Distinguishing Takayasu Arteritis and Giant Cell Arteritis Based on Large-Vessel Involvement Patterns

Purpose Takayasu arteritis (TAK) and extracranial large-vessel (LV) giant cell arteritis (GCA) share overlapping features, making differential diagnosis between the two diseases challenging. We aimed to identify LV involvement patterns that could accurately differentiate TAK and GCA. Materials and Methods This retrospective cohort study included 181 patients (TAK, n=175; GCA, n=6). LV involvement patterns were assessed using computed tomography (CT) and/or 18F-fluorodeoxyglucose positron emission tomography/CT performed at diagnosis. A multivariable logistic regression model was used to identify LV involvement patterns that accurately distinguish TAK and GCA. Area under the curve (AUC) was estimated to determine the accuracy. Results The right subclavian artery (30.3% vs. 83.3%, p=0.013), aortic arch (13.7% vs. 83.3%, p<0.001), descending aorta (30.3% vs. 100.0%, p=0.001), and abdominal aorta (30.9% vs. 83.3%, p=0.015) were less commonly involved in TAK than in GCA. When categorized according to Hata’s classification and clusters, type V (31.4% vs. 83.3%, p=0.016) and cluster 5 (2.3% vs. 83.3%, p<0.001) were less common in TAK than in GCA. Type V demonstrated an AUC of 0.760, whereas cluster 5 showed higher accuracy (AUC=0.905) in distinguishing TAK and GCA. A combination of right subclavian artery and aortic arch involvement (2.358 × right subclavian artery involvement+3.385 × aortic arch involvement; cut-off=2.872), derived from the multivariable logistic regression model, yielded the highest accuracy (AUC=0.925). Conclusion Distinct patterns of LV involvement, particularly aortic arch involvement, either alone or combined with right subclavian artery involvement, could accurately differentiate TAK and GCA.

• Publication year

  2025

• Venue

  Yonsei medical journal

• Publication date

  2025-11-06

• Fields of study

  Medicine

• Identifiers
  DOI 10.3349/ymj.2025.0073PMID 41560383PMCID 12880768
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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