Molecular imaging may reduce need for melanoma biopsies

Douglas Grossman, MD, Ph.D., co-leader of the Melanoma Center at Huntsman Cancer Institute at the University of Utah (the U) and professor of dermatology at the U, has helped develop a noninvasive technology that aims to improve melanoma screening outcomes and reduce unnecessary procedures. Grossman and a team evaluated the accuracy of Skin Fluorescent Imaging (SFI), a system developed by Orlucent, Inc. that reads the molecular makeup of moles and lesions without removing possibly cancerous skin.

The results of the Phase 2 clinical trial were published in the February issue of JAAD International.

“By identifying the molecular signals that drive melanoma early on, SFI has the potential to help clinicians decide which suspicious lesions to biopsy while sparing patients unnecessary biopsies,” says Grossman. “SFI is a promising step forward in noninvasive approaches to catching melanoma early and reducing biopsies of moles.”

Melanoma is the deadliest form of skin cancer, making up 90% of skin cancer-related deaths. The disease becomes dangerous once it has metastasized or spread beyond its original site. Removing melanoma early decreases the death rate, leading many to adopt a “when in doubt, cut it out” approach.

Clinicians typically rely on visual recognition to find moles that appear abnormal or have changed shape. Once an unusual mole or lesion is found, it is removed out of caution that it could be cancerous. Making a definitive diagnosis requires an invasive biopsy. “Many early melanomas and moles can have similar features,” says Grossman. “SFI offers a more targeted approach to melanoma screening, so that the lesions most likely to be melanoma can be biopsied.”

In the study evaluating SFI, Grossman and his fellow researchers analyzed 240 pigmented lesions that had been flagged for suspicion of melanoma during a skin exam. The team applied a fluorescent dye to the irregular lesion and surrounding skin. The dye is designed to bind to a protein called αvβ3, which plays a key role in tumor growth and is a marker of tumor aggression. Researchers then used a handheld imaging device to capture fluorescent images of the mole and surrounding tissue and analyze the data in real time using a machine-learning algorithm.

SFI was able to identify all cases of invasive melanoma, the most threatening form of the disease. The system was also successful in differentiating between normal and atypical moles, which have some features of melanoma that may be precancerous. This novel noninvasive technology could be particularly impactful in the Mountain West, the area Huntsman Cancer Institute serves, which has consistently high melanoma rates and distant and rural populations without close access to dermatologists. Additional validation will be required prior to broad clinical use outside of a clinical trial setting.