In vivo reflectance confocal microscopy can detect the invasive component of lentigo maligna melanoma: Prospective analysis and case-control study.

Gouveia BM, Carlos G, Wadell A, Sinz C, Ahmed T, Lo SN, Rawson RV, Ferguson PM, Scolyer RA, Guitera P. J Eur Acad Dermatol Venereol. 2023 Feb 28. doi: 10.1111/jdv.18998. Epub ahead of print. PMID: 36855833.


Background: Lentigo maligna (LM), a form of melanoma in situ, has no risk of causing metastasis unless dermal invasive melanoma (LMM) supervenes. Furthermore, the detection of invasion impacts prognosis and management.

Objective: To assess the accuracy of RCM for the detection of invasion component on LM/LMM lesions.

Methods: In the initial case-control study, the performance of one expert in detecting LMM at the time of initial RCM assessment of LM/LMM lesions was recorded prospectively (n = 229). The cases were assessed on RCM-histopathology correlation sessions and a panel with nine RCM features was proposed to identify LMM, which was subsequently tested in a subset of initial cohort (n = 93) in the matched case-control study by two blinded observers. Univariable and multivariable logistic regression models were performed to evaluate RCM features predictive of LMM. Reproducibility of assessment of the nine RCM features was also evaluated.

Results: A total of 229 LM/LMM cases evaluated by histopathology were assessed blindly and prospectively by an expert confocalist. On histopathology, 210 were LM and 19 were LMM cases. Correct identification of an invasive component was achieved for 17 of 19 LMM cases (89%) and the absence of a dermal component was correctly diagnosed in 190 of 210 LM cases (90%). In the matched case-control (LMM n = 35, LM n = 58), epidermal and junctional disarray, large size of melanocytes and nests of melanocytes were independent predictors of LMM on multivariate analysis. The interobserver analysis demonstrated that these three features had a fair reproducibility between the two investigators (K = 0.4). The multivariable model including those three features showed a high predictive performance AUC = 74% (CI 95% 64-85%), with sensitivity of 63% (95% CI 52-78%) and specificity of 79% (CI 95% 74-88%), and likelihood ratio of 18 (p-value 0.0026).

Conclusion: Three RCM features were predictive for identifying invasive melanoma in the background of LM.