Digital imaging biomarkers feed machine learning for melanoma screening. Issue 7 (19th December 2016)

• Record Type: Journal Article
• Authors: Gareau, Daniel S.; Correa da Rosa, Joel; Yagerman, Sarah; Carucci, John A.; Gulati, Nicholas; Hueto, Ferran; DeFazio, Jennifer L.; Suárez‐Fariñas, Mayte; Marghoob, Ashfaq; Krueger, James G.
• Other Names: Picardo Mauro guestEditor.; Slominski Andrzej T. guestEditor.
• Is Part Of: Experimental dermatology. Volume 26:Issue 7(2017)
• Page Start: 615
• Page End: 618
• Publication Date: 2016-12-19
• Subjects: dermoscopy -- imaging biomarkers -- machine learning -- machine vision -- melanoma -- pigmented lesion -- screening -- skin optics; Dermatology -- Periodicals; 616.5
• DOI: 10.1111/exd.13250
• Languages: English
• ISSNs: 0906-6705

Abstract

Abstract: We developed an automated approach for generating quantitative image analysis metrics (imaging biomarkers) that are then analysed with a set of 13 machine learning algorithms to generate an overall risk score that is called a Q‐score. These methods were applied to a set of 120 "difficult" dermoscopy images of dysplastic nevi and melanomas that were subsequently excised/classified. This approach yielded 98% sensitivity and 36% specificity for melanoma detection, approaching sensitivity/specificity of expert lesion evaluation. Importantly, we found strong spectral dependence of many imaging biomarkers in blue or red colour channels, suggesting the need to optimize spectral evaluation of pigmented lesions.