Journal
JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY
Volume 44, Issue 2, Pages 207-218Publisher
MOSBY, INC
DOI: 10.1067/mjd.2001.110395
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Funding
- NCI NIH HHS [1R43 CA74628-01, 2R44 CA/AR60299-02, 2R44 CA74628-02] Funding Source: Medline
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Background: Differentiation of melanoma from melanocytic nevi is difficult even for skin cancer specialists. This motivates interest in computer-assisted analysis of lesion images. Objective: Our purpose was to offer fully automatic differentiation of melanoma from dysplastic and other melanocytic nevi through multispectral digital dermoscopy. Method: At 4 clinical centers, images were taken of pigmented lesions suspected of being melanoma before biopsy Ten gray-level (MelaFind) images of each lesion were acquired, each in a different portion of the visible and near-infrared spectrum. The images of 63 melanomas (33 invasive, 30 in situ) and 183 melanocytic nevi (of which 111 were dysplastic) were processed automatically through a computer expert system to separate melanomas from nevi. The expert system used either a linear or a nonlinear classifier. The gold standard for training and testing these classifiers was concordant diagnosis by two dermatopathologists. Results: On resubstitution, 100% sensitivity was achieved at 85% specificity with a W-parameter linear classifier and 100%-/73% with a 12-parameter nonlinear classifier. Under leave-one-out cross-validation, the linear classifier gave 100%/84% (sensitivity/specificity), whereas the nonlinear classifier gave 95%/68%. Infrared image features were significant, as were features based on wavelet analysis. Conclusion: Automatic differentiation of invasive and in situ melanomas from melanocytic nevi is feasible, through multispectral digital dermoscopy.
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