Real-time, In Vivo Skin Cancer Triage by Laser-Induced Plasma Spectroscopy Combined with Deep Learning-based Diagnostic Algorithm.

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Abstract

Although various skin cancer detection devices have been proposed, most of them are not used owing to their insufficient diagnostic accuracies. Laser-induced plasma spectroscopy (LIPS) can noninvasively extract biochemical information of skin lesions using an ultrashort pulsed laser.To investigate the diagnostic accuracy and safety of a real-time noninvasive in vivo skin cancer diagnostics utilizing non-discrete molecular LIPS combined with a deep neural network (DNN)-based diagnostic algorithm.In vivo LIPS spectra were acquired from 296 skin cancers (186 BCCs, 96 SCCs and 14 melanomas) and 316 benign lesions in a multisite clinical study. The diagnostic performance was validated using 10-fold cross-validations.The sensitivity and specificity for differentiating skin cancers from benign lesions using LIPS and the DNN-based algorithm were 94.6% (95% CI: 92.0 – 97.2%) and 88.9% (95% CI: 85.5 – 92.4%), respectively. No adverse events, including macroscopic or microscopic visible marks or pigmentation due to laser irradiation, were observed.The diagnostic performance was evaluated using a limited dataset. More extensive clinical studies are needed to validate these results.This LIPS system with a DNN-based diagnostic algorithm is a promising tool to distinguish skin cancers from benign lesions with high diagnostic accuracy in real clinical settings.Copyright © 2022. Published by Elsevier Inc.