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

Importance: 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.

Objectives: 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.

Results: 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.