Abstract
Early detection of skin cancer is of critical importance to provide five year survival rates that approach 99%. By 2050, one out of five Americans by age 70 will develop some form of skin cancer. This will result in a projected rate of 50 million skin biopsies per year given the current rate of escalation. In addition, the ability to differentiate between pigmented lesions and melanomas has proven a diagnostic challenge. While dermoscopy and visual analysis are useful in identifying many skin lesions, additional non-invasive techniques are needed to assist in the analysis of difficult to diagnose skin tumors. To augment dermoscopy data, we have developed 3D maps based on physical biomarker characteristics of benign and cancerous lesions using vibrational optical coherence tomography (VOCT). 3D images based on quantitative physical data involving changes in cellular and fibrous tissue stiffness along with changes in vascular quality are used to map and evaluate different types of cancers. 3D tumor maps constructed using quantitative VOCT data and OCT images have been used to characterize the differences between melanoma and other lesions. These characteristics can be used to plan the excision of difficult lesions where extensive surgery may be needed to remove the entire tumor in one step. In addition, it is now possible to use dermoscopy and VOCT to non-invasively differentiate between different cancerous lesion types using measurements of the resonant frequency of new cellular and vascular peaks. Quantitative VOCT information along with dermoscopic findings can be collected and analyzed remotely using artificial intelligence to improve cancerous tissue diagnosis.