Publication date: Jul 09, 2019
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 analyzedin 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. This article is protected by copyright. All rights reserved.
Rodriguez-Diaz, E., Manolakos, D., Christman, H., Bonning, M.A., Geisse, J.K., A’Amar, O.M., Leffell, D.J., and Bigio, I.J. Optical Spectroscopy as a Method for Skin Cancer Risk Assessment. 23321. 2019 Photochem Photobiol.
- Enhanced classifier training to improve precision of a convolutional neural network to identify images of skin lesions.
- Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.