Weakly-supervised classification of Mohs surgical sections using artificial intelligence.

Abstract

Basal cell carcinoma (BCC) is the most frequently diagnosed form of skin cancer, and its incidence continues to rise, particularly among older individuals. This trend puts a significant strain on healthcare systems, especially in terms of histopathologic diagnostics required for Mohs micrographic surgery (MMS), which is used to treat BCC in sensitive locations to minimize tissue loss. This study aims to address the challenges in BCC detection within MMS whole-slide images (WSIs) by developing and evaluating a deep learning model that bridges weakly-supervised learning with interpretable segmentation-based methods through attention maps. Utilizing datasets from two medical centers, the model demonstrated an average area under the ROC curve (AUC) of 0.958 on internal testing and an AUC of 0.934 on an independent third external dataset despite no fine-tuning or preprocessing for the latter. Attention maps provided insights into the model's decision-making, highlighting critical regions for slide-level classification. The sensitivity of the attention maps in localizing tumor regions was 0.853 when no filtering was applied and gave 8 revision false positives per slide on average and was reduced to an average of 2 false positives per slide with a sensitivity of 0.873 when detections smaller than 200 micrometers were removed from the attention maps. These findings indicate that the deep learning model is highly effective in detecting BCC in MMS WSIs, with robust performance across different datasets and conditions. The use of attention maps enhances the model's interpretability, making it a promising tool for aiding dermatopathologists and MMS surgeons.