First Prospective Study of the Recognition Process of Melanoma in Dermatological Practice

Abstract

Early detection is crucial to improve melanoma prognosis. Different diagnostic guides such as the ABCD rule (asymmetry [A], irregularity of borders [B], unevenness of distribution of color [C], and diameter [D]) have been proposed to identify melanoma, but their efficacy in real life is questionable. We investigated the recognition process of melanoma by dermatologists to use as a model to improve self-detection in the general population and to train students and general practitioners. To understand the major principles of the recognition process of nevi and melanoma unconsciously used by dermatologists. Prospective survey recording the immediate perceptions of dermatologists of the morphologic features of the lesion and intuitive diagnostic opinion about 4036 consecutive resected nevi and melanoma. One hundred thirty-five volunteer dermatologists in their daily practices. Perceptions of the image best explaining the diagnostic opinion and best predicting the final diagnosis by univariate and multivariate analysis. The immediate diagnostic opinion of the dermatologist is mainly explained by an unconscious reference to the overall pattern compared with the common nevi, but also compared with the other nevi of the individual (the "ugly duckling sign"). The dermatologist's ability to discriminate between nevi and melanoma relies on the assessment of the overall pattern, the ugly duckling sign, and the knowledge of a recent change. A separate or combined analysis of individual morphologic criteria such as ABCD does not seem to play a major role in this recognition process. Persons most skilled at the clinical detection of melanoma seem to unconsciously rely on cognitive (overall pattern) and comparative (ugly duckling sign) processes rather than an algorithm of morphologic criteria (ABCD). These concepts could be tested in the medical training of general practitioners and education of the general population, where they might be more efficient than algorithms such as the ABCD criteria.