Whole-slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma.

BACKGROUND: Basal cell carcinoma (BCC) is the most common type of skin cancer with incidence rates rising each year. Mohs micrographic surgery (MMS) is most often chosen as treatment for BCC on the face for which each frozen section has to be histologically analysed to ensure complete tumor removal. This causes a heavy burden on health economics.

OBJECTIVES: To develop and evaluate a deep learning model for the automated detection of BCC-negative slides and classification of BCC in histopathology slides of MMS based on whole-slide image (WSI).

METHODS: Two deep learning models were developed on the basis of 171 digitized H&E frozen slides from 70 different patients. The first model had a U-Net architecture and was used for the segmentation of BCC. A subsequent convolutional neural network used the segmentation to classify the whole slide as BCC or BCC-negative.

RESULTS: Quantitative evaluation over manually labelled ground truth data resulted in a Dice score of 0.66 for the segmentation of BCC and an area under the receiver operating characteristic curve (AUC) of 0.90 for the slide-level classification.

CONCLUSIONS: This study demonstrates that through WSIs deep learning models may be a feasible option to improve the clinical workflow and reduce costs in histological analysis of BCC in MMS.