Periosteal chondroblastoma of the femoral neck: two cases and a review of the literature.

Chondroblastoma is a rare benign cartilaginous tumor mostly confined to the epiphyses and apophyses. Cases outside the epiphyseal region are exceedingly rare. Extramedullary chondroblastomas are exceptional; to our knowledge, only two cases qualified as "periosteal chondroblastoma" have been described in the literature. We report two cases of metaphyseal periosteal chondroblastoma both located on the inferior surface of the femoral neck. Both cases were paucicellular with an unusual dense sclerotic reaction. The diagnosis of chondroblastoma was supported by the expression of histone 3.3, K36M mutant in tumor cells.

Scoliosis.

Scoliosis is a three-dimensional spinal deformity that can occur at any age. It may be idiopathic or secondary in children, idiopathic and degenerative in adults. Management of patients with scoliosis is multidisciplinary, involving rheumatologists, radiologists, orthopaedic surgeons, and prosthetists. Imaging plays a central role in diagnosis, including the search for secondary causes, follow-up, and preoperative work-up if surgery is required. Evaluating scoliosis involves obtaining frontal and lateral full-spine radiographs in the standing position, with analysis of coronal and sagittal alignment. For adolescent idiopathic scoliosis, imaging follow-up is often required, accomplished using low-dose stereoradiography such as EOS imaging. For adult degenerative scoliosis, the crucial characteristic is rotatory subluxation, also well detected on radiographs. Magnetic resonance imaging is usually more informative than computed tomography for visualizing associated canal and foraminal stenoses. Radiologists must also have a thorough understanding of postoperative features and complications of scoliosis surgery because aspects can be misleading.

Results of an International Survey on Spinal Imaging by the ASNR/ASSR/ESNR/ESSR "Nomenclature 3.0" Working Group.

Our goal was to determine if "Nomenclature 2.0," the classification of lumbar disk pathology consensus, should be updated. We conducted a social media and e-mail-based survey on preferences regarding the use of classification on magnetic resonance spine reporting. Members of the European Society of Neuroradiology, European Society of Musculoskeletal Radiology, American Society of Neuroradiology, and American Society of Spine Radiology received a 15-question online survey between February and March 2022. A total of 600 responses were received from 63 countries. The largest number of responses came from Italy and the United States. We found that 71.28% of respondents used Nomenclature 2.0, Classification of Lumbar Disk Pathology. But classification on stenosis is used less often: 53.94% and 60% of respondents do not use any classification of spinal canal stenosis and foraminal stenosis, respectively. When queried about which part of Nomenclature needs improving, most respondents asked for a Structured Reporting Template (SRT), even though 58.85% of respondents do not currently use any template and 54% routinely use a clinical information questionnaire. These results highlight the importance of an updated Nomenclature 3.0 version that integrates the classifications of lumbar disk disease and spinal canal and foraminal stenosis. Further attention should also be directed toward developing a robust endorsed SRT.

Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives.

This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.

History Page: Leaders in MSK Radiology Maxime Ménard, 1872-1926.

This history page in the series "Leaders in Musculoskeletal Radiology" is dedicated to the memory and achievements of French radiologist Maxime Ménard, whose name is associated with the Shenton-Ménard line that Ménard described independently with the British radiologist Edward Shenton. This landmark line describes the relationship of the proximal femur to the acetabulum in hip dysplasia and fractures of the femoral neck.

Preoperative Arterial Embolization of Musculoskeletal Tumors: A Tertiary Center Experience.

The purpose of this study was to report the effectiveness of preoperative transcatheter arterial embolization (TAE) of musculoskeletal tumors in terms of blood loss and functional outcomes. Patients who underwent preoperative TAE of hypervascular musculoskeletal tumors between January 2018 and December 2021 were retrospectively included. The patients' characteristics, TAE procedure details, degree of post-TAE devascularization, surgical outcomes in terms of red blood cell transfusion and functional results were collected. The degree of devascularization was compared between patients who had peri-operative transfusion and those who did not. Thirty-one patients were included. The 31 TAE procedures led to complete (58%) or near-complete (42%) tumor devascularization. Twenty-two patients (71%) had no blood transfusion during surgery. Nine patients (29%) had a blood transfusion, with a median number of red blood cell packs of three (q1, 2; q3, 4; range: 1-4). Eight patients (27%) had complete improvement of the initial musculoskeletal symptoms at the end of the follow-up, 15 (50%) had partially satisfying improvement, 4 (13%) had partially unsatisfying improvement and 3 (10%) had no improvement. Our study suggests that preoperative TAE of hypervascular musculoskeletal tumors allowed for bloodless surgery in 71% of patients and minimal transfusion needs for the remaining 29%.

A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging.

PURPOSE: The purpose of this study was to develop and evaluate a deep learning model to detect bone marrow edema (BME) in sacroiliac joints and predict the MRI Assessment of SpondyloArthritis International Society (ASAS) definition of active sacroiliitis in patients with chronic inflammatory back pain. MATERIALS AND METHODS: MRI examinations of patients from the French prospective multicenter DESIR cohort (DEvenir des Spondyloarthropathies Indifférenciées Récentes) were used for training, validation and testing. Patients with inflammatory back pain lasting three months to three years were recruited. Test datasets were from MRI follow-ups at five years and ten years. The model was evaluated using an external test dataset from the ASAS cohort. A neuronal network classifier (mask-RCNN) was trained and evaluated for sacroiliac joints detection and BME classification. Diagnostic capabilities of the model to predict ASAS MRI active sacroiliitis (BME in at least two half-slices) were assessed using Matthews correlation coefficient (MCC), sensitivity, specificity, accuracy and AUC. The gold standard was experts' majority decision. RESULTS: A total of 256 patients with 362 MRI examinations from the DESIR cohort were included, with 27% meeting the ASAS definition for experts. A total of 178 MRI examinations were used for the training set, 25 for the validation set and 159 for the evaluation set. MCCs for DESIR baseline, 5-years, and 10-years follow-up were 0.90 (n = 53), 0.64 (n = 70), and 0.61 (n = 36), respectively. AUCs for predicting ASAS MRI were 0.98 (95% CI: 0.93-1), 0.90 (95% CI: 0.79-1), and 0.80 (95% CI: 0.62-1), respectively. The ASAS external validation cohort included 47 patients (mean age 36 ± 10 [SD] years; women, 51%) with 19% meeting the ASAS definition. MCC was 0.62, sensitivity 56% (95% CI: 42-70), specificity 100% (95% CI: 100-100) and AUC 0.76 (95% CI: 0.57-0.95). CONCLUSION: The deep learning model achieves performance close to those of experts for BME detection in sacroiliac joints and determination of active sacroiliitis according to the ASAS definition.

Risk stratification using anti-citrullinated peptide antibodies (ACPA) in polyarticular subtypes of juvenile idiopathic arthritis in adulthood.

OBJECTIVES: Polyarticular juvenile idiopathic arthritis (pJIA) is a subset of juvenile idiopathic arthritis (JIA), divided into two subtypes according to the presence of rheumatoid factor: pJIA without rheumatoid factor (pJIA RF-) and pJIA with positive rheumatoid factor (pJIA RF+), this latter is characterised with more structural damage. Anti-citrullinated peptide antibodies (ACPA) are often associated with RF. The respective performance of ACPA versus RF in structural outcome in pJIA, and in particular in adulthood pJIA remains unknown. Therefore, the aim of this study was to determine whether ACPA could be of value to assess structural damage in pJIA persisting in adulthood. METHODS: Patients with pJIA and available data for ACPA, RF and X-ray were included retrospectively. Structural damage was assessed by two independent blinded investigators using Sharp Van Der Heijde scores. RESULTS: 56 pJIA adult patients were included: 62% (35/56) had pJIA RF+ and 38% (21/56) pJIA RF-. ACPA positivity in pJIA was significantly associated with presence of RF (96% vs 26%, P<0.001). RF positivity was significantly associated with higher Sharp van Der Heijde erosion and total scores (respectively P<0.01 and P<0.05). There were higher Sharp Van Der Heijde erosion, joint space narrowing and total scores in the pJIA ACPA+ subgroup than in the pJIA ACPA- subgroup, although there was no statistical significance. However, when adjusted on disease duration, pJIA ACPA+ patients had significantly higher erosion and total scores than pJIA ACPA- patients (P<0.05), and pJIA ACPA+ patients required more bDMARDs than pJIA ACPA- patients (P<0.05). Moreover, pJIA patients with high Sharp van Der Heijde joint space narrowing and total scores had significantly higher ACPA levels (P<0.01). A correlation was identified between ACPA levels and Sharp van Der Heijde total score (r=0.54, P<0.05). In the pJIA RF+ subgroup the presence of ACPA was associated with additional structural damage compared to no ACPA: sharp Van Der Heijde erosion, joint space narrowing and total scores were higher in the pJIA RF+ ACPA+ subgroup than in the pJIA RF+ ACPA- subgroup although these results did not reach significance. CONCLUSION: Our results suggest that pJIA RF+ ACPA+ adult patients may have a more severe articular phenotype than pJIA RF+ ACPA- patients. ACPA could bring an additional value to RF for pJIA patients regarding structural damage. Altogether our results show that RF and ACPA are associated with structural damage measured by Sharp Van Der Heijde score in pJIA persisting in adulthood.

Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications.

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

Percutaneous alcohol embolization and vertebroplasty of a T2 aggressive vertebral hemangioma with prior embolization of dangerous arterial anastomoses.

Aggressive vertebral hemangiomas usually exhibit extraosseous expansion that can result in spinal cord or radicular compression.1 In symptomatic cases, treatment by alcohol embolization and percutaneous vertebroplasty has been reported as feasible, safe, and effective with long-term benefits on neurological symptoms.2 Safety rules before vertebral alcohol embolization include preoperative spinal cord vascularization mapping and opacification through bone needles to assess the absence of dangerous intratumoral anastomoses.In video 1 we present a case of a symptomatic T2 aggressive vertebral hemangioma with dangerous anastomoses between the lesion and both supreme intercostal arteries (SIAs). Embolization by the arterial route of both SIAs was performed, which required good anatomic knowledge of the spinal cord vascularization at the cervicothoracic junction3 4 as a cervical radiculomedullary artery arose from the left costocervical trunk which also fed the left SIA. After occlusion of all dangerous arterial anastomoses, we were able to successfully perform T2 alcohol embolization and percutaneous vertebroplasty. neurintsurg;15/7/728/V1F1V1Video 1Case presentation.

Artificial intelligence in diagnostic and interventional radiology: Where are we now?

The emergence of massively parallel yet affordable computing devices has been a game changer for research in the field of artificial intelligence (AI). In addition, dramatic investment from the web giants has fostered the development of a high-quality software stack. Going forward, the combination of faster computers with dedicated software libraries and the widespread availability of data has opened the door to more flexibility in the design of AI models. Radiomics is a process used to discover new imaging biomarkers that has multiple applications in radiology and can be used in conjunction with AI. AI can be used throughout the various processes of diagnostic imaging, including data acquisition, reconstruction, analysis and reporting. Today, the concept of "AI-augmented" radiologists is preferred to the theory of the replacement of radiologists by AI in many indications. Current evidence bolsters the assumption that AI-assisted radiologists work better and faster. Interventional radiology becomes a data-rich specialty where the entire procedure is fully recorded in a standardized DICOM format and accessible via standard picture archiving and communication systems. No other interventional specialty can bolster such readiness. In this setting, interventional radiology could lead the development of AI-powered applications in the broader interventional community. This article provides an update on the current status of radiomics and AI research, analyzes upcoming challenges and also discusses the main applications in AI in interventional radiology to help radiologists better understand and criticize articles reporting AI in medical imaging.

Effect of diagnosis level of certainty on adherence to antibiotics' guidelines in ED patients with pneumonia: a post-hoc analysis of an interventional trial.

BACKGROUND AND IMPORTANCE: Clinical diagnosis of community-acquired pneumonia (CAP) is difficult to establish with certainty. Adherence to antibiotic guidelines independently affects the prognosis of CAP patients. OBJECTIVE: We aimed to determine whether guidelines' adherence was related to CAP diagnosis level of certainty and could be reinforced accordingly to diagnosis improvement. DESIGN: Secondary analysis of a prospective, multicenter study, which evaluated the impact of early thoracic CT scan on diagnosis and therapeutic plan in patients with clinically suspected CAP visiting emergency departments. SETTING AND PARTICIPANTS: In total 319 patients with clinically suspected CAP were enrolled in four emergency departments, Paris, France, between Nov 2011 and Jan 2013. OUTCOME MEASURES AND ANALYSIS: We evaluated guidelines' adherence before and after CT scan and its relationship with CAP diagnosis level of certainty. Antibiotics were categorized as adherent according to 2010 French guidelines. CAP diagnosis level of certainty was prospectively classified by the emergency physicians based on a Likert scale as excluded, possible, probable or definite before and immediately after the CT scan. These classifications and therapeutic plans were also completed by an independent adjudication committee. Determinants of adherence were assessed using Poisson regression with robust variance. MAIN RESULTS: Adherence to guidelines increased from 34.2% before CT scan to 51.3% after CT scan [difference 17.1% (95% CI, 9.5-24.7)], meanwhile CAP diagnosis with high level of certainty (definite and excluded CAP) increased from 46.1 to 79.6% [difference 33.5% (95% CI, 26.5-40.5)]. Diagnosis level of certainty before CT scan was the strongest determinant of adherence in multivariate analysis (RR, 2.63; 95% CI, 1.89-3.67). CONCLUSION: Antibiotic guidelines' adherence was poor and positively related to CAP diagnosis level of certainty. The results suggest that improvements in CAP diagnosis may increase adherence to antibiotic guidelines. Clinical trial registered with www.clinicaltrials.gov (NCT01574066).

Prognostic factors of the synovial sarcoma of the extremities: imaging does matter.

OBJECTIVES: Synovial sarcomas (SS) of the extremities are rare soft tissue sarcomas that are more common in young adults. We deciphered the imaging phenotype of SS with the aim to determine if imaging could provide an incremental value to currently known prognostic factors (PF)-age and histological grade-to predict long-term overall survival (OS). METHODS: This retrospective multicenter study included consecutive pediatric and adult patients with synovial sarcomas of the extremities from December 2002 to August 2020. Inclusion criteria were (i) a follow-up greater than 5 years and (ii) available pre-therapeutic MRI. A subset analysis included MRI and CT-scan. Clinical, pathological, and imaging variables were collected in all patients. The primary endpoint was to evaluate the association of these variables with OS using univariate and multivariate Cox regressions. RESULTS: Out of 428 patients screened for eligibility, 98 patients (mean age: 37.1 ± 15.2 years) were included (MRI: n = 98/98, CT scan: n = 34/98; 35%). The median OS was 75.25 months (IQR = 55.50-109.12) and thirty-six patients (n = 36/98;37%) died during follow-up. The recurrence rate was 12.2% (n =12/98). SS lesions were mostly grade 2 (57/98; 58%). On MRI, SS had a mean long-axis diameter of 67.5 ± 38.3 mm. On CT scan, 44% (15/34) were calcified. Grade (hazard ratio [HR] = 2.71; 95%CI = 1.30-5.66; p = 0.008), size of the lesions evaluated on MRI (HR = 1.02; 95% CI = 1.01-1.03; p < 0.001), and calcifications on CT scan (HR = 0.10; 95% CI = 0.02-0.50; p = 0.005) were independent PF of OS. CONCLUSIONS: This study demonstrated that imaging biomarkers can be used to predict long-term outcome in patients with SS. Strikingly, the presence of calcifications on CT scan is associated with favorable outcome and provides an incremental value over existing PF such as age, grade, and size. KEY POINTS: • Beyond its diagnostic value, MRI is a pre-operative prognostic tool in synovial sarcomas of the extremities since the size of the lesion is an important prognostic factor. • Calcifications on CT scans are independently and significantly associated with prolonged overall survival.

Impact of a multidisciplinary team meeting on patient-reported outcomes at 2 years after lumbar surgery: A prospective comparative exploratory study.

Failed back surgery syndrome is a challenge. We hypothesized that a multidisciplinary team meeting (MTM) may be useful to select patients who are the most likely to benefit from lumbar surgery. We conducted an observational, prospective, comparative, exploratory study. We aimed to compare core clinical patient-reported outcomes at 2 years after lumbar surgery between patients who attended a MTM and those who did not. Patients who underwent lumbar surgery for a degenerative disease, in a single academic orthopedic department, between January and September 2018, were consecutively screened. Eligible patients were surveyed between April and June 2020. Patient-reported outcomes included lumbar and radicular pain, spine-specific activity limitations and health-related quality of life assessed via self-administered questionnaires. Outcomes were compared between respondents who attended the MTM and those who did not. Overall, 211 patients underwent lumbar surgery, 108 were eligible and 44 included: 11 attended the MTM and 33 did not. Mean participants' age was 57.4 (15.4) years, symptom duration was 14.8 (15.3) months, lumbar pain was 51.3 (18.2) and radicular pain was 53.4 (18.6). At 2 years, we found no evidence that lumbar and radicular pain, activity limitations and health-related quality of life differed between the 2 groups. The decrease was -26.8 (41.1) versus -20.8 (30.4) in lumbar pain and -25.5 (43.0) versus -19.5 (27.5) in radicular pain, in participants who attended the MTM versus those who did not, respectively. We found no evidence that core clinical patient-reported outcomes at 2 years after lumbar surgery differed between participants who attended the MTM and those who did not. However, the exploratory design of our study does not allow concluding that MTMs do not have an impact.

Performance of Sonoelastography for predicting malignancy in soft tissue.

BACKGROUND: Separating benign from malignant soft-tissue masses often requires a biopsy. The objective of this study was to assess whether shear-wave elastography (SWE) helped to separate benign from malignant soft-tissue masses. METHODS: In 2015-2016, we prospectively included patients with soft-tissue masses deemed by our multidisciplinary sarcoma board to require a diagnostic biopsy. All patients underwent ultrasonography (US) followed by SWE to measure elasticity. We compared benign and malignant tumors, overall and after separating tumors with vs. without a fatty component. The biopsy findings, and surgical-specimen histology when available, served as the reference standard. RESULTS: We included 136 patients, 99 with non-fatty and 37 with fatty soft-tissue masses. Mean elasticity and tumor-to-fat elasticity ratio (T/F) values were significantly lower for the benign than the malignant soft-tissue masses in the overall cohort (30.9 vs. 50.0 kilopascals (kPa), P = 0.03; and 2.55 vs. 4.30, P = 0.046) and in the non-fatty subgroup (37.8 ± 31.9 vs. 58.9 ± 39.1 kPa, P = 0.049 and 2.89 ± 5.25 vs. 5.07 ± 5.41, P = 0.046). Data for fatty tumors were non relevant due to lack of conclusive results. By receiver operating characteristics curve analysis, a T/F cutoff of 3.5 had 46% sensitivity and 84% specificity for separating benign and malignant soft-tissue masses. CONCLUSIONS: SWE had good specificity and poor sensitivity for separating benign from malignant soft-tissue masses.

Degenerative Lumbar Spine Disease: Imaging and Biomechanics.

Chronic low back pain (CLBP) is one of the most common diagnoses encountered when considering years lived with disability. The degenerative changes of the lumbar spine include a wide spectrum of morphological modifications visible on imaging, some of them often asymptomatic or not consistent with symptoms. Phenotyping by considering both clinical and imaging biomarkers can improve the management of CLBP. Depending on the clinical presentation, imaging helps determine the most likely anatomical nociceptive source, thereby enhancing the therapeutic approach by targeting a specific lesion. Three pathologic conditions with an approach based on our experience can be described: (1) pure painful syndromes related to single nociceptive sources (e.g., disk pain, active disk pain, and facet joint osteoarthritis pain), (2) multifactorial painful syndromes, representing a combination of several nociceptive sources (such as lumbar spinal stenosis pain, foraminal stenosis pain, and instability pain), and (3) nonspecific CLBP, often explained by postural (muscular) syndromes.

Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays.

PURPOSE: To appraise the performances of an AI trained to detect and localize skeletal lesions and compare them to the routine radiological interpretation. METHODS: We retrospectively collected all radiographic examinations with the associated radiologists' reports performed after a traumatic injury of the limbs and pelvis during 3 consecutive months (January to March 2017) in a private imaging group of 14 centers. Each examination was analyzed by an AI (BoneView, Gleamer) and its results were compared to those of the radiologists' reports. In case of discrepancy, the examination was reviewed by a senior skeletal radiologist to settle on the presence of fractures, dislocations, elbow effusions, and focal bone lesions (FBL). The lesion-wise sensitivity of the AI and the radiologists' reports was compared for each lesion type. This study received IRB approval (CRM-2106-177). RESULTS: A total of 4774 exams were included in the study. Lesion-wise sensitivity was 73.7% for the radiologists' reports vs. 98.1% for the AI (+24.4 points) for fracture detection, 63.3% vs. 89.9% (+26.6 points) for dislocation detection, 84.7% vs. 91.5% (+6.8 points) for elbow effusion detection, and 16.1% vs. 98.1% (+82 points) for FBL detection. The specificity of the radiologists' reports was always 100% whereas AI specificity was 88%, 99.1%, 99.8%, 95.6% for fractures, dislocations, elbow effusions, and FBL respectively. The NPV was measured at 99.5% for fractures, 99.8% for dislocations, and 99.9% for elbow effusions and FBL. CONCLUSION: AI has the potential to prevent diagnosis errors by detecting lesions that were initially missed in the radiologists' reports.

Lumbar Spine Posttherapeutic Imaging.

Management of patients after lumbar spine surgery or interventional radiology can be complex, and postoperative imaging patterns are often poorly understood by nonspecialized radiologists. This article focuses on postoperative imaging features of the lumbar spine in five clinical settings (with corresponding interventions): vertebral osteoporotic fractures (percutaneous vertebroplasty and vertebral augmentation), lumbar disk herniation (surgical diskectomy and percutaneous interventional radiology), lumbar spinal stenosis (surgical decompression), lumbar spondylolisthesis (surgical decompression and fusion), and degenerative scoliosis (techniques of osteotomies).For each intervention, we discuss imaging indications, depending if the patient is asymptomatic or if there are suspected complications, describe normal and pathologic imaging features, and present key points.