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Original Investigation
February 8, 2017

Ugly Duckling Sign as a Major Factor of Efficiency in Melanoma Detection

Author Affiliations
  • 1Dermatology and Skin Cancer Department, Hôpital Timone, Marseille, France
  • 2Laboratory of Sciences of Information and Systems, Aix-Marseille University, Marseille, France
  • 3Department of Dermatology, The Hertford British Hospital Charity, Levallois-Perret, France
  • 4Department of Dermatology, Lyon Sud Hospital, Pierre Bénite, France
  • 5Department of Dermatology, University of Modena and Reggio Emilia, Modena, Italy
  • 6Department of Dermatology, Hospital Clinic of Barcelona, Barcelona, Spain
  • 7Department of Dermatology, Assistance Publique des Hôpitaux de Paris, Hospital Cochin, University Paris Descartes, Paris, France
JAMA Dermatol. Published online February 8, 2017. doi:10.1001/jamadermatol.2016.5500
Key Points

Question  What is the effect of intrapatient comparative analysis using the ugly duckling sign on diagnosis of melanoma?

Findings  In this experimental study, images of all nevi of 80 patients, including 7 patients with a melanoma, were presented to 9 dermatologists during 2 separate experiments. All melanoma were labeled ugly duckling nevi and access to intrapatient comparative analysis reduced the potential biopsy of nevi by a factor of 6.9.

Meaning  Intrapatient comparative analysis improves the effectiveness of the diagnosis of melanoma.


Importance  Understanding the contribution of the ugly duckling sign (a nevus that is obviously different from the others in a given individual) in intrapatient comparative analysis (IPCA) of nevi may help improve the detection of melanoma.

Objectives  To assess the agreement of dermatologists on identification of the ugly duckling sign and estimate the contribution of IPCA to the diagnosis of melanoma.

Design, Setting, and Participants  The same 2089 digital images of the nevi of a sample of 80 patients (mean age, 42 years [range, 19-80 years]; 33 men and 47 women), as well as 766 dermoscopic images from a subset of 30 patients (mean age, 40 years [range, 21-75 years]; 12 men and 18 women), were randomly presented to the same 9 dermatologists for blinded assessment from September 22, 2011, to April 1, 2013. The first experiment was designed to mimic an IPCA situation, with images of all nevi of each patient shown to the dermatologists, who were asked to identify ugly duckling nevi (UDN). The second experiment was designed to mimic a lesion-focused analysis to identify morphologically suspicious nevi. Data analysis was conducted from November 1, 2012, to June 1, 2013.

Main Outcomes and Measures  Number of nevi labeled UDN and morphologically suspicious nevi, specificity of lesion-focused analysis and IPCA, and number of nevi identified for biopsy.

Results  Of the 2089 clinical images of nevi from 80 patients (median number of nevi per patient, 26 [range, 8-81]) and 766 dermoscopic images (median number of nevi per patient, 19 [range, 8-81]), all melanomas were labeled UDN and as morphologically suspicious nevi by the 9 dermatologists. The median number of UDN detected per patient was 0.8 among the clinical images of nevi (mean, 1.0; range, 0.48-2.03) and 1.26 among the dermoscopic images (mean, 1.4; range, 1.00-2.06). The propensity to consider more or fewer nevi as having ugly duckling signs was independent of the presentation (clinical or dermoscopic). The agreement among the dermatologists regarding UDN was lower with dermoscopic images (mean pairwise agreement, 0.53 for clinical images and 0.50 for dermoscopic images). The specificity of IPCA was 0.96 for clinical images and 0.95 for dermoscopic images vs 0.88 and 0.85, respectively, for lesion-focused analysis. When both IPCA and lesion-focused analyses were used, the number of nevi considered for biopsy was reduced by a factor of 6.9 compared with lesion-focused analysis alone.

Conclusions and Relevance  Intrapatient comparative analysis is of major importance to the effectiveness of the diagnosis of melanoma. Introducing IPCA using the ugly duckling sign in computer-assisted diagnosis systems would be expected to improve performance.