Three-Dimensional Reconstruction of Basal Cell and Squamous Cell Carcinomas: Noninvasive Evaluation of Cancerous Tissue Cross Sections and Margins

Background: There are approximately 5.4 M basal cell (BCC) and squamous cell (SCC) carcinomas diagnosed each year, and the number is increasing. Currently, the gold standard for skin cancer diagnosis is histopathology, which requires the surgical excision of the tumor followed by pathological evaluation of a tissue biopsy. The three-dimensional (3D) nature of human tissue suggests that two-dimensional (2D) cross sections may be insufficient in some cases to represent the complex structure due to sampling bias. There is a need for new techniques that can be used to classify skin lesion types and margins noninvasively. Methods: We use optical coherence tomography volume scan images and AI to noninvasively create 3D images of basal cell and squamous cell carcinomas. Results: Three-dimensional optical coherence tomography images can be broken down into a series of cross sections that can be classified as benign or cancerous using convolutional neural network models developed in this study. These models can identify cancerous regions as well as clear edges. Cancerous regions can also be verified based on visual review of the color-coded images and the loss of the green and blue subchannel pixel intensities. Conclusions: Three-dimensional optical coherence tomography cross sections of cancerous lesions can be collected noninvasively, and AI can be used to classify skin lesions and detect clear lesion edges. These images may provide a means to speed up treatment and promote better patient screening, especially in older patients who will likely develop several lesions as they age.