A BEST classification system of large to giant congenital melanocytic nevi based on expert consensus and distribution characteristics.

Background: Large to giant congenital melanocytic nevi (LGCMN) significantly decrease patients' quality of life, but the inaccuracy of current classification system makes their clinical management challenging.

Objectives: To improve and extend the existing LGCMN 6B/7B classification systems by developing a novel LGCMN classification system based on a new phenotypic approach to clinical tool development.

Methods: Three hundred and sixty-one LGCMN cases were categorized into four subtypes based on anatomic site: bonce (25.48%), extremity (17.73%), shawl (19.67%) and trunks (37.12%) LGCMN. A 'BEST' classification system of LGCMN was established and validated by a support vector machine classifier combined with the 7B system.

Results: The most common LGCMN distributions were on bonce and trunks (bathing trunk), whereas breast/belly and body LGCMN were exceptionally rare. Sexual dimorphism characterized distribution, with females showing a wider range of lesions in the genital area. Nearly half of the patients with bathing trunk LGCMN exhibited a butterfly-like distribution. Approximately half of the LGCMN with chest involvement did not have nipple-areola complex involvement. Abdomen, back and buttock involvement was associated with the presence of satellite nevi (r = 0.558), and back and buttock involvement was associated with the presence of nodules (r = 0.364).

Conclusions: The effective quantification of a standardized anatomical site provides data support for the accuracy of the 6B/7B classification systems. The simplified BEST classification system can help establish a LGCMN clinical database for exploration of LGCMN aetiology, disease management and prognosis prediction.