Image and statistical analysis of melanocytic histology

Aims: We applied digital image analysis techniques to study selected types of melanocytic lesions. Methods and results: We used advanced digital image analysis to compare melanocytic lesions as follows: (i) melanoma to nevi, (ii) melanoma subtypes to nevi, (iii) severely dysplastic nevi to other nevi and (iv) melanoma to severely dysplastic nevi. We were successful in differentiating melanoma from nevi [receiver operating characteristic area (ROC) 0.95] using image-derived features, among which those related to nuclear size and shape and distance between nuclei were most important. Dividing melanoma into subtypes, even greater separation was obtained (ROC area 0.98 for superficial spreading melanoma; 0.95 for lentigo maligna melanoma; and 0.99 for unclassified). Severely dysplastic nevi were best differentiated from conventional and mildly dysplastic nevi by differences in cellular staining qualities (ROC area 0.84). We found that melanomas were separated from severely dysplastic nevi by features related to shape and staining qualities (ROC area 0.95). All comparisons were statistically significant (P<0.0001). Conclusions: We offer a unique perspective into the evaluation of melanocytic lesions and demonstrate a technological application with increasing prevalence, and with potential use as an adjunct to traditional diagnosis in the future.