Presentation of enthesitis-related arthritis and juvenile-onset spondyloarthritis: a cross-sectional study in a pediatric and adult clinic.

BACKGROUND: Juvenile idiopathic arthritis (JIA) comprises a whole spectrum of chronic arthritis starting before 16 years of age. The study aims to explore the clinical and demographic descriptors, treatment, and disease progression of enthesitis-related arthritis (ERA) in comparison with juvenile-onset spondyloarthritis (SpA). METHODS: Cross-sectional analysis of consecutive patients in two dedicated clinics, with a single visit and retrospective case-notes review. Arthritis, enthesitis and sacroiliitis were evaluated by scoring disease activity and damage. Continuous variables were reported by median, interquartile range; categorical variables were reported by the frequency comparison of the two groups. RESULTS: Thirty-three cases were included, being 23 (69.7%) with ERA. The median age at diagnosis was 12.5 y (SpA) vs. 9 y (ERA) (p < 0.01); the time from symptom onset to diagnosis was 5.5 y (SpA) vs. 1.5 y (ERA) (p < 0.03). In both groups, the predominant presentation was a single joint or < 5 lower limb joints and asymmetric involvement, with a high frequency of enthesitis. There was a higher frequency of mid-tarsal and ankle synovitis in the ERA group and hip involvement in those with SpA. The comparison of the frequency of spine symptoms at presentation, 30% SpA vs. 21.7% ERA (p = 0.7), was not significant, and radiographic progression to spinal involvement occurred in 43.5% of ERA patients. The median time for spinal progression and age at onset was 2.2 and 12 y for ERA, and 4 and 16.5 y for SpA, respectively. Activity and damage scores were not significantly different between the groups. Treatment comparison resulted in 91.3% of ERA and 100% SpA being treated, predominantly with NSAIDs in both groups, followed by DMARDs and biologics, with a higher frequency of biologics in SpA. CONCLUSION: The main differences were the late diagnoses of SpA, and the hip and spine involvement, with higher frequency of biologic treatment in juvenile-onset SpA compared to ERA.

Relationship between sacroiliitis and inflammatory markers in familial Mediterranean fever.

OBJECTIVE: Familial Mediterranean fever is the most common monogenic autoinflammatory disease. This study aimed to evaluate the relationship between sacroiliitis observed in familial Mediterranean fever and hematological inflammatory markers. METHODS: In this study, 168 familial Mediterranean fever patients were examined. A total of 61 familial Mediterranean fever patients who had sacroiliac magnetic resonance imaging due to waist and hip pain were included in the study. According to the magnetic resonance imaging findings, patients were divided into two groups: with and without sacroiliitis. The relationship between hematological inflammatory markers and sacroiliitis was investigated. RESULTS: The frequency of sacroiliitis was found to be 13.6% in all familial Mediterranean fever patients and 37.8% in patients with low back pain who underwent sacroiliac magnetic resonance imaging. Neutrophil count, neutrophil/lymphocyte ratio, monocyte/lymphocyte ratio, and systemic immune-inflammatory index were significantly higher in the sacroiliitis group than in the other group, and this difference was found to be statistically significant (p<0.05). As a result of the receiver operating characteristic analysis, it was observed that neutrophil/lymphocyte ratio, monocyte/lymphocyte ratio, and systemic immune-inflammatory index were very sensitive parameters in determining sacroiliitis in patients with familial Mediterranean fever. CONCLUSION: It was observed that the frequency of sacroiliitis was increased in familial Mediterranean fever patients. It is predicted that hematological inflammatory markers such as neutrophil/lymphocyte ratio, monocyte/lymphocyte ratio, and systemic immune-inflammatory index can be used in the diagnosis of sacroiliitis.

Does sacroiliitis is a mandatory criterion for enthesitis-related arthritis diagnosis?

INTRODUCTION AND OBJECTIVES: Magnetic resonance imaging (MRI) sensitivity and specificity seem to be less studied in enthesitis-related arthritis (ERA). We aimed to determine the ability of sacroiliac MRI to diagnose ERA patients. MATERIALS AND METHODS: We conducted a retrospective study including 44 patients with juvenile idiopathic arthritis (JIA). Each patient had a sacroiliac joint MRI. We divided patients into two groups: G1 patients with ERA and G2 patients with non-ERA subtype. RESULTS: ERA was noted in 61% of the cases. Sacroiliac joints were painful in 15 patients (34%). MRI was normal in 25 patients (57%) (G1:11 versus G2:14) and showed bone marrow edema in the sacroiliac joints in 19 patients (34%) (G1=16 versus G2=3, p=0.005). Sacroiliac joints MRI's sensitivity and specificity in the ERA diagnosis were 61.54% and 82.35%, respectively. Positive and negative predictive values were 84.21% and 58.33%, respectively. Furthermore, sacroiliac joint pain in the clinical examination was able to predict sacroiliac bone edema in MRI with an odds ratio of 6.8 (95% CI 1.68-28.09; p=0.006). CONCLUSION: Our study showed that sacroiliac joint MRI has good specificity and positive predictive value in the diagnosis of ERA patients among JIA patients. This underlines the usefulness of sacroiliac joint MRI in the early diagnosis of ERA patients.

Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis.

OBJECTIVES: This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, which integrates multimodal MRI features and clinical information, in diagnosing sacroiliitis related to axial spondyloarthritis (axSpA). MATERIAL & METHODS: A total of 485 patients diagnosed with sacroiliitis related to axSpA (n = 288) or non-sacroiliitis (n = 197) by sacroiliac joint (SIJ) MRI between May 2018 and October 2022 were retrospectively included in this study. The patients were randomly divided into training (n = 388) and testing (n = 97) cohorts. Data were collected using three MRI scanners. We applied a convolutional neural network (CNN) called 3D U-Net for automated SIJ segmentation. Additionally, three CNNs (ResNet50, ResNet101, and DenseNet121) were used to diagnose axSpA-related sacroiliitis using a single modality. The prediction results of all the CNN models across different modalities were integrated using a stacking method based on different algorithms to construct ensemble models, and the optimal ensemble model was used as DLR signature. A combined model incorporating DLR signature with clinical factors was developed using multivariable logistic regression. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Automated deep learning-based segmentation and manual delineation showed good correlation. ResNet50, as the optimal basic model, achieved an area under the curve (AUC) and accuracy of 0.839 and 0.804, respectively. The combined model yielded the highest performance in diagnosing axSpA-related sacroiliitis (AUC: 0.910; accuracy: 0.856) and outperformed the best ensemble model (AUC: 0.868; accuracy: 0.825) (all P < 0.05). Moreover, the DCA showed good clinical utility in the combined model. CONCLUSION: We developed a diagnostic model for axSpA-related sacroiliitis by combining the DLR signature with clinical factors, which resulted in excellent diagnostic performance.

The value of a repeat MRI examination of the sacroiliac joints following an inconclusive initial examination.

OBJECTIVE: Assess the diagnostic utility of repeat sacroiliac joint (SIJ) magnetic resonance imaging (MRI) examinations following an inconclusive initial examination performed for suspected sacroiliitis. METHOD: Subjects with > 1 SIJ MRI examinations, an inconclusive first scan and at least 6 months interval between scans, were included. All scans were evaluated for the presence of structural/active SIJ lesions as well as any other pathology. Clinical data was extracted from the patients' clinical files, and any missing data was obtained by a telephone interview. Diagnosis and active/structural scores were compared between first and follow-up examinations (t test). RESULTS: Seventy-one subjects were included in the study, 77.4% females, mean age 41.0 ± 15 years, mean time interval between exams 30.4 ± 25.24 months. Twelve subjects performed > 2 scans. In only two subjects (2.81%), both females, MRI diagnosis changed from inconclusive to definite sacroiliitis. None of the subjects with > 2 scans had evidence of sacroiliitis in any of the following MRI examinations. Significant differences were observed between the scores of active SIJ lesion of the first and follow-up MRI (1.51/1.62, p = 0.02) but not for scores of structural lesions (1.22/1.68, p = 0.2). CONCLUSIONS: Repeat SIJ MRI when the first MRI is inconclusive for sacroiliitis is more valuable in ruling out than in securing diagnosis of sacroiliitis. We suggest that when MRI findings are inconclusive, decision-making should be based on clinical data.

Can rheumatologists unequivocally diagnose axial spondyloarthritis in patients with chronic back pain of less than 2 years duration? Primary outcome of the 2-year SPondyloArthritis Caught Early (SPACE) cohort.

OBJECTIVES: To investigate the prevalence of axial spondyloarthritis (axSpA) in patients with chronic back pain (CBP) of less than 2 years (2y) duration referred to the rheumatologist, the development of diagnosis over time, and patient characteristics of those developing definite (d-)axSpA over 2y. METHODS: We analysed the 2y data from SPondyloArthritis Caught Early, a European cohort of patients (<45 years) with CBP (≥3 months, ≤2y) of unknown origin. The diagnostic workup comprised evaluation of clinical SpA features, acute phase reactants, HLA-B27, radiographs and MRI (sacroiliac joints and spine), with repeated assessments. At each visit (baseline, 3 months, 1y and 2y), rheumatologists reported a diagnosis of axSpA or non-axSpA with level of confidence (LoC; 0-not confident at all to 10-very confident). MAIN OUTCOME: axSpA diagnosis with LoC≥7 (d-axSpA) at 2y. RESULTS: In 552 patients with CBP, d-axSpA was diagnosed in 175 (32%) at baseline and 165 (30%) at 2y. Baseline diagnosis remained rather stable: at 2y, baseline d-axSpA was revised in 5% of patients, while 8% 'gained' d-axSpA. Diagnostic uncertainty persisted in 30%. HLA-B27+ and baseline sacroiliitis imaging discriminated best 2y-d-axSpA versus 2y-d-non-axSpA patients. Good response to non-steroidal anti-inflammatory drugs and MRI-sacroiliitis most frequently developed over follow-up in patients with a new d-axSpA diagnosis. Of the patients who developed MRI-sacroiliitis, 7/8 were HLA-B27+ and 5/8 male. CONCLUSION: A diagnosis of d-axSpA can be reliably made in nearly one-third of patients with CBP referred to the rheumatologist, but diagnostic uncertainty may persist in 5%-30% after 2y. Repeated assessments yield is modest, but repeating MRI may be worthwhile in male HLA-B27+ patients.

Sacroiliitis diagnosis based on interpretable features and multi-task learning.

Objective.Sacroiliitis is an early pathological manifestation of ankylosing spondylitis (AS), and a positive sacroiliitis test on imaging may help clinical practitioners diagnose AS early. Deep learning based automatic diagnosis algorithms can deliver grading findings for sacroiliitis, however, it requires a large amount of data with precise labels to train the model and lacks grading features visualization. In this paper, we aimed to propose a radiomics and deep learning based deep feature visualization positive diagnosis algorithm for sacroiliitis on CT scans. Visualization of grading features can enhance clinical interpretability with visual grading features, which assist doctors in diagnosis and treatment more effectively.Approach.The region of interest (ROI) is identified by segmenting the sacroiliac joint (SIJ) 3D CT images using a combination of the U-net model and certain statistical approaches. Then, in addition to extracting spatial and frequency domain features from ROI according to the radiographic manifestations of sacroiliitis, the radiomics features have also been integrated into the proposed encoder module to obtain a powerful encoder and extract features effectively. Finally, a multi-task learning technique and five-class labels are utilized to help with performing positive tests to reduce discrepancies in the evaluation of several radiologists.Main results.On our private dataset, proposed methods have obtained an accuracy rate of 87.3%, which is 9.8% higher than the baseline and consistent with assessments made by qualified medical professionals.Significance.The results of the ablation experiment and interpreting analysis demonstrated that the proposed methods are applied in automatic CT scan sacroiliitis diagnosis due to their excellently interpretable and portable advantages.

Goodbye to the term 'ankylosing spondylitis', hello 'axial spondyloarthritis': time to embrace the ASAS-defined nomenclature.

Ankylosing spondylitis (AS) is the historic term used for decades for the HLA-B27-associated inflammatory disease affecting mainly the sacroiliac joints (SIJ) and spine. Classification criteria for AS have radiographic sacroiliitis as a dominant characteristic. However, with the availability of MRI of SIJ, it could be demonstrated that the disease starts long before definite SIJ changes become visible on radiographs. The Assessment of SpondyloArthritis international Society, representing a worldwide group of experts reached consensus on changes in the nomenclature pertaining to axial spondyloarthritis (axSpA), such as the terminology of diagnosis and of assessment of disease activity tools. These are important changes in the field, as experts in axSpA are now in agreement that the term axSpA is the overall term for the disease. A further differentiation, of which radiographic versus non-radiographic is only one aspect, may be relevant for research purposes. Another important decision was that the terms AS and radiographic axSpA (r-axSpA) can be used interchangeably, but that the preferred term is r-axSpA. Based on the decision that axSpA is the correct terminology, a proposal was made to officially change the meaning of the ASDAS acronym to 'Axial Spondyloarthritis Disease Activity Score'. In addition, for simplification it was proposed that the term ASDAS (instead of ASDAS-CRP) should be preferred and applied to the ASDAS calculated with C reactive protein (CRP). It is hoped that these changes will be used consequently for education, in textbooks, manuscripts and presentations.

Subclinical enthesitis in enthesitis-related arthritis and sacroiliitis associated with familial Mediterranean fever.

OBJECTIVES: In our study, we investigated the presence of subclinical enthesitis by ultrasonography (US) in asymptomatic patients with enthesitis-related arthritis (ERA) and sacroiliitis associated with familial Mediterranean fever (FMF). METHODS: A total of 50 patients, including 35 patients with ERA and 15 with sacroiliitis associated with FMF, were included in the study. All patients were evaluated with US by a paediatric radiologist. Enthesis of seven tendons (common extensor and flexor tendons, quadriceps tendon, proximal and distal patellar tendon, Achilles tendon, and plantar fascia) was examined on both sides. RESULTS: Subclinical enthesitis was detected in 10 ERA (28.5%) and three FMF (20%) patients. Enthesitis was radiologically diagnosed in 16 (2.3%) out of 700 evaluated entheseal sites. The most frequent sites of enthesitis were Achilles (37.5%) and quadriceps (31.3%) tendons. All patients were in clinical remission and had no active complaints, and acute phase reactants were within normal limits. Therefore, the patients were followed up without treatment change. However, disease flare-up was observed in three of these patients (23.1%) during the follow-up, and their treatments were intensified. CONCLUSIONS: Our results showed that the US can be particularly helpful in detecting subclinical enthesitis and predicting disease flare-ups.

Association of anatomical variants of the sacroiliac joint with bone marrow edema in patients with axial spondyloarthritis.

OBJECTIVE: To determine the prevalence of sacroiliac joint variants in patients with axial spondyloarthritis (axSpA) using MRI-based synthetic CT images and to evaluate their relationships with the presence of bone marrow edema, as this may potentially complicate diagnosing active sacroiliitis on MRI in patients with suspected axSpA. METHODS: 172 patients were retrospectively included. All patients underwent MRI because of clinical suspicion of sacroiliitis. The diagnosis of axSpA was made by a tertiary hospital rheumatologist. Two readers independently determined the presence of bone marrow edema and the presence of one or more of the nine known sacroiliac joint (SIJ) variants. RESULTS: SIJ variants were common in axSpA patients (82.9%) and the non-SpA group (85.4%); there were no significant differences in prevalence. Bone marrow edema was frequently found in axSpA (86.8%) and non-SpA patients (34%). AxSpA patients with SIJ variants (except for accessory joint) demonstrated 4 to 10 times higher odds for bone marrow edema, however not statistically significant. The more variants were present in this group, the higher the chance of bone marrow edema. However, some multicollinearity cannot be excluded, since bone marrow edema is very frequent in the axSpA group by definition. CONCLUSION: SIJ variants are common in axSpA and non-SpA patients. SIJ variants were associated with higher prevalence of bone marrow edema in axSpA patients, potentially due to altered biomechanics, except for accessory joint which may act as a stabilizer.

[Clinical and imaging features of infective sacroiliitis in children].

Objective: To summarize the clinical, radiological characteristics, and prognosis of infectious sacroiliitis in children. Methods: A case-control study was conducted, including 12 cases of infectious sacroiliitis diagnosed in the Rheumatology and Immunology Department of the Children's Hospital affiliated with the Capital Institute of Pediatrics from June 2018 to June 2023. These cases comprised the case group. Concurrently, 28 cases of pediatric idiopathic arthritis involving the sacroiliac joint in the same department served as the control group. Basic patient information, clinical features, laboratory parameters, and clinical treatment outcomes for both groups were collected and analyzed. Independent sample t-tests and chi-squared tests were used for inter-group comparisons. Results: Among the 12 cases in the case group, there were 5 males and 7 females, with a disease duration of 0.8 (0.5, 1.2) months. Nine patients presented with fever, and 1 patient had limping gait. Human leukocyte antigen (HLA)-B27 positivity was observed in 1 case, and there was no family history of ankylosing spondylitis. In the control group of 28 cases, there were 19 males and 9 females, with a disease duration of 7.0 (3.0, 17.0) months. One patient (4%) had fever, and 14 cases (50%) exhibited limping gait. HLA-B27 positivity was found in 18 cases (64%), and 18 cases (64%) had a family history of ankylosing spondylitis. The case group had higher white blood cell count (WBC), neutrophil ratio, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP) levels, as well as a higher proportion of unilateral involvement on magnetic resonance imaging and bone destruction on CT compared to the control group ((11.1±6.2)×109 vs. (7.3±2.3)×109/L, 0.64±0.10 vs. 0.55±0.12, 72 (34, 86) vs. 18 (5, 41) mm/1 h, 24.6 (10.1, 67.3) mg/L vs. 3.6 (0.8, 15.0) mg/L, 11/12 vs. 36% (10/28), 9/12 vs. 11% (3/28), t=2.90, 3.07, Z=-2.94, -3.28, χ2=10.55, 16.53, all P<0.05). Conclusions: Pediatric infectious sacroiliitis often presents as unilateral involvement with a short disease history. Elevated WBC, CRP, and ESR, as well as a high rate of bone destruction, are also common characteristics.

Clash of the titans: Current CT and CT-like imaging modalities in sacroiliitis in spondyloarthritis.

Sacroiliitis is characterised by active and structural changes of the joint. While the Assessment of Spondyloarthritis international Society (ASAS) classification criteria stress the importance of bone marrow inflammation, recent reports suggest that osteitis can occur in various diseases, mechanical conditions and healthy individuals. Thus, structural lesions such as joint surface erosion and ankylosis are important factors for differential diagnosis. Various imaging modalities are available to examine these changes. However, computed tomography (CT) is generally considered the reference standard. Nonetheless, recent advances in magnetic resonance imaging (MRI) allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. This way, the ability of MRI to detect and measure structural lesions is strengthened. The aim of this review is to provide an overview of the pros and cons of CT and CT-like imaging modalities in sacroiliitis.

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis.

Background: Magnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI. Methods: This study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation. Results: A total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705-0.745), 0.936 (95% CI, 0.924-0.947), and 0.830 (95%CI, 0.792-0.868), respectively, at the image level and 0.947 (95% CI, 0.912-0.982), 0.691 (95% CI, 0.603-0.779), and 0.816 (95% CI, 0.776-0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493-0.780), 0.944 (95% CI, 0.933-0.955), and 0.731 (95% CI, 0.681-0.780), respectively, at the image level and 0.806 (95% CI, 0.729-0.883), 0.617 (95% CI, 0.523-0.711), and 0.711 (95% CI, 0.660-0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation. Conclusion: An AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting.

Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.

OBJECTIVES: Summarise the evidence of the performance of the machine learning algorithm in discriminating sacroiliitis features on MRI and compare it with the accuracy of human physicians. METHODS: MEDLINE, EMBASE, CIHNAL, Web of Science, IEEE, American College of Rheumatology and European Alliance of Associations for Rheumatology abstract archives were searched for studies published between 2008 and 4 June 2023. Two authors independently screened and extracted the variables, and the results are presented using tables and forest plots. RESULTS: Ten studies were selected from 2381. Over half of the studies used deep learning models, using Assessment of Spondyloarthritis International Society sacroiliitis criteria as the ground truth, and manually extracted the regions of interest. All studies reported the area under the curve as a performance index, ranging from 0.76 to 0.99. Sensitivity and specificity were the second-most commonly reported indices, with sensitivity ranging from 0.56 to 1.00 and specificity ranging from 0.67 to 1.00; these results are comparable to a radiologist's sensitivity of 0.67-1.00 and specificity of 0.78-1.00 in the same cohort. More than half of the studies showed a high risk of bias in the analysis domain of quality appraisal owing to the small sample size or overfitting issues. CONCLUSION: The performance of machine learning algorithms in discriminating sacroiliitis features on MRI varied owing to the high heterogeneity between studies and the small sample sizes, overfitting, and under-reporting issues of individual studies. Further well-designed and transparent studies are required.

[Computed tomography versus magnetic resonance imaging : Pros and cons in axial spondyloarthritis]. / Computertomographie versus Magnetresonanztomographie : Pro und Kontra in der axialen Spondyloarthritis.

The diagnosis of axial spondyloarthritis depends on direct visualization of the sacroiliitis in addition to clinical assessment and determination of the histocompatibility antigen HLA-B27. While the value of conventional radiographic images has meanwhile been described in many studies as insufficient to diagnose the disease at an early stage, magnetic resonance imaging and also computed tomography now offer the possibility to visualize findings, such as bone marrow edema, erosion, fat metaplasia, backfill and ankylosis. Thus, it is necessary to decide which procedure should be used and when. Furthermore, both cross-sectional imaging techniques are currently undergoing major changes, and technical advancements are making great strides every year. This article provides an overview of which future technologies will be included in the rheumatological diagnostics of the sacroiliac joints. This overview also illustrates which standard methods are established in the diagnostics of axial spondyloarthritis and how they are used.

Current Role of Conventional Radiography of Sacroiliac Joints in Adults and Juveniles with Suspected Axial Spondyloarthritis: Opinion from the ESSR Arthritis and Pediatric Subcommittees.

This opinion article by the European Society of Musculoskeletal Radiology Arthritis and Pediatric Subcommittees discusses the current use of conventional radiography (CR) of the sacroiliac joints in adults and juveniles with suspected axial spondyloarthritis (axSpA). The strengths and limitations of CR compared with magnetic resonance imaging (MRI) and computed tomography (CT) are presented.Based on the current literature and expert opinions, the subcommittees recognize the superior sensitivity of MRI to detect early sacroiliitis. In adults, supplementary pelvic radiography, low-dose CT, or synthetic CT may be needed to evaluate differential diagnoses. CR remains the method of choice to detect structural changes in patients with suspected late-stage axSpA or established disease and in patients with suspected concomitant hip or pubic symphysis involvement. In children, MRI is the imaging modality of choice because it can detect active as well as structural changes and is radiation free.

Could serum uric acid to HDL cholesterol ratio predict sacroiliitis?

Recently, several inflammatory markers, including the uric acid to HDL cholesterol ratio (UHR), triglyceride/HDL cholesterol ratio (THR), systemic inflammatory index (SII), and C-reactive protein to albumin ratio (CAR), have been reported to be associated with inflammatory conditions. However, their collective role in sacroiliitis has not been extensively studied. This study aims to investigate the general characteristics and inflammatory markers in patients with and without sacroiliitis, and to observe any differences in these parameters in subjects with active and chronic sacroiliitis. Patient with sacroiliitis who showed up in the Radiology Department of Abant Izzet Baysal University Hospital were enrolled. Patients diagnosed with sacroiliitis based on clinical symptoms, physical examination, and conventional radiography or MRI findings were included in the sacroiliitis group. Patients without sacroiliitis who present with back pain or hip pain but have normal radiographic findings were included in the control group. General characteristics, including age, sex, body mass index (BMI), medical history, and disease duration, were collected from all participants. Blood samples were collected to measure inflammatory markers, including UHR, THR, SII, and CAR. The collected data were compared between sacroiliitis and control groups. Subgroup analysis was also performed to compare the inflammatory markers between subjects with active and chronic sacroiliitis. The median UHR of the sacroiliitis and control subjects were 11% (3-20%) and 7% (3-13%), respectively (p<0.001). Serum UHR was significantly and positively correlated with CRP (r: 0.4, p = 0.001) and ferritin (r: 0.17, p = 0.045) levels. In ROC analysis, a UHR level higher than 8% has an 81% sensitivity and 64% specificity in detecting sacroiliitis (AUC: 0.8, p<0.001, 95% CI: 0.72-0.84). In conclusion, we suggest that UHR could provide useful data as an additional diagnostic tool in patients with sacroiliitis.

Role of Patrick-FABER test in detecting sacroiliitis and diagnosing spondyloarthritis in subjects with low back pain.

OBJECTIVES: To evaluate sensitivity, specificity, and predictive value of Patrick-FABER test in assessing magnetic resonance imaging (MRI) sacroiliitis and addressing the diagnosis of spondyloarthritis (SpA) in subjects with low back pain (LBP). METHODS: Subjects with LBP were consecutively enrolled. The assessors were blinded to patients' clinical, laboratory, or imaging data. All subjects underwent sacroiliac joint MRI to detect presence of sacroiliac oedema or structural changes. RESULTS: One hundred and ten subjects were included in the study [males (61.8%); median age of 45 (21-69) years; LBP duration of 78 (3-240) months]. Patrick-FABER test sign's sensitivity was 76.2% (95% CI: 60.5-87.9%), specificity was 66.2% (95% CI: 53.6-77.2%), positive predictive value (PPV) was 58.1% (95% CI: 44.1-71.3%) and negative predictive value (NPV) was 81.8% (95% CI: 69.1-90.9%) for the diagnosis of sacroiliitis, with an overall diagnostic accuracy of 70%. At the univariate and multivariate analysis, Patrick-FABER test sign was associated with inflammatory lesions of sacroiliitis at MRI and SpA diagnosis. Univariate and multivariate analysis showed an association between smoking status (p=0.01), sacroiliitis, and SpA diagnosis. The odds of having sacroiliitis was 2.7 higher in smokers (OR: 2.7; 95% CI: 1.1-7) as compared to non-smokers and 6.3 higher in those with a positive Patrick-FABER test sign (OR: 6.3; 95%CI: 2.5-15.6) as compared to those with a negative sign. CONCLUSIONS: Our study shows that Patrick-FABER test positivity could represent a useful clinical test for addressing the use of sacroiliac joints MRI and SpA diagnosis in subjects with LBP. Further, smoking habit could represent an associate anamnestic element for addressing the use of sacroiliac MRI.

Paraneoplastic sacroiliitis.

A man in his early 70s presented with stiffness and aching in the shoulder and pelvic girdles. His C reactive protein level was elevated at 116 mg/L, leading to an initial diagnosis of polymyalgia rheumatica. Treatment with prednisone at 20 mg/day provided limited improvement and relapses recurred despite concomitant immunosuppressive agents. Extensive investigations failed to reveal an underlying aetiology.Five years later, gross painless haematuria led to the detection of an invasive papillary urothelial carcinoma. A review of the staging CT scan revealed findings compatible with bilateral erosive sacroiliitis, which had developed since his initial presentation. Radical cystoprostatectomy provided temporary relief but after a further 9 months, symptoms relapsed, and metastatic spread was discovered.Paraneoplastic sacroiliitis is a rare clinical entity; and to the best of our knowledge, this is the first reported case associated with a solid tumour.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.