Assessment of a Predictive Scoring Model for Dermoscopy of Subungual Melanoma In Situ

    loading  Checking for direct PDF access through Ovid



Subungual melanoma in situ (SMIS) is a malignant neoplasm that requires early diagnosis and complete surgical excision; however, little is known about the usefulness of the detailed dermoscopic features of longitudinal melanonychia (LM) to predict the diagnosis of SMIS.


To investigate the characteristic dermoscopic findings of SMIS and to establish a predictive scoring model for the diagnosis of SMIS in patients with adult-onset LM affecting a single digit.

Design, Setting, and Participants

A cohort study of 19 patients with biopsy-proven SMIS and 26 patients with benign LM diagnosed in a tertiary referral hospital in Seoul, South Korea, from September 1, 2013, to July 31, 2017.

Main Outcomes and Measures

Patient demographics, frequency of specific dermoscopic findings, and a predictive scoring model.


Of the total 45 patients with pigmented nails, the 19 patients with SMIS included 14 women and had a mean (SD) age of 52.0 (14.4) years, and the 26 patients with benign LM included 18 women and had a mean (SD) age of 48.1 (13.2) years. Asymmetry (odds ratio [OR], 34.00; 95% CI, 3.88-297.70), border fading (OR, 9.33; 95% CI, 2.37-36.70), multicolor (OR, 11.59; 95% CI, 2.21-60.89), width of the pigmentation of at least 3 mm (OR, 5.31; 95% CI, 1.01-28.07), and presence of the Hutchinson sign (OR, 18.18; 95% CI, 2.02-163.52) were features of LM that were significantly associated with SMIS. A predictive scoring model incorporating these dermoscopic features of SMIS was assessed. The model, ranging from 0 to 8 points, showed a reliable diagnostic value (the receiver operating characteristic curve had an area under the curve [C statistic] of 0.91) in differentiating SMIS from benign LM at a cutoff value of 3, with a sensitivity of 89% and a specificity of 62%.

Conclusions and Relevance

This study suggests characteristic dermoscopic features for SMIS. A predictive scoring model based on these morphologic features may help differentiate SMIS from benign LM.

Related Topics

    loading  Loading Related Articles