Skin cancer is the most prevalent cancer, and its assessment remains a challenge for physicians. This study reports the application of an optical sensing method, elastic scattering spectroscopy (ESS), coupled with a classifier that was developed with machine learning, to assist in the discrimination of skin lesions that are concerning for malignancy. The method requires no special skin preparation, is non‐invasive, easy to administer with minimal training, and allows rapid lesion classification. This novel approach was tested for all common forms of skin cancer. ESS spectra from a total of 1307 lesions were analyzed in a multi‐center, non‐randomized clinical trial. The classification algorithm was developed on a 950‐lesion training dataset, and its diagnostic performance was evaluated against a 357‐lesion testing dataset that was independent of the training dataset. The observed sensitivity was 100% (14/14) for melanoma and 94% (105/112) for non‐melanoma skin cancer. The overall observed specificity was 36% (84/231). ESS has potential, as an adjunctive assessment tool, to assist physicians to differentiate between common benign and malignant skin lesions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.