Even experts can be fooled by melanoma. People with this type of skin cancer often have mole-looking growths on their skin that tend to be irregular in shape and color, and can be hard to tell apart from benign ones, making the disease difficult to diagnose. Now, researchers at The Rockefeller University have developed an automated technology that combines imaging with digital analysis and machine learning to help physicians detect melanoma at its early stages. “There is a real need for standardization across the field of dermatology in how melanomas are evaluated,” says James Krueger, the D. Martin Carter Professor in Clinical Investigation and Head of the Laboratory of Investigative Dermatology. “Detection through screening saves live, but is very challenging visually, and even when a suspicious lesion is extracted and biopsied, it is confirmed to be melanoma in only about 10 percent of cases.” In the new approach, images of lesions are processed by a series of computer programs that extract information about the number of colors present in a growth, and other quantitative data. The analysis generates an overall risk score, called a Q-score, which indicates the likelihood that the growth is cancerous. Published online on December 19, 2016 in Experimental Dermatology, a recent study evaluating the tool’s usefulness indicates that the Q-score yields 98 percent sensitivity, meaning it is very likely to correctly identify early melanomas on the skin. The ability of the test to correctly diagnose normal moles was 36 percent, approaching the levels achieved by expert dermatologists performing visual examinations of suspect moles under the microscope.
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