Dermatology practical & conceptual · 2024
Dermoscopic Features of Cutaneous Vasculitis.
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Bakay Ozge Sevil Karstarli, Kacar Nida, Gonulal Melis, Demirkan Nese Calli, Cenk Hülya, Goksin Sule, Gural Yunus
Аннотация
INTRODUCTION: Dermoscopy has become widespread in the diagnosis of inflammatory skin diseases. 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.
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.