Background: The clinical and dermoscopic diagnosis of facial lentigo maligna (LM) and pigmented actinic keratosis (PAK) remains challenging, particularly at the early disease stages. Objectives: To identify dermoscopic criteria that might be useful to differentiate LM from PAK, and to elaborate and validate an automated diagnostic algorithm for facial LM/PAK. Materials & Methods: We performed a retrospective multicentre study to evaluate dermoscopic images of histologically-proven LM and PAK, and assess previously described dermoscopic criteria. Results: In the first part of the study, 61 cases of LM and 74 PAK were examined and a parsimonious algorithm was elaborated using stepwise discriminant analysis. The following eight dermoscopic criteria achieved the greatest discriminative power: (1) light brown colour; (2) a structureless zone, varying in colour from brown to brown/tan, to black; (3) in-focus, discontinuous brown lines; (4) incomplete brown or grey circles; (5) a structureless brown or black zone, obscuring the hair follicles; (6) a brown (tan), eccentric, structureless zone; (7) a blue structureless zone; and (8) scales. The newly developed algorithm was subsequently validated using an additional series of 110LMand 75 PAKcases. Diagnostic accuracy was 86.5% (k: 0.73, 95% CI: 0.63-0.83). For the diagnosis of LM vs PAK, sensitivity was 82.7% (95% CI: 75.7-89.8%), specificity was 92.0% (95% CI: 85.9-98.1%), positive predictive value was 93.8% (95% CI: 89.0-98.6%), and negative predictive value was 78.4% (95% CI: 68.4-86.5%). Conclusions: This algorithm may represent an additional tool for clinicians to distinguish between facial LM and PAK.

A new dermoscopic algorithm for the differential diagnosis of facial lentigo maligna and pigmented actinic keratosis

Micantonio, Tamara;Antonini, Ambra;Fargnoli, Maria Concetta;
2018-01-01

Abstract

Background: The clinical and dermoscopic diagnosis of facial lentigo maligna (LM) and pigmented actinic keratosis (PAK) remains challenging, particularly at the early disease stages. Objectives: To identify dermoscopic criteria that might be useful to differentiate LM from PAK, and to elaborate and validate an automated diagnostic algorithm for facial LM/PAK. Materials & Methods: We performed a retrospective multicentre study to evaluate dermoscopic images of histologically-proven LM and PAK, and assess previously described dermoscopic criteria. Results: In the first part of the study, 61 cases of LM and 74 PAK were examined and a parsimonious algorithm was elaborated using stepwise discriminant analysis. The following eight dermoscopic criteria achieved the greatest discriminative power: (1) light brown colour; (2) a structureless zone, varying in colour from brown to brown/tan, to black; (3) in-focus, discontinuous brown lines; (4) incomplete brown or grey circles; (5) a structureless brown or black zone, obscuring the hair follicles; (6) a brown (tan), eccentric, structureless zone; (7) a blue structureless zone; and (8) scales. The newly developed algorithm was subsequently validated using an additional series of 110LMand 75 PAKcases. Diagnostic accuracy was 86.5% (k: 0.73, 95% CI: 0.63-0.83). For the diagnosis of LM vs PAK, sensitivity was 82.7% (95% CI: 75.7-89.8%), specificity was 92.0% (95% CI: 85.9-98.1%), positive predictive value was 93.8% (95% CI: 89.0-98.6%), and negative predictive value was 78.4% (95% CI: 68.4-86.5%). Conclusions: This algorithm may represent an additional tool for clinicians to distinguish between facial LM and PAK.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11697/126571
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