Skin cancer is the fastest growing cancer. Approximately 34,100 Americans developed cutaneous melanoma in 1995; of the survivors, many must contend with the ongoing trauma of disfigurement and fear. Skin biopsies are now the most frequently performed medical procedure. It is axiomatic among dermatologists that early detection and diagnosis are critical. Great strides have been made in early detection of suspect skin lesions; however failure to biopsy the right lesion has severe consequences. The dilemma is exacerbated since 50- 80 percent of biopsies prove unnecessary, contributing to an enormous waste of health care dollars, patient trauma and negative patient behavior feedback. The Phase I work in dermatological spectroscopy and artificial neural net technology suggest that an automated clinical diagnostic aid which produces a quantitative rather than qualitative diagnostic assessment of skin lesions is possible. This project proposes development and testing of such a product. During Phase II a large number of spectroscopic samples of melanoma and nevi will be used to complete development of an artificial neural net classifier. Such a classifier system will lead to a commercial product to discriminate """"""""normal,"""""""" pre-cancerous and cancerous skin lesions.
The proposed project will lead to a non-invasive, in-office, real-time test to provide an automated, repeatable diagnostic probability of the nature of skin lesions prior to biopsy. Skin biopsies are now the most frequently performed reimbursed Medicare procedure, and as many as 50-80% are found not to be necessary after the fact. The low cost of this test, and rapid amortization of the system, coupled with the enormous health care cost savings possible in conjunction with a significant and widely recognized health problem, suggest that this product could have great commercial potential.
