Dermoscopic Algorithms Lack Reliability for Detecting Melanoma

Source: Cancer Network, April 2016

A new study has shown that algorithms of dermoscopic criteria used to detect melanoma had only modest levels of accuracy and lacked interobserver agreement among a group of regular dermoscopy users. Among six common algorithms examined, none seemed to be easy to learn or reliable.

“Our results confirm the need to further improve dermoscopic terminology, criteria, and algorithms,” wrote Cristina Carrera, MD, PhD, of Memorial Sloan Kettering Cancer Center, and colleagues in JAMA Dermatology. “To do so, future studies may benefit from crowd-sourcing and collective intelligence approaches, as well as the public image archive being created in the International Skin Imaging Collaboration Melanoma Project, which permits analysis and comparison of the areas within a lesion that users select as having unique dermoscopic structures.”

According to the study, the use of dermoscopy improves diagnostic accuracy compared with a naked eye examination alone; however, diagnosis made by trained users are often made without the use of structured analytical criteria. For new dermoscopy users, several simplified diagnostic algorithms exist—such as the ABCD rule, the Menzies method, or the 3-point checklist. In this study, Carrera and colleagues wanted to compare the diagnostic accuracy of these varied dermoscopic algorithms.