We present the initial findings of two ML algorithms developed to automate reflectance confocal microscopy (RCM) of skin. On a retrospective test set of 141 pigmented lesions collected at MSKCC between 2011 and 2020, our DEJ detection algorithm identified the DEJ with a median precision of 3 “slices”. The algorithm was less precise on melanomas and on facial lesions. On a retrospective test set of 302 RCM mosaics, the segmentation algorithm identified nonspecific patterns with a sensitivity of 0.75 and specificity of 0.79. Prospectively, on 31 benign pigmented lesions, the DEJ detection algorithm was performed with a median precision of 6.18µm.
There is an urgent need for predictive platforms for response to immunotherapy in patients. In vivo phenotyping of tumor-immune microenvironment (TiME) for predicting response to immunotherapy was evaluated using non-invasive reflectance confocal microscopy (RCM) in skin cancer patients. Phenotypes were correlated with underlying biology and response to topical immunotherapy. Using both inflammation and vasculature features, four major phenotypes were observed. The VaschiInfhi phenotype correlated with high immune activation, exhaustion, and vascular signatures while VaschiInflo with endothelial anergy and immune exclusion. Highest response to immunotherapy was seen in VascloInfhi phenotype. This study establishes proof-of-concept for in vivo TiME phenotyping in patients.
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