title: Digital imaging biomarkers feed machine learning for melanoma screening.
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.
authors: Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA.
authors: Daniel S
authors: DS
authors: Gareau
authors: Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA.|The Center for Clinical and Translational Science, The Rockefeller University, New York, NY, USA.
authors: Joel
authors: J
authors: Correa da Rosa
authors: Department of Dermatology, New York University Langone Medical Center, New York, NY, USA.
authors: Sarah
authors: S
authors: Yagerman
authors: Department of Dermatology, New York University Langone Medical Center, New York, NY, USA.
authors: John A
authors: JA
authors: Carucci
authors: Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA.
authors: Nicholas
authors: N
authors: Gulati
authors: Massachusetts Institute of Technology, Auto-ID Lab, Cambridge, MA, USA.
authors: Ferran
authors: F
authors: Hueto
authors: Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors: Jennifer L
authors: JL
authors: DeFazio
authors: Icahn School of Medicine at Mount Sinai, New York, NY, USA.
authors: Mayte
authors: M
authors: Suárez-Fariñas
authors: Dermatology Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
authors: Ashfaq
authors: A
authors: Marghoob
authors: Laboratory for Investigative Dermatology, The Rockefeller University, New York, NY, USA.
authors: James G
authors: JG
authors: Krueger
mesh_terms: D000465:Algorithms; D001331:Automation; D014408:Biomarkers, Tumor / Q000378:metabolism*; D003116:Color; D003880:Dermatology / Q000379:methods / Q000592:standards; D046169:Dermoscopy; D003937:Diagnosis, Differential; D004416:Dysplastic Nevus Syndrome; D006801:Humans; D007091:Image Processing, Computer-Assisted; D000069550:Machine Learning; D008545:Melanoma / Q000000981:diagnostic imaging* / Q000473:pathology; D009508:Nevus, Pigmented / Q000000981:diagnostic imaging* / Q000473:pathology; D010363:Pattern Recognition, Automated; D010858:Pigmentation; D015203:Reproducibility of Results; D012306:Risk; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor
keywords: dermoscopy; imaging biomarkers; machine learning; machine vision; melanoma; pigmented lesion; screening; skin optics