Dataset Information

Performance of magnetic resonance imaging in the diagnosis of axial spondyloarthritis: a systematic literature review.

ABSTRACT:

Objectives

To summarize the evidence on the performance of MRI for the diagnosis of axial SpA.

Methods

This was a systematic literature review of all studies from January 2013 to March 2017 including adult patients with clinically suspected axial SpA undergoing MRI. Studies from a previously published systematic literature review up to January 2013 were also included.

Results

Thirty-one studies were included. Six studies demonstrated good sensitivity and specificity for SI joint (SIJ) bone marrow oedema (BMO). Specificity was increased by the presence of other structural lesions alongside BMO, particularly erosions or fat infiltration. Four studies addressed the utility of SIJ fat infiltration, finding good sensitivity but poor specificity. SIJ erosions showed good specificity in five studies. Studies addressing high T1 signal in the SIJ, fluid signal in the SIJ, ankylosis, sclerosis, capsulitis, backfill and vacuum phenomenon reported limited diagnostic value. In the spine, four studies reported moderate sensitivity and specificity for corner inflammatory lesions, and four reported poor sensitivity and specificity for spinal fat infiltration. Five studies evaluated the added value of spinal MRI over SIJ MRI alone, with variable results depending on the cohort. Six studies addressed the effect of acquisition parameters on diagnostic accuracy: fat-saturated T2-weighted imaging and short tau inversion recovery (STIR) imaging showed comparable utility in identifying BMO. Three studies showed that gadolinium was of minimal added value in the detection of BMO.

Conclusions

These results confirmed the diagnostic utility of MRI in axial SpA. Performance varied according to the characteristics of the cohort and the number and combination of MRI lesions considered.

SUBMITTER: Jones A

PROVIDER: S-EPMC6812711 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Performance of magnetic resonance imaging in the diagnosis of axial spondyloarthritis: a systematic literature review.

Jones Alexis A Bray Timothy J P TJP Mandl Peter P Hall-Craggs Margaret A MA Marzo-Ortega Helena H Machado Pedro M PM

Rheumatology (Oxford, England) 20191101 11

<h4>Objectives</h4>To summarize the evidence on the performance of MRI for the diagnosis of axial SpA.<h4>Methods</h4>This was a systematic literature review of all studies from January 2013 to March 2017 including adult patients with clinically suspected axial SpA undergoing MRI. Studies from a previously published systematic literature review up to January 2013 were also included.<h4>Results</h4>Thirty-one studies were included. Six studies demonstrated good sensitivity and specificity for SI ...[more]

PMID: 31046102

Similar Datasets

Project description:BackgroundPatients with inflammatory bowel disease (IBD) have an excess burden of axial spondyloarthritis (axSpA), which, if left untreated, may significantly impact on clinical outcomes. We aimed to estimate the prevalence of axSpA, including previously undiagnosed cases, in IBD patients from studies involving cross-sectional imaging and identify the IBD features potentially associated with axSpA.MethodsPubMed, Embase and Cochrane databases were searched systematically between 1990 and 2018. Article reference lists and key conference abstract lists from 2012 to 2018 were also reviewed. All abstracts were reviewed by two authors to determine eligibility for inclusion. The study inclusion criteria were (a) adults aged 18 years or above, (b) a clinical diagnosis of IBD and (c) reporting identification of sacroiliitis using cross-sectional imaging.ResultsA total of 20 observational studies were identified: 12 used CT, 6 used MR and 2 utilised both computed tomography (CT) and magnetic resonance (MR) imaging. Sample sizes ranged from 25 to 1247 (a total of 4096 patients); 31 studies were considered to have low selection bias, 13 included two or more radiology readers, and 3 included rheumatological assessments. The prevalence of sacroiliitis, the most commonly reported axSpA feature, ranged from 2.2% to 68.0% with a pooled prevalence of 21.0% [95% confidence interval (CI) 17-26%]. Associated IBD features include increasing IBD duration, increasing age, male sex, IBD location, inflammatory back pain and peripheral arthritis. No significant difference in the prevalence of sacroiliitis between Crohn's disease and ulcerative colitis was identified. Study limitations include variability in the individual study sample sizes and patient demographics.ConclusionThis review highlights the need for larger, well-designed studies using more sensitive imaging modalities and multivariable modelling to better estimate the prevalence of axSpA in IBD. An improved knowledge of the IBD phenotype(s) associated with axSpA and use of cross-sectional imaging intended for IBD assessment to screen for axSpA may help clinicians identify those patients most at risk.

Project description:BackgroundMagnetic 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.MethodsThis 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.ResultsA 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.ConclusionAn 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.

Project description:BackgroundAxial spondyloarthritis (axSpA) is frequently diagnosed late, particularly in human leukocyte antigen (HLA)-B27-negative patients, resulting in a missed opportunity for optimal treatment. This study aimed to develop an artificial intelligence (AI) tool, termed NegSpA-AI, using sacroiliac joint (SIJ) magnetic resonance imaging (MRI) and clinical SpA features to improve the diagnosis of axSpA in HLA-B27-negative patients.MethodsWe retrospectively included 454 HLA-B27-negative patients with rheumatologist-diagnosed axSpA or other diseases (non-axSpA) from the Third Affiliated Hospital of Southern Medical University and Nanhai Hospital between January 2010 and August 2021. They were divided into a training set (n=328) for 5-fold cross-validation, an internal test set (n=72), and an independent external test set (n=54). To construct a prospective test set, we further enrolled 87 patients between September 2021 and August 2023 from the Third Affiliated Hospital of Southern Medical University. MRI techniques employed included T1-weighted (T1W), T2-weighted (T2W), and fat-suppressed (FS) sequences. We developed NegSpA-AI using a deep learning (DL) network to differentiate between axSpA and non-axSpA at admission. Furthermore, we conducted a reader study involving 4 radiologists and 2 rheumatologists to evaluate and compare the performance of independent and AI-assisted clinicians.ResultsNegSpA-AI demonstrated superior performance compared to the independent junior rheumatologist (≤5 years of experience), achieving areas under the curve (AUCs) of 0.878 [95% confidence interval (CI): 0.786-0.971], 0.870 (95% CI: 0.771-0.970), and 0.815 (95% CI: 0.714-0.915) on the internal, external, and prospective test sets, respectively. The assistance of NegSpA-AI promoted discriminating accuracy, sensitivity, and specificity of independent junior radiologists by 7.4-11.5%, 1.0-13.3%, and 7.4-20.6% across the 3 test sets (all P<0.05). On the prospective test set, AI assistance also improved the diagnostic accuracy, sensitivity, and specificity of independent junior rheumatologists by 7.7%, 7.7%, and 6.9%, respectively (all P<0.01).ConclusionsThe proposed NegSpA-AI effectively improves radiologists' interpretations of SIJ MRI and rheumatologists' diagnoses of HLA-B27-negative axSpA.

Project description:Identification of axial spondyloarthritis (axSpA) remains challenging, frequently resulting in a diagnostic delay for patients. Current benchmarks of delay are usually reported as mean data, which are typically skewed and therefore may be overestimating delay. Our aim was to determine the extent of median delay patients' experience in receiving a diagnosis of axSpA and examine whether specific factors are associated with the presence of such delay. We conducted a systematic review across five literature databases (from inception to November 2021), with studies reporting the average time period of diagnostic delay in patients with axSpA being included. Any additional information examining associations between specific factors and delay were also extracted. A narrative synthesis was used to report the median range of diagnostic delay experienced by patients with axSpA and summarise which factors have a role in the delay. From an initial 11,995 articles, 69 reported an average time period of diagnostic delay, with 25 of these providing a median delay from symptom onset to diagnosis. Across these studies, delay ranged from 0.67 to 8 years, with over three-quarters reporting a median of between 2 years and 6 years. A third of all studies reported median delay data ranging from just 2 to 2.3 years. Of seven variables reported with sufficient frequency to evaluate, only 'gender' and 'family history of axSpA' had sufficient concordant data to draw any conclusion on their role, neither influenced the extent of the delay. Despite improvements in recent decades, patients with axSpA frequently experience years of diagnostic delay and this remains an extensive worldwide problem. This is further compounded by a mixed picture of the disease, patient and healthcare-related factors influencing delay. Key points • Despite improvements in recent decades, patients with axSpA frequently experience years of diagnostic delay. • Median diagnostic delay typically ranges from 2 to 6 years globally. • Neither 'gender' nor 'family history of axSpA' influenced the extent of diagnostic delay experienced. • Diagnostic delay based on mean, rather than median, data influences the interpretation of the delay time period and consistently reports a longer delay period.

Project description:ContextThere is a lack of comprehensive data regarding the factors that influence the diagnostic accuracy of multiparametric magnetic resonance imaging (mpMRI) to detect and localize clinically significant prostate cancer (csPCa).ObjectiveTo systematically review the current literature assessing the factors influencing the variability of mpMRI performance in csPCa diagnosis.Evidence acquisitionA computerized bibliographic search of Medline/PubMed database was performed for all studies assessing magnetic field strength, use of an endorectal coil, assessment system used by radiologists and inter-reader variability, experience of radiologists and urologists, use of a contrast agent, and use of computer-aided diagnosis (CAD) tools in relation to mpMRI diagnostic accuracy.Evidence synthesisA total of 77 articles were included. Both radiologists' reading experience and urologists'/radiologists' biopsy experience were the main factors that influenced diagnostic accuracy. Therefore, it is mandatory to indicate the experience of the interpreting radiologists and biopsy-performing urologists to support the reliability of the findings. The most recent Prostate Imaging Reporting and Data System (PI-RADS) guidelines are recommended for use as the main assessment system for csPCa, given the simplified and standardized approach as well as its particular added value for less experienced radiologists. Biparametric MRI had similar accuracy to mpMRI; however, biparametric MRI performed better with experienced readers. The limited data available suggest that the combination of CAD and radiologist readings may influence diagnostic accuracy positively.ConclusionsMultiple factors affect the accuracy of mpMRI and MRI-targeted biopsy to detect and localize csPCa. The high heterogeneity across the studies underlines the need to define the experience of radiologists and urologists, implement quality control, and adhere to the most recent PI-RADS assessment guidelines. Further research is needed to clarify which factors impact the accuracy of the MRI pathway and how.Patient summaryWe systematically reported the factors influencing the accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting clinically significant prostate cancer (csPCa). These factors are significantly related to each other, with the experience of the radiologists being the dominating factor. In order to deliver the benefits of mpMRI to diagnose csPCa, it is necessary to develop expertise for both radiologists and urologists, implement quality control, and adhere to the most recent Prostate Imaging Reporting and Data System assessment guidelines.

Dataset Information

Performance of magnetic resonance imaging in the diagnosis of axial spondyloarthritis: a systematic literature review.

Objectives

Methods

Results

Conclusions

Publications

Performance of magnetic resonance imaging in the diagnosis of axial spondyloarthritis: a systematic literature review.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets