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Dermoscopy Aids in the Diagnosis of Discoid Lupus Erythematosus
Published on: May 16, 2025
A comparative study of melanocytic nevi classification with dermoscopy and high-frequency ultrasound
Yu-Kun Wang1, Yuan-Jing Gao2, Jie Liu1
1Department of Dermatology, Peking Union Medical College Hospital, Center for Translational Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background:
Melanocytic nevi (MN) can be classified into three subtypes according to the depth of the nests of nevus cells which is important for management. High-frequency ultrasound (HF-US) can clearly reveal the lesion size, contour, depth, and internal structures. However, the HF-US studies of MN according to subtypes are limited. We aimed to describe the HF-US features of MN and explore its value in accurate classification.
Materials And Methods:
This retrospective study was conducted from January 2018 to November 2019. Eighty-five patients with MN were included and examined by 50 and 20 MHz HF-US. The HF-US features were recorded including morphological flatness, depth, shape, boundary, internal echogenicity, hyperechoic spots, lateral acoustic shadow, posterior echoic patterns, mushroom signs, and straw-hat signs. Each image was evaluated by two physicians independently, and the consistency was tested.
Results:
Eleven lesions could not be detected by HF-US. The rest 74 lesions underwent ultrasonic analysis. MN appeared as strip-shaped or oval, hypoechoic areas localized in the epidermis and dermis under ultrasonography. A strong consistency between HF-US and dermoscopy of determining the lesion depth was achieved (κ = 0.935, p < 0.001). The hyperechoic spots were found in 57.6% intradermal nevi. The mushroom signs were seen in 34.8% intradermal nevi, and the straw-hat signs were seen in all the compound nevi.
Conclusion:
MN can be correctly classified using HF-US, and it had a strong correlation with dermoscopic and clinical classification. HF-US could further reveal the internal morphological features of MN, which may support more precise classification and management.
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