TY - JOUR
T1 - Optical spectroscopy as a method for skin cancer risk assessment
AU - Rodriguez-Diaz, Eladio
AU - Manolakos, Danielle
AU - Christman, Holly
AU - Bonning, Michael A.
AU - Geisse, John K.
AU - A'Amar, Ousama M.
AU - Leffell, David J.
AU - Bigio, Irving J.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85070962019&partnerID=8YFLogxK
U2 - 10.1111/php.13140
DO - 10.1111/php.13140
M3 - Article
C2 - 31287160
AN - SCOPUS:85070962019
SN - 0031-8655
VL - 95
SP - 1441
EP - 1445
JO - Photochemistry and Photobiology
JF - Photochemistry and Photobiology
IS - 6
ER -