Artificial intelligence for basal cell carcinoma: diagnosis and distinction from histological mimics

We trained an artificial intelligence (AI) algorithm to identify basal cell carcinoma (BCC), and to distinguish BCC from histological mimics. A total of 1061 glass slides were collected: 616 containing BCC and 445 without BCC. BCC slides were collected prospectively, reflecting the range of specimen types and morphological variety encountered in routine pathology practice. Benign and malignant histological mimics of BCC were selected prospectively and retrospectively, including cases considered diagnostically challenging for pathologists. Glass slides were digitally scanned to create a whole slide image (WSI), which was divided into patches representing a tissue area of 65,535 μm2. Pathologists annotated the data, yielding 87,205 patches labelled BCC present and 1,688,697 patches labelled BCC absent. The COMPASS model (COntext-aware Multi-scale tool for Pathologists Assessing SlideS) based on Convolutional Neural Networks, was trained to provide a probability of BCC being present at the patch level and the slide level. The test set comprised 246 slides, 147 of which contained BCC. The COMPASS AI model demonstrated high accuracy, classifying WSIs as containing BCC with a sensitivity of 98.0% and a specificity of 97.0%, representing 240 WSIs classified correctly, three false positives, and three false negatives. Using BCC as a proof of concept, we demonstrate how AI can account for morphological variation within an entity, and accurately distinguish from histologically similar entities. Our study highlights the potential for AI in routine pathology practice. We trained an artificial intelligence (AI) algorithm to identify basal cell carcinoma (BCC), and to distinguish BCC from histological mimics. A total of 1061 glass slides were collected: 616 containing BCC and 445 without BCC. BCC slides were collected prospectively, reflecting the range of specimen types and morphological variety encountered in routine pathology practice. Benign and malignant histological mimics of BCC were selected prospectively and retrospectively, including cases considered diagnostically challenging for pathologists. Glass slides were digitally scanned to create a whole slide image (WSI), which was divided into patches representing a tissue area of 65,535 μm2. Pathologists annotated the data, yielding 87,205 patches labelled BCC present and 1,688,697 patches labelled BCC absent. The COMPASS model (COntext-aware Multi-scale tool for Pathologists Assessing SlideS) based on Convolutional Neural Networks, was trained to provide a probability of BCC being present at the patch level and the slide level. The test set comprised 246 slides, 147 of which contained BCC. The COMPASS AI model demonstrated high accuracy, classifying WSIs as containing BCC with a sensitivity of 98.0% and a specificity of 97.0%, representing 240 WSIs classified correctly, three false positives, and three false negatives. Using BCC as a proof of concept, we demonstrate how AI can account for morphological variation within an entity, and accurately distinguish from histologically similar entities. Our study highlights the potential for AI in routine pathology practice.