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Limited studies have reported the in vivo reflectance confocal microscopy (RCM) features of lentigo maligna (LM). A total of 64 RCM features were scored retrospectively and blinded to diagnosis in a consecutive series of RCM sampled, clinically equivocal, macules of the face (n=81 LM, n=203 benign macules (BMs)). In addition to describing RCM diagnostic features for LM (univariate), an algorithm was developed (LM score) to distinguish LM from BM. This comprised two major features each scoring +2 points (nonedged papillae and round large pagetoid cells > 20 microm), and four minor features; three scored +1 point each (three or more atypical cells at the dermoepidermal junction in five 0.5 x 0.5 mm(2) fields, follicular localization of atypical cells, and nucleated cells within the dermal papillae), and one (negative) feature scored -1 point (a broadened honeycomb pattern). A LM score of > or = 2 resulted in a sensitivity of 85% and specificity of 76% for the diagnosis of LM (odds ratio (OR) for LM 18.6; 95% confidence interval: 9.3-37.1). The algorithm was equally effective in the diagnosis of amelanotic lesions and showed good interobserver reproducibility (87%). In a test set of 29 LMs and 44 BMs, the OR for LM was 60.7 (confidence interval: 11.9-309) (93% sensitivity, 82% specificity).

We describe two algorithms to diagnose basal cell carcinomas (BCCs) and melanomas (MMs) using in vivo reflectance confocal microscopy (RCM). A total of 710 consecutive cutaneous lesions excised to exclude malignancy (216 MMs, 266 nevi, 119 BCCs, 67 pigmented facial macules, and 42 other skin tumors) were imaged by RCM. RCM features were correlated with pathology diagnosis to develop diagnostic algorithms. The diagnostic accuracy of the BCC algorithm defined on multivariate analysis of the training set (50%) and tested on the remaining cases was 100% sensitivity, 88.5% specificity. Positive features were polarized elongated features, telangiectasia and convoluted vessels, basaloid nodules, and epidermal shadowing corresponding to horizontal clefting. Negative features were non-visible papillae, disarrangement of the epidermal layer, and cerebriform nests. Multivariate discriminant analysis on the training set (excluding the BCCs) identified seven independently significant features for MM diagnosis. The diagnostic accuracy of the MM algorithm on the test set was 87.6% sensitivity, 70.8% specificity. The four invasive MMs that were misdiagnosed by RCM were all of nevoid subtype. RCM is a highly accurate non-invasive technique for BCC diagnosis. Good diagnostic accuracy was achieved also for MM diagnosis, although rare variants of melanocytic tumors may limit the strict application of the algorithm.

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