Validity of high resolution magnetic resonance imaging in detecting giant cell arteritis: a meta-analysis
Ke-Jia Zhang1,2, Ming-Xi Li1,2, Peng Zhang1,2
1China National Comprehensive Stroke Center, Department of Neurology, the First Hospital of Jilin University, Changchun, Jilin, China.
Objectives:
This study was designed to evaluate the performance of high-resolution magnetic resonance imaging (HR-MRI) in detecting giant cell arteritis (GCA), evaluate superficial extracranial artery and other MRI abnormalities, and compare three-dimensional (3D) and two-dimensional (2D) techniques.
Methods:
PubMed, Web of Science, and Cochrane Library were screened up to March 7, 2021, and further selection was performed according to the eligibility criteria. Quality Assessment of Diagnostic Accuracy Studies-2 was used for quality assessment, and heterogeneity assessment and statistical calculations were also performed.
Results:
In total, 1851 records were retrieved from online databases, and 15 studies were finally included. Regarding the performance of HR-MRI, the superficial extracranial artery had 75% sensitivity and 89% specificity, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.91. Positive and negative post-test possibilities were 86% and 20%, respectively, with clinical diagnosis as reference. When referenced with temporal artery biopsy, the sensitivity was 91%, specificity was 78%, AUC was 0.92, and positive and negative post-test possibilities were 78% and 10%, respectively. 3D HR-MRI and 2D HR-MRI had 70% and 72% sensitivity, respectively, and 91% and 84% specificity, respectively.
Conclusions:
HR-MRI is a valuable imaging modality for GCA diagnosis. It provided high accuracy in the diagnosis of GCA and played a potential role in identifying GCA-related ischemic optic neuropathy. 3D HR-MRI had better specificity than 2D HR-MRI.
Key Points:
HR-MRI helps clinicians to diagnose GCA. Superficial extracranial arteries and other MRI abnormalities can be assessed with HR-MRI. HR-MRI can help in assessing GCA-related optic neuropathy.
Insights
High-resolution magnetic resonance imaging (HR-MRI) is effective for diagnosing giant cell arteritis (GCA), showing high accuracy and potential for identifying related optic neuropathy. Three-dimensional HR-MRI offers better specificity than two-dimensional techniques.
Area of Science:
- Radiology
- Vascular Imaging
- Rheumatology
Background:
- Giant cell arteritis (GCA) is a systemic vasculitis affecting large and medium-sized arteries.
- Accurate diagnosis of GCA is crucial to prevent complications such as ischemic optic neuropathy.
- High-resolution magnetic resonance imaging (HR-MRI) has emerged as a promising tool for evaluating GCA.
Purpose of the Study:
- To evaluate the diagnostic performance of HR-MRI for GCA.
- To assess HR-MRI's ability to detect superficial extracranial artery abnormalities and other MRI findings in GCA.
- To compare the efficacy of three-dimensional (3D) versus two-dimensional (2D) HR-MRI techniques in GCA diagnosis.
Main Methods:
- A systematic literature search was conducted across PubMed, Web of Science, and Cochrane Library up to March 7, 2021.
- Included studies were assessed for quality using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
- Statistical analyses, including heterogeneity assessment, were performed to synthesize the findings.
Main Results:
- The analysis included 15 studies from 1851 retrieved records.
- HR-MRI demonstrated a sensitivity of 75% and specificity of 89% for superficial extracranial arteries against clinical diagnosis (AUC=0.91).
- Compared to temporal artery biopsy, HR-MRI showed 91% sensitivity and 78% specificity (AUC=0.92). 3D HR-MRI had higher specificity (91%) than 2D HR-MRI (84%).
Conclusions:
- HR-MRI is a valuable imaging modality for the diagnosis of GCA.
- HR-MRI can accurately detect abnormalities in superficial extracranial arteries and aid in identifying GCA-related ischemic optic neuropathy.
- Three-dimensional HR-MRI demonstrates superior specificity compared to two-dimensional techniques for GCA evaluation.
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