Biopsy-free, label-free 3D virtual histology of intact skin

University Of California-Los Angeles, Los Angeles CA

Investigators

Abstract

Over 2 million skin cancers are diagnosed annually in the United States, with >80% being basal cell carcinoma. The current standard of care remains invasive biopsy followed by assessment, resulting in unnecessary scars, multiple patient visits, and added costs to the healthcare system. Non-invasive imaging of the skin at the microscopic level could allow for immediate triage of skin cancers. This project will develop a machine learning-based technique to transform 3D images of intact skin into high quality virtually stained images that can be easily interpreted. The technology developed in this project can allow dermatologists to obtain accurate and rapid diagnosis of skin lesions and improve clinical care. Furthermore, this technology may improve patient access to dermatologists and dermatology clinic workflow. It may also provide tele-dermatology solutions during times with limited patient interactions. This project will also advance a complementary education and outreach program which will involve public interviews and popular science articles in news media and internet; research opportunities in the investigator's laboratory for underrepresented undergraduate students; and graduate student training through the organization of workshops, seminars, and conferences. These activities will serve undergraduate and high school students to interact with a cutting-edge research environment, increasing their scientific curiosity and shaping their career goals in science and engineering. The overarching goal of this project is to develop deep learning-based digital staining technology that can fundamentally improve dermatologists’ ability to accurately diagnose skin lesions without requiring skin biopsy or any chemical processing of tissue. Deep neural network-based computational approaches will be used to train the virtual histology algorithm for obtaining accurate, virtually stained images of intact skin corresponding to normal skin and neoplasms suspicious for basal cell carcinoma, the most common type of skin cancer. Furthermore, various tests will be used to determine and quantify whether the virtual histology format can improve diagnosis. These will include comparisons with actual histology of the skin and objective performance measures of diagnostic accuracy (sensitivity and specificity) by dermatologists who are trained in reflectance confocal microscopy when compared to the gold standard for diagnosis: histological processing. Once successful, these studies will provide a leapfrog advancement towards biopsy-free histologic assessment of the skin, which has the potential to transform the practice of clinical dermatology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.