens. We aim to develop a radiomics nomogram for the preoperative prediction of NVFs after VA. Data from center 1 (training set: n = 153; internal validation set: n = 66) and center 2 (external validation set: n = 44) were retrospectively collected. Radiomics features were extracted from MRI images and radiomics scores (radscores) were constructed for each level-specific vertebra based on least absolute shrinkage and selection operator (LASSO). The radiomics nomogram, integrating radiomics signature with presence of intravertebral cleft and number of previous vertebral fractures, was developed by multivariable logistic regression analysis. The predictive performance of the vertebrae was level-specific based on radscores and was generally superior to clinical variables. RadscoreL2 had the optimal discrimination (AUC ≥ 0.751). The nomogram provided good predictive performance (AUC ≥ 0.834), favorable calibration, and large clinical net benefits in each set. It was used successfully to categorize patients into high- or low-risk subgroups. As a noninvasive preoperative prediction tool, the MRI-based radiomics nomogram holds great promise for individualized prediction of NVFs following VA.

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

BACKGROUND

Vertebral 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).

RESULTS

Twenty-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.

CONCLUSIONS

The 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.

A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI

To train an artificial neural network model using 3D radiomic features to differentiate benign from malignant vertebral compression fractures (VCFs) on MRI. This retrospective study analyzed sagittal T1-weighted lumbar spine MRIs from 91 patients (average age of 64.24 ± 11.75 years) diagnosed with benign or malignant VCFs from 2010 to 2019, of them 47 (51.6%) had benign VCFs and 44 (48.4%) had malignant VCFs. The lumbar fractures were three-dimensionally segmented and had their radiomic features extracted and selected with the wrapper method. The training set consisted of 100 fractured vertebral bodies from 61 patients (average age of 63.2 ± 12.5 years), and the test set was comprised of 30 fractured vertebral bodies from 30 patients (average age of 66.4 ± 9.9 years). Classification was performed with the multilayer perceptron neural network with a back-propagation algorithm. To validate the model, the tenfold cross-validation technique and an independent test set (holdout) were used. The performance of the model was evaluated using the average with a 95% confidence interval for the ROC AUC, accuracy, sensitivity, and specificity (considering the threshold = 0.5). In the internal validation test, the best model reached a ROC AUC of 0.98, an accuracy of 95% (95/100), a sensitivity of 93.5% (43/46), and specificity of 96.3% (52/54). In the validation with independent test set, the model achieved a ROC AUC of 0.97, an accuracy of 93.3% (28/30), a sensitivity of 93.3% (14/15), and a specificity of 93.3% (14/15). The model proposed in this study using radiomic features could differentiate benign from malignant vertebral compression fractures with excellent performance and is promising as an aid to radiologists in the characterization of VCFs.

Geriatric spine fractures - Demography, changing trends, challenges and special considerations: A narrative review

The aim of this manuscript was to summarize the demography and changing trends of geriatric spinal injuries and to enumerate the challenges and special considerations in the care of geriatric spinal injuries. PubMed, Scopus and Embase databases were searched for literature on geriatric spine fractures using MeSH terms 'aged', 'aged, 80 and over', 'elderly', 'spinal fracture/epidemiology', spinal fracture/therapy∗' and keywords pertaining to the same. The search results were screened for appropriate articles and reviewed. There is a high community prevalence of elderly vertebral fractures ranging from 18% to as high as 51%. The proportion of older patients among the spinal injured is rising as well. There is a higher chance of missing spinal injuries in the elderly and clinical guidelines may not be applicable to this patient group. Classification and surgical treatment are different from younger adult counterparts as the elderly osteoporotic spine behaves differently biomechanically. There is a high incidence of respiratory complications both for surgically and conservatively managed groups. Older age generally is associated with a higher complication rate including mortality.

Development and validation of a machine learning model to predict imminent new vertebral fractures after vertebral augmentation

BACKGROUND

Accurately predicting the occurrence of imminent new vertebral fractures (NVFs) in patients with osteoporotic vertebral compression fractures (OVCFs) undergoing vertebral augmentation (VA) is challenging with yet no effective approach. This study aim to examine a machine learning model based on radiomics signature and clinical factors in predicting imminent new vertebral fractures after vertebral augmentation.

METHODS

A total of 235 eligible patients with OVCFs who underwent VA procedures were recruited from two independent institutions and categorized into three groups, including training set (n = 138), internal validation set (n = 59), and external validation set (n = 38). In the training set, radiomics features were computationally retrieved from L1 or adjacent vertebral body (T12 or L2) on T1-w MRI images, and a radiomics signature was constructed using the least absolute shrinkage and selection operator algorithm (LASSO). Predictive radiomics signature and clinical factors were fitted into two final prediction models using the random survival forest (RSF) algorithm or COX proportional hazard (CPH) analysis. Independent internal and external validation sets were used to validate the prediction models.

RESULTS

The two prediction models were integrated with radiomics signature and intravertebral cleft (IVC). The RSF model with C-indices of 0.763, 0.773, and 0.731 and time-dependent AUC (2 years) of 0.855, 0.907, and 0.839 (p < 0.001 for all) was found to be better predictive than the CPH model in training, internal and external validation sets. The RSF model provided better calibration, larger net benefits (determined by decision curve analysis), and lower prediction error (time-dependent brier score of 0.156, 0.151, and 0.146, respectively) than the CPH model.

CONCLUSIONS

The integrated RSF model showed the potential to predict imminent NVFs following vertebral augmentation, which will aid in postoperative follow-up and treatment.

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics

PURPOSE

Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs).

METHODS

We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features.

RESULTS

For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak.

CONCLUSIONS

The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.

Imaging of low-energy vertebral fractures

Low-energy vertebral fractures pose a diagnostic challenge for the radiologist due to their often-inadvertent nature and often subtle imaging semiology. However, the diagnosis of this type of fractures can be decisive, not only because it allows targeted treatment to prevent complications, but also because of the possibility of diagnosing systemic pathologies such as osteoporosis or metastatic disease. Pharmacological treatment in the first case has been shown to prevent the development of other fractures and complications, while percutaneous treatments and various oncological therapies can be an alternative in the second case. Therefore, it is necessary to know the epidemiology and typical imaging findings of this type of fractures. The objective of this work is to review the imaging diagnosis of low-energy fractures, with special emphasis on the characteristics that should be outlined in the radiological report to guide a specific diagnosis that favours and optimizes the treatment of patients suffering of low energy fractures.

ACR Appropriateness Criteria® Management of Vertebral Compression Fractures: 2022 Update

Vertebral compression fractures (VCFs) can have a variety of etiologies, including trauma, osteoporosis, or neoplastic infiltration. Osteoporosis related fractures are the most common cause of VCFs and have a high prevalence among all postmenopausal women with increasing incidence in similarly aged men. Trauma is the most common etiology in those >50 years of age. However, many cancers, such as breast, prostate, thyroid, and lung, have a propensity to metastasize to bone, which can lead to malignant VCFs. Indeed, the spine is third most common site of metastases after lung and liver. In addition, primary tumors of bone and lymphoproliferative diseases such as lymphoma and multiple myeloma can be the cause of malignant VCFs. Although patient clinical history could help raising suspicion for a particular disorder, the characterization of VCFs is usually referred to diagnostic imaging. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Can Anterior Osteophyte Fractures Be Distinguished From Fracture Mimics in the Subaxial Cervical Spine? A Retrospective Analysis Evaluating Reported Fractures With Clinical Management Correlation

OBJECTIVE

This study aimed to retrospectively distinguish true- from false-positive fractures of anterior subaxial cervical osteophytes, which were reported on noncontrast computed tomography reports, and to correlate the imaging findings with patient symptoms and analyze the downstream impact on management of both true and false positive fractures.

METHODS

A total of 127 patients had computed tomography reports of anterior osteophyte fractures. Radiology reports and imaging studies were evaluated to distinguish true fractures from fracture mimics. We analyzed imaging features including rigid spine (RS), prevertebral soft tissue swelling (PVSTS), and instability. We categorized symptoms and examination findings into 3 groups (0, asymptomatic; 1, neck pain; 2, neurological symptoms). Management was categorized into 3 groups (0, no treatment; 1, external bracing; 2, surgery). Associations between imaging features, fracture classification, clinical symptoms, magnetic resonance imaging utilization, and management were calculated using χ2 with Cramer V test to determine effect size.

RESULTS

Eighty patients had false-positive fractures, and 47 were true positive. There were significant associations between magnetic resonance imaging utilization and fracture classification (P ≤ 0.001), PVSTS (P ≤ 0.005), patient symptoms (P ≤ 0.001), and patient management (P ≤ 0.001). There were significant associations between patient management and fracture classification (P ≤ 0.001), patient symptoms (P ≤ 0.001), PVSTS (P ≤ 0.001), imaging findings of instability (P ≤ 0.001), and RS (P ≤ 0.021). There were significant associations between fracture classification and patient symptoms (P ≤ 0.045), and RS (P ≤ 0.006).

CONCLUSIONS

Subaxial isolated anterior osteophyte fractures fell into 3 major categories. By our methodology, if a suspected fracture was determined to be a fracture mimic in an asymptomatic patient, it was unlikely to be clinically significant. Isolated anterior osteophyte fractures without neurological symptoms or more concerning imaging findings can be treated conservatively. Finally, fractures that demonstrate indirect signs of instability or are associated with RS are more associated with surgical management.

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

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 surgical management trends of osteoporotic vertebral compression fractures: 5-year experience in one institution

Osteoporotic vertebral compression fractures (OVCFs) have gradually become a health threat to elderly individuals. Treatment options are controversial, and many challenges remain. Our study aimed to investigate the management trends of OVCFs at a single institution, covering all cases of OVCFs between January 1, 2016, and December 31, 2020. A total of 938 OVCF patients were reviewed, and OVCFs were most common in patients over 70 years old. The hospital stay, surgery haemorrhage rate and total cost decreased year by year. The number of patients with previous OVCFs varied from 123 in 2016 to 83 in 2020. The average bone mineral density (BMD) of the patients generally decreased year by year. In OVCF treatments, the rate of PV or PK increased from 93.86% in 2016 to 98.98% in 2020, while the rate of PV combined with pedicle fixation decreased from 6.14% in 2012 to 1.12% in 2020. Most patients were treated with bisphosphonates, and only 2 patients were treated with teriparatide. The visual analogue scale scores significantly improved at the final follow-up compared with the preoperative values. The rate of previous fractures was correlated with BMD, while there were no correlations with sex, age, or anti-osteoporosis treatment. In conclusion, the 5-year incidence of OVCFs increased and average patient BMD worsened by year. Although the total cost is continuously decreasing, poor adherence to anti-osteoporosis treatments and the prevention of refracture create more severe challenges.

Prediction of the Acuity of Vertebral Compression Fractures on CT Using Radiologic and Radiomic Features

RATIONALE AND OBJECTIVES

To develop and validate prediction models to differentiate acute and chronic vertebral compression fractures based on radiologic and radiomic features on CT.

MATERIALS AND METHODS

This study included acute and chronic compression fractures in patients who underwent both spine CT and MRI examinations. For each fractured vertebra, three CT findings ([1] cortical disruption, [2] hypoattenuating cleft or sclerotic line, and [3] relative bone marrow attenuation) were assessed by two radiologists. A radiomic score was built from 280 radiomic features extracted from non-contrast-enhanced CT images. Weighted multivariable logistic regression analysis was performed to build a radiologic model based on CT findings and an integrated model combining the radiomic score and CT findings. Model performance was evaluated and compared. Models were externally validated using an independent test cohort.

RESULTS

A total to 238 fractures (159 acute and 79 chronic) in 122 patients and 58 fractures (39 acute and 19 chronic) in 32 patients were included in the training and test cohorts, respectively. The AUC of the radiomic score was 0.95 in the training and 0.93 in the test cohorts. The AUC of the radiologic model was 0.89 in the training and 0.83 in the test cohorts. The discriminatory performance of the integrated model was significantly higher than the radiologic model in both the training (AUC, 0.97; p<0.01) and the test (AUC, 0.95; p=0.01) cohorts.

CONCLUSION

Combining radiomics with radiologic findings significantly improved the performance of CT in determining the acuity of vertebral compression fractures.

Efficacy of Recombinant Human Parathyroid Hormone versus Vertebral Augmentation Procedure on Patients with Acute Osteoporotic Vertebral Compression Fracture

OBJECTIVE

Although widely used in clinical practice, vertebral augmentation procedure (VAP) for osteoporotic vertebral compression fracture (OVCF) is not supported. Recently, the effect of recombinant human parathyroid hormone (1-34) (rhPTH) has been paid great attention for its efficacy in anti-osteoporosis and bone union. This study aims to explore the outcome of rhPTH on acute OVCF and compare it with VAP to clarify its therapeutic advantages.

METHODS

The retrospective study comprised 71 acute OVCF patients from January 2015 to March 2020: 22 received rhPTH treatment (rhPTH group) and 49 underwent VAP (VAP group). The rhPTH group was 15 women and seven men with an average of 76.18 years, and the VAP group were 35 women and 14 men with an average of 73.63 years. The thoracic/lumbar vertebrae were 14/8 in the rhPTH group and 29/20 in the VAP group. The average follow-up period was 14.05 months in the rhPTH group and 13.82 months in the VAP group. The two groups were assessed regarding the visual analog score (VAS), Oswestry Disability Index (ODI), OVCF bone union, bone mineral density (BMD), kyphotic angle (KA), anterior and posterior border height (ABH and PBH, respectively), adverse events and the health-related quality of life assessed by short form-36 health survey scores (SF-36). Categorical variables were analyzed by chi-square test and continuous variables between groups were analyzed by independent samples t-test or Mann-Whitney U test according to the normality.

RESULTS

During the follow-up, the VAS was significantly lower in the rhPTH group than in the VAP group at month 3 (0.39 ± 0.6 vs 0.68 ± 0.651) (p = 0.047), month 6 (0.45 ± 0.60 vs 2.18 ± 1.22) (p < 0.001), and month 12 (0.45 ± 0.60 vs 2.43 ± 1.49) (p < 0.001). At month 12, the ODI was significantly lower in the rhPTH group (18.59 ± 3.33%) than in the VAP group (28.93 ± 16.71%) (p < 0.001). Bone bridge was detected on sagittal computed tomography images of all fractured vertebrae in the rhPTH group. The BMD was significantly higher in the rhPTH group (87.66 ± 5.91 Hounsfield units [HU]) than in the VAP group (68.15 ± 11.32HU) (p < 0.001). There were no significant differences in the changes in KA, ABH, and PBH between groups (all p > 0.05). The incidence of new OVCF was significantly lower in the rhPTH group than in the VAP group (p = 0.042). All scores of SF-36 were significantly higher in the rhPTH group than in the VAP group (all p < 0.05).

CONCLUSION

In acute OVCF patients, rhPTH was better than VAP in increasing spinal BMD to promote OVCF healing, reduce new OVCF, and improve back pain, physical ability, and health-related quality of life.

Strategies in the Management of Osteoporotic Kummell's Disease

Introduction

Kummell disease is a condition characterized by severe pain, progressing kyphosis with or without neurological deficit following a trivial trauma in the old age osteoporotic population. It is an osteoporotic vertebral fracture due to avascular necrosis of the vertebra, having an asymptomatic period initially followed by progressive pain, kyphosis, and neurologic deficit. Although various management options are available for Kummell's disease, a dilemma occurs in selecting an optimal modality in each case.

Case Report

A 65-year-old female presented with complaints of low back pain for 4 weeks. She developed progressive weakness and bowel bladder disturbance. Radiographs showed a D12 vertebral compression fracture with an intravertebral vacuum cleft sign. Magnetic resonance imaging showed intravertebral fluid and significant compression of the cord. We performed posterior decompression, stabilization, and transpedicular bone grafting at the D12 level. Histopathology confirmed Kummell's disease. The patient recovered with restored power and bladder control and resumed independent ambulation.

Conclusion

Osteoporotic compression fractures are more prone to pseudoarthrosis due to poor vascular and mechanical support, they need adequate immobilization and bracing. Transpedicular bone grafting for kummels disease seems to be a good surgical option due to its short operating time, less bleeding, less invasive approach, and early recovery. However, a treatment-oriented classification is needed to treat this clinical entity on a case-by-case basis.

Spinal Tumors: Diagnosis and Treatment

Tumors that present in or around the spine can be challenging to diagnose and treat. A proper workup involves a complete history and physical examination, appropriate staging studies, appropriate imaging of the entire spine, and a tissue biopsy. The biopsy defines the lesion and guides treatment, but in some rare instances, rapid neurological decline may lead to urgent or emergent surgery before it can be analyzed. "Enneking-appropriate" margins should remain the goal for primary tumors while adequate debulking/separation/stabilization are often the goals in metastatic disease. Primary tumors of the spine are rare and often complex tumors to operate on-achieving Enneking-appropriate margins provides the greatest chance of survival while decreasing the chance of local recurrence. Metastatic tumors of the spine are increasingly more common, and timing of surgery must be considered within the greater framework of the patient and the patient's disease, deficits, stability, and other treatments available. The specific tumor type will dictate what other multidisciplinary approaches are available, allowing for chemotherapy and radiation as needed.

Utility of dual energy computed tomography in the evaluation of infiltrative skeletal lesions and metastasis: a literature review

Computed tomography (CT) is routinely used to diagnose and evaluate metastatic lesions in oncology. CT alone suffers from lack of sensitivity, especially for skeletal lesions in the bone marrow and lesions that have similar attenuation profiles to surrounding bone. Magnetic resonance imaging and nuclear medicine imaging remain the gold standard in evaluating skeletal lesions. However, compared to CT, these modalities are not as widely available or suitable for all patients. Dual energy computed tomography (DECT) exploits variations in linear attenuation coefficient of materials at different photon energy levels to reconstruct images based on material composition. DECT in musculoskeletal imaging is used in the imaging of crystal arthropathy and detecting subtle fractures, but it is not broadly utilized in evaluating infiltrative skeletal lesions. Malignant skeletal lesions have different tissue and molecular compositions compared to normal bone. DECT may exploit these physical differences to delineate infiltrative skeletal lesions from surrounding bone better than conventional monoenergetic CT. Studies so far have examined the utility of DECT in evaluating skeletal metastases, multiple myeloma lesions, pathologic fractures, and performing image-guided biopsies with promising results. These studies were mostly retrospective analyses and case reports containing small samples sizes. As DECT becomes more widely used clinically and more scientific studies evaluating the performance of DECT are published, DECT may eventually become an important modality in the work-up of infiltrative skeletal lesions. It may even challenge MRI and nuclear medicine because of relatively faster scanning times and ease of access.

Tumors of the Spine: When Can Biopsy Be Avoided?

Regarding osseous tumors of the spine, characteristic morphology is encountered in hemangioma of the vertebral body, osteoid osteoma (OO), osteochondroma, Paget's disease, and bone islands. In these cases, radiologic imaging can make a specific diagnosis and thereby avoid biopsy, especially when the radiologist has chosen the correct imaging modality to establish the diagnosis, such as thin-slice computed tomography in suspected OO. A benign lesion is suggested by a high amount of fat within the lesion, the lack of uptake of the contrast agent, and a homogeneous aspect without solid parts in a cystic tumor. Suspicion of malignancy should be raised in spinal lesions with a heterogeneous disordered matrix, distinct signal decrease in T1-weighted magnetic resonance imaging, blurred border, perilesional edema, cortex erosion, and a large soft tissue component. Biopsy is mandatory in presumed malignancy, such as any Lodwick grade II or III osteolytic lesion in the vertebral column. The radiologist plays a crucial role in determining the clinical pathway by choosing the imaging approach wisely, by narrowing the differential diagnosis list, and, when characteristic morphology is encountered, by avoiding unnecessary biopsies.

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

Diagnosis and Management of Vertebral Compression Fracture

Vertebral compression fractures are the most common complication of osteoporosis, with 700,000 cases reported every year in the United States. Vertebral compression fractures typically present with abrupt-onset low back pain with or without a history of trauma, although more than two-thirds are detected incidentally. Diagnosis is confirmed using plain radiographs, while computed tomography and magnetic resonance imaging may be required to evaluate for a malignant cause or if there are neurological deficits on examination. Magnetic resonance imaging is also the modality of choice to determine if the fracture is acute vs chronic in nature. Patients can be managed with a combination of nonsurgical modalities including medications, bracing, and physical therapy, although when indicated, kyphoplasty or vertebroplasty may be considered to provide symptom relief.

The value of magnetic resonance imaging and computed tomography in the study of spinal disorders

Computed tomography (CT) and magnetic resonance imaging (MRI) have replaced conventional radiography in the study of many spinal conditions, it is essential to know when these techniques are indicated instead of or as complementary tests to radiography, which findings can be expected in different clinical settings, and their significance in the diagnosis of different spinal conditions. Proper use of CT and MRI in spinal disorders may facilitate diagnosis and management of spinal conditions. An adequate clinical approach, a good understanding of the pathological manifestations demonstrated by these imaging techniques and a comprehensive report based on a universally accepted nomenclature represent the indispensable tools to improve the diagnostic approach and the decision-making process in patients with spinal pain. Several guidelines are available to assist clinicians in ordering appropriate imaging techniques to achieve an accurate diagnosis and to ensure appropriate medical care that meets the efficacy and safety needs of patients. This article reviews the clinical indications of CT and MRI in different pathologic conditions affecting the spine, including congenital, traumatic, degenerative, inflammatory, infectious and tumor disorders, as well as their main imaging features. It is intended to be a pictorial guide to clinicians involved in the diagnosis and treatment of spinal disorders.

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.

Automated Differentiation Between Osteoporotic Vertebral Fracture and Malignant Vertebral Fracture on MRI Using a Deep Convolutional Neural Network

STUDY DESIGN

Retrospective study of magnetic resonance imaging (MRI).

OBJECTIVES

To assess the ability of a convolutional neural network (CNN) model to differentiate osteoporotic vertebral fractures (OVFs) and malignant vertebral compression fractures (MVFs) using short-TI inversion recovery (STIR) and T1-weighted images (T1WI) and to compare it to the performance of three spine surgeons.

SUMMARY OF BACKGROUND DATA

Differentiating between OVFs and MVFs is crucial for appropriate clinical staging and treatment planning. However, an accurate diagnosis is sometimes difficult. Recently, CNN modeling-an artificial intelligence technique-has gained popularity in the radiology field.

METHODS

We enrolled 50 patients with OVFs and 47 patients with MVFs who underwent thoracolumbar MRI. Sagittal STIR images and sagittal T1WI were used to train and validate the CNN models. To assess the performance of the CNN, the receiver operating characteristic curve was plotted and the area under the curve was calculated. We also compared the accuracy, sensitivity, and specificity of the diagnosis made by the CNN and three spine surgeons.

RESULTS

The area under the curve of receiver operating characteristic curves of the CNN based on STIR images and T1WI were 0.967 and 0.984, respectively. The CNN model based on STIR images showed a performance of 93.8% accuracy, 92.5% sensitivity, and 94.9% specificity. On the other hand, the CNN model based on T1WI showed a performance of 96.4% accuracy, 98.1% sensitivity, and 94.9% specificity. The accuracy and specificity of the CNN using both STIR and T1WI were statistically equal to or better than that of three spine surgeons. There were no significant differences in sensitivity based on both STIR images and T1WI between the CNN and spine surgeons.

CONCLUSION

We successfully differentiated OVFs and MVFs based on MRI with high accuracy using the CNN model, which was statistically equal or superior to that of the spine surgeons.Level of Evidence: 4.

Osteoporotic vertebral fractures: radiologic diagnosis, clinical and radiologic factors affecting surgical decision making: WFNS Spine Committee Recommendations

With the varied literature on osteoporotic vertebral fracture that may predispose to diagnostic and management dilemma, it is timely to evaluate and streamline the evidence. The aim of this review is to create recommendations on osteoporotic vertebral fractures regarding radiologic diagnosis, and clinical and radiological factors affecting surgical decision making. A computerized literature search was done using PubMed, Google scholar and Cochrane Database of Systematic Reviews from 2010 to 2020. For radiologic diagnosis, the keywords "osteoporotic vertebral fractures" and "radiologic diagnosis" were used yielding 394 articles (19 relevant articles). For clinical and radiological factors affecting surgical decision making, the keywords "osteoporotic vertebral fractures", "radiologic diagnosis", and "surgery" were used yielding 568 articles (25 relevant articles). All pertinent data were reviewed, and consensus statements were obtained in two virtual separate consensus meetings of the World Federation of Neurosurgical Societies (WFNS) Spine committee. The statements were voted and yielded positive or negative consensus using the Delphi method. This review summarizes the WFNS Spine Committee recommendations on the radiologic diagnosis, and clinical and radiological factors affecting surgical decision making of osteoporotic vertebral fractures.

Percutaneous Vertebroplasty: Efficacy of Unipedicular Vertebroplasty as Compared to Bipedicular Vertebroplasty

Introduction  Percutaneous vertebroplasty has been used for treatment of intractable painful fractures of vertebral bodies. With the help of refined procedures and standard techniques, the interventional radiologist can now offer help to orthopedics and neurosurgeons in these cases, which include treatment of vertebral compression fracture. Vertebroplasty is aimed at reducing the pain induced by collapse. Vertebroplasty is the standard mode of treatment for vertebral collapse, and in our study, bipedicular vertebroplasty was compared with unipedicular approach as bipedicular vertebroplasty is the routinely used approach.

Aim  To compare efficacy of unipedicular percutaneous vertebroplasty with that of bipedicular percutaneous vertebroplasty.

Material and Methods  A total of 52 vertebroplasties were done over a period of 2 years. Out of 52 patients, 28 patients underwent unipedicular vertebroplasty and 24 patients underwent bipedicular vertebroplasty. Visual analogue scale (VAS) scores were used to assess the pain prior to vertebroplasty and after vertebroplasty. Efficacy of the two procedures were assessed by comparing VAS scores.

Results  There was no statistically significant difference observed in the preprocedure and postprocedure VAS scores ( p -value < 0.0001, < 0.0001, respectively). The mean procedure time was lesser in unipedicular vertebroplasty (41.9 ± 3.90) than bipedicular vertebroplasty (54.5 ± 3.4).

Conclusion  Unipedicular vertebroplasty is as effective as bipedicular vertebroplasty, as there is insignificant difference in postprocedure VAS scores between the unipedicular and bipedicular vertebroplasty.

Myeloma Response Assessment and Diagnosis System (MY-RADS): strategies for practice implementation

Structured reporting systems have been developed for many organ systems and disease processes beginning with BI-RADS in 1993. Numerous reports indicate that referring health care providers prefer structured reports. Reducing variability of reports from one radiologist to another helps referring physician and patient confidence. Changing radiologists practice habits from completely free text to structured reports can be met with some resistance, but most radiologists quickly find that structured reports make their job easier. Whole-body MR studies are recommended as first-line imaging, by the International Myeloma Working Group (IMWG), for all patients with suspected diagnosis of asymptomatic myeloma and/or initial diagnosis of solitary plasmacytoma. Whole-body MR imaging (WBMRI) has been shown to have equal or greater sensitivity and specificity compared to PET/CT for detection of bone marrow involvement. Changing to WBMRI from other imaging modalities can be difficult for referring providers. Patient acceptance is high. MY-RADS is for myeloma patients who have WBMRI studies done. The intent of the system is to promote uniformity in MR imaging acquisition, diagnostic criteria, and response assessment and to diminish differences in the subsequent interpretation and reporting. A secondary benefit is a report template that provides a guide for interpretation for radiologists who may not have previously dictated these difficult studies. The characterization of bone marrow abnormalities in myeloma patients usually is fairly straightforward. To date, there is no standardized scoring or risk stratification of abnormalities nor is there an imaging atlas of abnormalities.

Associations of incidental vertebral fractures and longitudinal changes of MR-based proton density fat fraction and T2* measurements of vertebral bone marrow

BACKGROUND

Quantitative magnetic resonance imaging (MRI) techniques such as chemical shift encoding-based water-fat separation techniques (CSE-MRI) are increasingly applied as noninvasive biomarkers to assess the biochemical composition of vertebrae. This study aims to investigate the longitudinal change of proton density fat fraction (PDFF) and T2* derived from CSE-MRI of the thoracolumbar vertebral bone marrow in patients that develop incidental vertebral compression fractures (VCFs), and whether PDFF and T2* enable the prediction of an incidental VCF.

METHODS

In this study we included 48 patients with CT-derived bone mineral density (BMD) measurements at baseline. Patients that presented an incidental VCF at follow up (N=12, mean age 70.5 ± 7.4 years, 5 female) were compared to controls without incidental VCF at follow up (N=36, mean age 71.1 ± 8.6 years, 15 females). All patients underwent 3T MRI, containing a significant part of the thoracolumbar spine (Th11-L4), at baseline, 6-month and 12 month follow up, including a gradient echo sequence for chemical shift encoding-based water-fat separation, from which PDFF and T2* maps were obtained. Associations between changes in PDFF, T2* and BMD measurements over 12 months and the group (incidental VCF vs. no VCF) were assessed using multivariable regression models. Mixed-effect regression models were used to test if there is a difference in the rate of change in PDFF, T2* and BMD between patients with and without incidental VCF.

RESULTS

Prior to the occurrence of an incidental VCF, PDFF in vertebrae increased in the VCF group (Δ_PDFF=6.3 ± 3.1%) and was significantly higher than the change of PDFF in the group without VCF (Δ_PDFF=2.1 ± 2.5%, P=0.03). There was no significant change in T2* (Δ_T2*=1.7 ± 1.1ms vs. Δ_T2*=1.1 ± 1.3ms, P=0.31) and BMD (Δ_BMD=-1.2 ± 11.3mg/cm³ vs. Δ_BMD=-11.4 ± 24.1mg/cm³, P= 0.37) between the two groups over 12 months. At baseline, no significant differences were detected in the average PDFF, T2* and BMD of all measured vertebrae (Th11-L4) between the VCF group and the group without VCF (P=0.66, P=0.35 and P= 0.21, respectively). When assessing the differences in rates of change, there was a significant change in slope for PDFF (2.32 per 6 months, 95% confidence interval (CI) 0.31-4.32; P=0.03) but not for T2* (0.02 per 6 months, CI -0.98-0.95; P=0.90) or BMD (-4.84 per 6 months, CI -23.4-13.7; P=0.60).

CONCLUSIONS

In our study population, the average change of PDFF over 12 months is significantly higher in patients that develop incidental fractures at 12-month follow up compared to patients without incidental VCF, while T2* and BMD show no significant changes prior to the occurrence of the incidental vertebral fractures. Therefore, a longitudinal increase in bone marrow PDFF may be predictive for vertebral compression fractures.

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.

Whole-Body Low-Dose Multidetector-Row CT in Multiple Myeloma: Guidance in Performing, Observing, and Interpreting the Imaging Findings

Multiple myeloma is a hematological malignancy of plasma cells usually detected due to various bone abnormalities on imaging and rare extraosseous abnormalities. The traditional approach for disease detection was based on plain radiographs, showing typical lytic lesions. Still, this technique has many limitations in terms of diagnosis and assessment of response to treatment. The new approach to assess osteolytic lesions in patients newly diagnosed with multiple myeloma is based on total-body low-dose CT. The purpose of this paper is to suggest a guide for radiologists in performing and evaluating a total-body low-dose CT in patients with multiple myeloma, both newly-diagnosed and in follow-up (pre and post treatment).

PET/MRI is useful for early detection of pelvic insufficiency fractures after radiotherapy for cervical cancer

Radiotherapy (RT) is used to manage cervical cancer, and pelvic insufficiency fracture (PIF) is known as a late complication of RT. The present study identified risk factors for PIF after radiotherapy for cervical cancer, and investigated its incidence rate. It also considered the usefulness of positron emission tomography/magnetic resonance imaging (PET/MRI) in PIF diagnosis. A total of 149 patients with cervical cancer who received definitive or adjuvant RT with/without concurrent chemotherapy between January 2013 and December 2018 were investigated in the present study and followed up for more than one month after RT at Kobe University Hospital. The median follow-up period was 32 months (range, 1-87 months), and the median age of all patients was 66 years (age range, 34-90 years). Computed tomography (CT), MRI, PET/CT or PET/MRI were used for image examination. Among the 149 patients, 31 (20.8%) developed PIF. The median age of these patients was 69 years (age range, 44-87 years). Univariate analysis using the log-rank test demonstrated that age (≥60 years) was significantly associated with PIF. The median maximum standardized uptake value of PIF sites on PET/CT was 4.32 (range, 3.04-4.81), and that on PET/MRI was 3.97 (range, 1.21-5.96) (P=0.162). Notably, the detection time of PIF by PET/MRI was significantly earlier compared with PET/CT (P<0.05). The incidence of PIF after RT for cervical cancer was 20.8%, and age was significantly associated with risk factors for such fractures. Taken together, these results suggest that PET/MRI, which offers the advantage of decreased radiation exposure to the patient, is useful for diagnosing PIF and can detect it earlier than PET/CT imaging.