Dermoscopic Features of Cutaneous Vasculitis

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Bakay O. S. K., Kacar N., GÖNÜLAL M., Demirkan N. C., Cenk H., Goksin S., ...Daha Fazla

Dermatology Practical and Conceptual, cilt.14, sa.1, 2024 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.5826/dpc.1401a51
  • Dergi Adı: Dermatology Practical and Conceptual
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Dermoscopy, machine learning, cutaneous vasculitis, inflammoscopy
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Sağlık Bilimleri Üniversitesi Adresli: Evet

Özet

Introduction: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. ABSTRACT Cutaneous vasculitis (CV) is characterized by inflammation of vessels, and a rapid and reliable technique is required for the diagnosis. Objectives: We aimed to define CV dermoscopic features and increase the diagnostic accuracy of dermoscopy with machine learning (ML) methods. Methods: Eighty-nine patients with clinically suspected CV were included in the study. Dermoscopic images were obtained before biopsy using a polarized dermoscopy. Dermoscopic images were independently evaluated, and interobserver variability was calculated. Decision Tree, Random Forest, and K-Nearest Neighbors were used as ML classification models. Results: The histopathological diagnosis of 58 patients was CV. Three patterns were observed: homogeneous pattern, mottled pattern, and meshy pattern. There was a significant difference in background color between the CV and non-CV groups (P = 0.001). The milky red and livedoid background color were specific markers in the differential diagnosis of CV (sensitivity 56.7%, specificity 96.3%, sensitivity 29.4%, specificity 99.2%, respectively). Red blotches were significantly more common in CV lesions (P = 0.038). Red dots, comma vessels, and scales were more common in the non-CV group (P = 0.002, P = 0.002, P = 0.003, respectively). Interobserver agreement was very good for both pattern (κ = 0.869) and background color analysis (κ = 0.846) (P < 0.001). According to ML classifiers, the background color and lack of scales were the most significant dermoscopic aspects of CV. Conclusions: Dermoscopy may guide as a rapid and reliable technique in CV diagnosis. High accuracy rates obtained with ML methods may increase the success of dermoscopy.