OBJECTIVE: To determine the predictive dermoscopic features of amelanotic and hypomelanotic melanoma. DESIGN: A total of 105 melanomas (median Breslow thickness, 0.76 mm), 170 benign melanocytic lesions, and 222 nonmelanocytic lesions lacking significant pigment (amelanotic, partially pigmented, and light colored) were imaged using glass-plate dermoscopy devices and scored for 99 dermoscopic features. Diagnostic models were derived from and tested on independent randomly selected lesions. SETTING: Predominantly hospital-based clinics from 5 continents. MAIN OUTCOME MEASURES: Sensitivity, specificity, and odds ratios for individual features and models for the diagnosis of melanoma and malignancy. RESULTS: The most significant negative predictors of melanoma were having multiple (>3) milialike cysts (odds ratio, 0.09; 95% confidence interval, 0.01-0.64), comma vessels with a regular distribution (0.10; 0.01-0.70), comma vessels as the predominant vessel type (0.16; 0.05-0.52), symmetrical pigmentation pattern (0.18; 0.09-0.39), irregular blue-gray globules (0.20; 0.05-0.87), and multiple blue-gray globules (0.28; 0.10-0.81). The most significant positive predictors were having a blue-white veil (odds ratio,13; 95% confidence interval, 3.9-40.0), scarlike depigmentation (4.4; 2.4-8.0), multiple blue-gray dots (3.5; 1.9-6.4), irregularly shaped depigmentation (3.3; 2.0-5.3), irregular brown dots/globules (3.2; 1.8-5.6), 5 to 6 colors (3.2; 1.6-6.3), and predominant central vessels (3.1; 1.6-6.0). A simple model distinguishing melanomas from all nonmelanomas had a sensitivity of 70% and a specificity of 56% in the test set. A model distinguishing all malignant lesions from benign lesions had a sensitivity of 96% and a specificity of 37%. Conclusion Although the diagnostic accuracy of dermoscopy for melanoma lacking significant pigment is inferior to that of more pigmented lesions, features distinguishing the former from benign lesions can be visualized on dermoscopic evaluation.
OBJECTIVE: To determine the predictive dermoscopic features of amelanotic and hypomelanotic melanoma. DESIGN: A total of 105 melanomas (median Breslow thickness, 0.76 mm), 170 benign melanocytic lesions, and 222 nonmelanocytic lesions lacking significant pigment (amelanotic, partially pigmented, and light colored) were imaged using glass-plate dermoscopy devices and scored for 99 dermoscopic features. Diagnostic models were derived from and tested on independent randomly selected lesions. SETTING: Predominantly hospital-based clinics from 5 continents. MAIN OUTCOME MEASURES: Sensitivity, specificity, and odds ratios for individual features and models for the diagnosis of melanoma and malignancy. RESULTS: The most significant negative predictors of melanoma were having multiple (>3) milialike cysts (odds ratio, 0.09; 95% confidence interval, 0.01-0.64), comma vessels with a regular distribution (0.10; 0.01-0.70), comma vessels as the predominant vessel type (0.16; 0.05-0.52), symmetrical pigmentation pattern (0.18; 0.09-0.39), irregular blue-gray globules (0.20; 0.05-0.87), and multiple blue-gray globules (0.28; 0.10-0.81). The most significant positive predictors were having a blue-white veil (odds ratio,13; 95% confidence interval, 3.9-40.0), scarlike depigmentation (4.4; 2.4-8.0), multiple blue-gray dots (3.5; 1.9-6.4), irregularly shaped depigmentation (3.3; 2.0-5.3), irregular brown dots/globules (3.2; 1.8-5.6), 5 to 6 colors (3.2; 1.6-6.3), and predominant central vessels (3.1; 1.6-6.0). A simple model distinguishing melanomas from all nonmelanomas had a sensitivity of 70% and a specificity of 56% in the test set. A model distinguishing all malignant lesions from benign lesions had a sensitivity of 96% and a specificity of 37%. Conclusion Although the diagnostic accuracy of dermoscopy for melanoma lacking significant pigment is inferior to that of more pigmented lesions, features distinguishing the former from benign lesions can be visualized on dermoscopic evaluation.
Authors: Alon Scope; Michael A Marchetti; Ashfaq A Marghoob; Stephen W Dusza; Alan C Geller; Jaya M Satagopan; Martin A Weinstock; Marianne Berwick; Allan C Halpern Journal: J Am Acad Dermatol Date: 2016-06-17 Impact factor: 11.527
Authors: A Blum; J Kreusch; W Stolz; H Haenssle; R Braun; R Hofmann-Wellenhof; P Tschandl; I Zalaudek; H Kittler Journal: Hautarzt Date: 2017-08 Impact factor: 0.751
Authors: Philipp Tschandl; Cliff Rosendahl; Bengu Nisa Akay; Giuseppe Argenziano; Andreas Blum; Ralph P Braun; Horacio Cabo; Jean-Yves Gourhant; Jürgen Kreusch; Aimilios Lallas; Jan Lapins; Ashfaq Marghoob; Scott Menzies; Nina Maria Neuber; John Paoli; Harold S Rabinovitz; Christoph Rinner; Alon Scope; H Peter Soyer; Christoph Sinz; Luc Thomas; Iris Zalaudek; Harald Kittler Journal: JAMA Dermatol Date: 2019-01-01 Impact factor: 10.282