Image-Guided Minimally Invasive Treatment Options for Degenerative Lumbar Spine Disease: A Practical Overview of Current Possibilities.

Lumbar back pain is one of the main causes of disability around the world. Most patients will complain of back pain at least once in their lifetime. The degenerative spine is considered the main cause and is extremely common in the elderly population. Consequently, treatment-related costs are a major burden to the healthcare system in developed and undeveloped countries. After the failure of conservative treatments or to avoid daily chronic drug intake, invasive treatments should be suggested. In a world where many patients reject surgery and prefer minimally invasive procedures, interventional radiology is pivotal in pain management and could represent a bridge between medical therapy and surgical treatment. We herein report the different image-guided procedures that can be used to manage degenerative spine-related low back pain. Particularly, we will focus on indications, different techniques, and treatment outcomes reported in the literature. This literature review focuses on the different minimally invasive percutaneous treatments currently available, underlining the central role of radiologists having the capability to use high-end imaging technology for diagnosis and subsequent treatment, allowing a global approach, reducing unnecessary surgeries and prolonged pain-reliever drug intake with their consequent related complications, improving patients' quality of life, and reducing the economic burden.

Imaging of Spondylodiscitis: A Comprehensive Updated Review-Multimodality Imaging Findings, Differential Diagnosis, and Specific Microorganisms Detection.

Spondylodiscitis is defined by infectious conditions involving the vertebral column. The incidence of the disease has constantly increased over the last decades. Imaging plays a key role in each phase of the disease. Indeed, radiological tools are fundamental in (i) the initial diagnostic recognition of spondylodiscitis, (ii) the differentiation against inflammatory, degenerative, or calcific etiologies, (iii) the disease staging, as well as (iv) to provide clues to orient towards the microorganisms involved. This latter aim can be achieved with a mini-invasive procedure (e.g., CT-guided biopsy) or can be non-invasively supposed by the analysis of the CT, positron emission tomography (PET) CT, or MRI features displayed. Hence, this comprehensive review aims to summarize all the multimodality imaging features of spondylodiscitis. This, with the goal of serving as a reference for Physicians (infectious disease specialists, spine surgeons, radiologists) involved in the care of these patients. Nonetheless, this review article may offer starting points for future research articles.

Imaging of lower extremity infections: predisposing conditions, atypical infections, mimics, and differentiating features.

Imaging evaluation for lower extremity infections can be complicated, especially in the setting of underlying conditions and with atypical infections. Predisposing conditions are discussed, including diabetes mellitus, peripheral arterial disease, neuropathic arthropathy, and intravenous drug abuse, as well as differentiating features of infectious versus non-infectious disease. Atypical infections such as viral, mycobacterial, fungal, and parasitic infections and their imaging features are also reviewed. Potential mimics of lower extremity infection including chronic nonbacterial osteomyelitis, foreign body granuloma, gout, inflammatory arthropathies, lymphedema, and Morel-Lavallée lesions, and their differentiating features are also explored.

Lower extremity infections: Essential anatomy and multimodality imaging findings.

In modern practice, imaging plays an integral role in the diagnosis, evaluation of extent, and treatment planning for lower extremity infections. This review will illustrate the relevant compartment anatomy of the lower extremities and highlight the role of plain radiographs, CT, US, MRI, and nuclear medicine in the diagnostic workup. The imaging features of cellulitis, abscess and phlegmon, necrotizing soft tissue infection, pyomyositis, infectious tenosynovitis, septic arthritis, and osteomyelitis are reviewed. Differentiating features from noninfectious causes of swelling and edema are discussed.

A new comprehensive MRI classification and grading system for lumbosacral central and lateral stenosis: clinical application and comparison with previous systems.

PURPOSE: The purpose of our study was to provide a novel schematized and comprehensive classification of causes and severity grading system for lumbosacral stenosis. MATERIALS AND METHODS: The MRI system proposed consisted of a severity grading scale for central and lateral (recess and foramen) stenosis, together with a schematized indication of the main causes of the disease (disc, arthritis, epidural lipomatosis, and their combinations). The system was applied to a cohort of patients from a single Institution in the last 2-years. Two radiologists evaluated all the MRIs to determine intra- and inter-observer reliability according to Cohen Kappa (Kc, for non-ordered categorical variables) and weighted Kappa (Kw, for ordered variables). Two orthopaedic surgeons clinically evaluated all patients and provided a schematic grading system with a central and lateral stenosis clinical score (CS-CS and LS-CS). Associations between ordinals were tested with chi-square test and measured with the Goodman and Kruskal's gamma index (Gi, with 95% confidence interval [95% CI]). Lastly, the most used previous MRI systems were applied, and their performances were compared to the new system proposed. RESULTS: One hundred and twelve patients were included (55 females-mean age 63.3 ± 10.7 years). An almost perfect intra-observer agreement for the assessment of central stenosis, foramen stenosis, and lateral recess stenosis was found (Kw = 0.929, 0.928, and 0.924, respectively). The inter-observer agreement was almost perfect for central stenosis and foramen stenosis and substantial for lateral recess stenosis (Kw = 0.863, 0.834, and 0.633, respectively). Whatever the aetiologies involved in central and lateral stenosis, the intra-observer agreement was perfect (all Kc = 1), whereas the inter-observer agreements were almost perfect for arthritis (Kc = 0.838) and lipomatosis (Kc = 0.955) and substantial for disc (Kc = 0.691) regarding central stenosis. The inter-observer agreement for the causes of lateral stenosis was lower and variable, ranging from perfect (lipomatosis) to fair (disc, Kc = 0.224). The grading system revealed a strong association with CS-CS for both readers, with GI = 0.671 (95% CI 0.535-0.807) and 0.603 (95% CI = 0.457-0.749), respectively. The association with MRI grading and LS-CS was moderate for foraminal stenosis and for the concomitant presence of foraminal and lateral recess stenosis, with Gi = 0.337 (95% CI 0.121-0.554) and Gi = 0.299 (95% CI 0.098-0.500), respectively. A weak association was found between lateral recess grading alone and LS-CS with Gi = 0.102 (95% CI 0.193-0.397). The new grading systems showed higher Gi for associations with clinical symptoms, compared with previous ones, both for CS-CS and LS-CS. CONCLUSIONS: A standardized visual grading system for lumbar spinal stenosis that takes into account all of the major contributing factors-including disc, arthritis, and lipomatosis, for the central canal, lateral recess, and neural foramina could be a useful and practical tool for defining the stenosis, lowering inter-observer variability, and directing the various treatment options.

Spatial assessments in texture analysis: what the radiologist needs to know.

To date, studies investigating radiomics-based predictive models have tended to err on the side of data-driven or exploratory analysis of many thousands of extracted features. In particular, spatial assessments of texture have proven to be especially adept at assessing for features of intratumoral heterogeneity in oncologic imaging, which likewise may correspond with tumor biology and behavior. These spatial assessments can be generally classified as spatial filters, which detect areas of rapid change within the grayscale in order to enhance edges and/or textures within an image, or neighborhood-based methods, which quantify gray-level differences of neighboring pixels/voxels within a set distance. Given the high dimensionality of radiomics datasets, data dimensionality reduction methods have been proposed in an attempt to optimize model performance in machine learning studies; however, it should be noted that these approaches should only be applied to training data in order to avoid information leakage and model overfitting. While area under the curve of the receiver operating characteristic is perhaps the most commonly reported assessment of model performance, it is prone to overestimation when output classifications are unbalanced. In such cases, confusion matrices may be additionally reported, whereby diagnostic cut points for model predicted probability may hold more clinical significance to clinical colleagues with respect to related forms of diagnostic testing.

Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.

Introduction: Image segmentation is an important process for quantifying characteristics of malignant bone lesions, but this task is challenging and laborious for radiologists. Deep learning has shown promise in automating image segmentation in radiology, including for malignant bone lesions. The purpose of this review is to investigate deep learning-based image segmentation methods for malignant bone lesions on Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron-Emission Tomography/CT (PET/CT). Method: The literature search of deep learning-based image segmentation of malignant bony lesions on CT and MRI was conducted in PubMed, Embase, Web of Science, and Scopus electronic databases following the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). A total of 41 original articles published between February 2017 and March 2023 were included in the review. Results: The majority of papers studied MRI, followed by CT, PET/CT, and PET/MRI. There was relatively even distribution of papers studying primary vs. secondary malignancies, as well as utilizing 3-dimensional vs. 2-dimensional data. Many papers utilize custom built models as a modification or variation of U-Net. The most common metric for evaluation was the dice similarity coefficient (DSC). Most models achieved a DSC above 0.6, with medians for all imaging modalities between 0.85-0.9. Discussion: Deep learning methods show promising ability to segment malignant osseous lesions on CT, MRI, and PET/CT. Some strategies which are commonly applied to help improve performance include data augmentation, utilization of large public datasets, preprocessing including denoising and cropping, and U-Net architecture modification. Future directions include overcoming dataset and annotation homogeneity and generalizing for clinical applicability.

Imaging Diagnosis and Management of Carpal Trauma and Instability-An Illustrated Guide.

Understanding the subtle signs of carpal instability and other unique injury patterns in the wrist is a critical skill for radiologists. Proper patient management and outcomes are directly dependent on the accurate interpretation of wrist imaging studies. This review will provide a detailed overview of typical imaging features of carpal trauma and instability, management, and complications, using multimodality imaging and original medical illustrations. A detailed overview of the osseous, ligamentous, arterial anatomy of the wrist, arcs of Gilula, and zones of vulnerability will be provided. Carpal fractures, dislocations, special radiographic views, and imaging pearls will be discussed. Instability patterns and the myriad of associate abbreviations (CID, CIND, CIC, CIA, VISI, DISI, SLD, LTD, MCI, SLAC, SNAC) will be clarified. Expected outcomes, potential complications, and management will be reviewed.

Ultrasound Fusion: Applications in Musculoskeletal Imaging.

Ultrasound fusion is an established technique that pairs real time B-scan ultrasound (US) with other forms of cross-sectional imaging, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Each of these imaging modalities has distinct advantages. CT provides superior anatomic resolution, with improved imaging of bone and calcified structures; MRI has superior contrast resolution; and PET provides physiologic information, identifying processes that are metabolically active (i.e., tumor, inflammatory conditions). However, these modalities are static. A key highlight of ultrasound is its capability of dynamic, real-time scanning. The ability to pair CT, MRI or PET with ultrasound can have significant advantages, both in diagnostic evaluation and when performing difficult or challenging image-guided interventions. Percutaneous interventions using ultrasound fusion have been described in the abdominal imaging literature; however, there have been very few musculoskeletal applications detailed in the literature. The purpose of this article is to review the basic concepts of real-time ultrasound fusion, and to detail, through the use of multiple case examples, its potential use as a safe and effective method for performing image-guided musculoskeletal interventions.

Predicting Soft Tissue Sarcoma Response to Neoadjuvant Chemotherapy Using an MRI-Based Delta-Radiomics Approach.

OBJECTIVES: To evaluate the performance of machine learning-augmented MRI-based radiomics models for predicting response to neoadjuvant chemotherapy (NAC) in soft tissue sarcomas. METHODS: Forty-four subjects were identified retrospectively from patients who received NAC at our institution for pathologically proven soft tissue sarcomas. Only subjects who had both a baseline MRI prior to initiating chemotherapy and a post-treatment scan at least 2 months after initiating chemotherapy and prior to surgical resection were included. 3D ROIs were used to delineate whole-tumor volumes on pre- and post-treatment scans, from which 1708 radiomics features were extracted. Delta-radiomics features were calculated by subtraction of baseline from post-treatment values and used to distinguish treatment response through univariate analyses as well as machine learning-augmented radiomics analyses. RESULTS: Though only 4.74% of variables overall reached significance at p ≤ 0.05 in univariate analyses, Laws Texture Energy (LTE)-derived metrics represented 46.04% of all such features reaching statistical significance. ROC analyses similarly failed to predict NAC response, with AUCs of 0.40 (95% CI 0.22-0.58) and 0.44 (95% CI 0.26-0.62) for RF and AdaBoost, respectively. CONCLUSION: Overall, while our result was not able to separate NAC responders from non-responders, our analyses did identify a subset of LTE-derived metrics that show promise for further investigations. Future studies will likely benefit from larger sample size constructions so as to avoid the need for data filtering and feature selection techniques, which have the potential to significantly bias the machine learning procedures.

Role of Imaging in Initial Prognostication of Locally Advanced Soft Tissue Sarcomas.

BACKGROUND: Although imaging is central in the initial staging of patients with soft tissue sarcomas (STS), it remains underused and few radiological features are currently used in practice for prognostication and to help guide the best therapeutic strategy. Yet, several prognostic qualitative and quantitative characteristics from magnetic resonance imaging (MRI) and positron emission tomography (PET) have been identified over these last decades. OBJECTIVE: After an overview of the current validated prognostic features based on baseline imaging and their integration into prognostic tools, such as nomograms used by clinicians, the aim of this review is to summarize more complex and innovative MRI, PET, and radiomics features, and to highlight their role to predict indirectly (through histologic grade) or directly the patients' outcomes.

Imaging of Musculoskeletal Soft-Tissue Infections in Clinical Practice: A Comprehensive Updated Review.

Musculoskeletal soft-tissue infections include a wide range of clinical conditions that are commonly encountered in both emergency departments and non-emergency clinical settings. Since clinical signs, symptoms, and even laboratory tests can be unremarkable or non-specific, imaging plays a key role in many cases. MRI is considered the most comprehensive and sensitive imaging tool available for the assessment of musculoskeletal infections. Ultrasound is a fundamental tool, especially for the evaluation of superficially located diseases and for US-guided interventional procedures, such as biopsy, needle-aspiration, and drainage. Conventional radiographs can be very helpful, especially for the detection of foreign bodies and in cases of infections with delayed diagnosis displaying bone involvement. This review article aims to provide a comprehensive overview of the radiological tools available and the imaging features of the most common musculoskeletal soft-tissue infections, including cellulitis, necrotizing and non-necrotizing fasciitis, foreign bodies, abscess, pyomyositis, infectious tenosynovitis, and bursitis.

Amyloid Deposition: An Unusual Case of Deep Gluteal Syndrome and Sciatic Nerve Compression: A Case Report.

CASE: Soft-tissue amyloidomas are exceedingly rare, with only a few cases reported in the literature. There are no reports of sciatic nerve compression secondary to a soft-tissue amyloidoma. We report a unique case of a 71-year-old man with an incidentally found amyloidoma who was initially believed to have deep gluteal syndrome. He had a favorable outcome after surgical decompression. CONCLUSION: For patients who do not have classic examination and electromyography/nerve conduction findings of piriformis syndrome, providers should explore other etiologies of peripheral nerve compression including soft-tissue amyloidoma.

Whole-tumor 3D volumetric MRI-based radiomics approach for distinguishing between benign and malignant soft tissue tumors.

OBJECTIVES: Our purpose was to differentiate between malignant from benign soft tissue neoplasms using a combination of MRI-based radiomics metrics and machine learning. METHODS: Our retrospective study identified 128 histologically diagnosed benign (n = 36) and malignant (n = 92) soft tissue lesions. 3D ROIs were manually drawn on 1 sequence of interest and co-registered to other sequences obtained during the same study. One thousand seven hundred eight radiomics features were extracted from each ROI. Univariate analyses with supportive ROC analyses were conducted to evaluate the discriminative power of predictive models constructed using Real Adaptive Boosting (Adaboost) and Random Forest (RF) machine learning approaches. RESULTS: Univariate analyses demonstrated that 36.89% of individual radiomics varied significantly between benign and malignant lesions at the p ≤ 0.05 level. Adaboost and RF performed similarly well, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81), respectively, after 10-fold cross-validation. Restricting the machine learning models to only sequences extracted from T2FS and STIR sequences maintained comparable performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84), respectively. CONCLUSION: Machine learning decision classifiers constructed from MRI-based radiomics features show promising ability to preoperatively discriminate between benign and malignant soft tissue masses. Our approach maintains applicability even when the dataset is restricted to T2FS and STIR fluid-sensitive sequences, which may bolster practicality in clinical application scenarios by eliminating the need for complex co-registrations for multisequence analysis. KEY POINTS: • Predictive models constructed from MRI-based radiomics data and machine learning-augmented approaches yielded good discriminative power to correctly classify benign and malignant lesions on preoperative scans, with AUCs of 0.77 (95% CI 0.68-0.85) and 0.72 (95% CI 0.63-0.81) for Real Adaptive Boosting (Adaboost) and Random Forest (RF), respectively. • Restricting the models to only use metrics extracted from T2 fat-saturated (T2FS) and Short-Tau Inversion Recovery (STIR) sequences yielded similar performance, with AUCs of 0.73 (95% CI 0.64-0.82) and 0.75 (95% CI 0.65-0.84) for Adaboost and RF, respectively. • Radiomics-based machine learning decision classifiers constructed from multicentric data more closely mimic the real-world practice environment and warrant additional validation ahead of prospective implementation into clinical workflows.

Evaluation of Knee Cartilage Diurnal, Activity, and BMI-Related Variations Using Quantitative T2 Mapping MRI and Fitbit Activity Tracking.

The aim of this study is to evaluate diurnal variation in knee cartilage 3 Tesla magnetic resonance imaging (MRI) T2 mapping relaxation times, as well as activity- and body mass index (BMI)-dependent variability, using quantitative analysis of T2 values from segmented regions of the weight-bearing articular surfaces of the medial and lateral femoral condyles and tibial plateaus. Ten healthy volunteers' daily activity (steps) were tracked with Fitbit pedometers. Sagittal MRI T2 maps were obtained in the morning and afternoon on days 2 and 3. Mean T2 values were analyzed for variation related to the number of steps taken (activity), time of day (diurnal variation), and BMI using mixed effect model. Significant (albeit small) differences in the medial femoral and medial tibial cartilage regions were identified between morning and afternoon scans (diurnal variation). Daily activity did not result in significant changes and increasing BMI only demonstrated a slight increase in T2 values for the lateral tibial plateau. These findings suggest that it may be necessary to control diurnal variation when using quantitative MRI T2 mapping to assess articular cartilage longitudinally in healthy participants. Further investigation is needed to confirm these findings and determine if they also apply to symptomatic patients.

Illustrative review of knee meniscal tear patterns, repair and replacement options, and imaging evaluation.

This review article aims to reinforce anatomical concepts about meniscal tears while linking associated treatment options. The main teaching points start with the basic meniscal anatomy and key differences between the medial and lateral menisci. Subsequently, various meniscal tear patterns along with their associated history and physical exam findings will be discussed with corresponding illustrations and MR images. Additional discussion will involve the different surgical repair techniques (with arthroscopic correlates), their indications with pertinent imaging findings, imaging related to previous meniscal tear repairs, and novel surgical techniques. Lastly, keys to evaluating for retear with an emphasis on MRI arthrogram findings will be reviewed. While each of these topics is not discussed in totality, the key points of the review article will enforce key concepts and help radiologists evaluate the menisci on imaging.

Imaging Coccygeal Trauma and Coccydynia.

The coccygeal region has complex anatomy, much of which may contribute to or be the cause of coccyx region pain (coccydynia). This anatomy is well depicted at imaging, and management is often dictated by what structures are involved. Coccydynia is a common condition that is known to be difficult to evaluate and treat. However, imaging can aid in determining potential causes of pain to help guide management. Commonly, coccydynia (coccygodynia) occurs after trauma and appears with normal imaging features at static neutral radiography, but dynamic imaging with standing and seated lateral radiography may reveal pathologic coccygeal motion that is predictive of pain. In addition, several findings seen at cross-sectional imaging in patients with coccydynia can point to a source of pain that may be subtle and easily overlooked. Radiology can also offer a role in management of coccygeal region pain with image-guided pain management procedures such as ganglion impar block. In addition to mechanical coccyx pain, a host of other conditions involving the sacrococcygeal region may cause coccydynia, which are well depicted at imaging. These include neoplasm, infection, crystal deposition, and cystic formations such as pilonidal cyst. The authors review a variety of coccydynia causes, their respective imaging features, and common management strategies.©RSNA, 2020.

Horseshoe abscess of the hand with rice bodies secondary to mycobacterium avium intracellulare infection.

A horseshoe abscess is caused by infection that spreads between the flexor tendon sheath of the thumb or little finger through the radial and ulnar bursae through communication between the two and/or the space of Parona. We present a case of an 80-year-old woman with rheumatoid arthritis who presented with 6 months of right hand and wrist soft tissue swelling, initially treated as a rheumatoid arthritis flare. MRI demonstrates the horseshoe abscess and after surgical irrigation and debridement with synovectomy, cultures demonstrated infection with mycobacterium avium intracellulare (MAI). This case demonstrates the importance of MRI in diagnosing and evaluating the extent of hand infections and for considering mycobacterial organisms for appropriate treatment and antibiotic regimen.

Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development.

Soft-tissue sarcomas are a heterogeneous class of tumors that exhibit varying degrees of cellularity and cystic degeneration in response to neoadjuvant chemotherapy. This creates unique challenges in the radiographic assessment of treatment response when relying on conventional markers such as tumor diameter (RECIST criteria). In this case series, we provide a narrative discussion of technique development for whole tumor volume quantitative magnetic resonance imaging (q-MRI), highlighting cases from a small pilot study of 8 patients (9 tumors) pre- and post-neoadjuvant chemotherapy. One of the methods of q-MRI analysis (the "constant-cutoff" technique) was able to predict responders versus non-responders based on percent necrosis and viable tumor volume calculations (p = 0.05), respectively. Our results suggest that q-MRI of whole tumor volume contrast enhancement may have a role in tumor response assessment, although further validation is needed.