title: Melanoma computer-aided diagnosis: reliability and feasibility study.
abstract: BACKGROUND
  Differential diagnosis of melanoma from melanocytic nevi is often not straightforward. Thus, a growing interest has developed in the last decade in the automated analysis of digitized images obtained by epiluminescence microscopy techniques to assist clinicians in differentiating early melanoma from benign skin lesions.
  
  
  OBJECTIVE
  The aim of this study was to evaluate diagnostic accuracy provided by different statistical classifiers on a large set of pigmented skin lesions grabbed by four digital analyzers located in two different dermatological units.
  
  
  METHODS
  Images of 391 melanomas and 449 melanocytic nevi were included in the study. A linear classifier was built by using the method of receiver operating characteristic curves to identify a threshold value for a fixed sensitivity of 95%. A K-nearest-neighbor classifier, a nonparametric method of pattern recognition, was constructed using all available image features and trained for a sensitivity of 98% on a large exemplar set of lesions.
  
  
  RESULTS
  On independent test sets of lesions, the linear classifier and the K-nearest-neighbor classifier produced a mean sensitivity of 95% and 98% and a mean specificity of 78% and of 79%, respectively.
  
  
  CONCLUSIONS
  In conclusion, our study suggests that computer-aided differentiation of melanoma from benign pigmented lesions obtained with DB-Mips is feasible and, above all, reliable. In fact, the same instrumentations used in different units provided similar diagnostic accuracy. Whether this would improve early diagnosis of melanoma and/or reducing unnecessary surgery needs to be demonstrated by a randomized clinical trial.
authors.affiliation: Department of Dermatology, University of Siena, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Burroni
authors.forename: Rosamaria
authors.initials: R
authors.lastname: Corona
authors.forename: Giordana
authors.initials: G
authors.lastname: Dell'Eva
authors.forename: Francesco
authors.initials: F
authors.lastname: Sera
authors.forename: Riccardo
authors.initials: R
authors.lastname: Bono
authors.forename: Pietro
authors.initials: P
authors.lastname: Puddu
authors.forename: Roberto
authors.initials: R
authors.lastname: Perotti
authors.forename: Franco
authors.initials: F
authors.lastname: Nobile
authors.forename: Lucio
authors.initials: L
authors.lastname: Andreassi
authors.forename: Pietro
authors.initials: P
authors.lastname: Rubegni
mesh_terms: D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005240:Feasibility Studies; D006801:Humans; D008545:Melanoma / Q000145:classification / Q000175:diagnosis*; D009508:Nevus, Pigmented / Q000175:diagnosis; D015203:Reproducibility of Results; D012878:Skin Neoplasms / Q000175:diagnosis