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Deep Learning for Automated Segmentation of Basal Cell Carcinoma on Mohs Micrographic Surgery Frozen Section Slides
Vamsi Varra1, Kathryn T Shahwan1,2,3, Kirsten Johnson1
1The Ohio State University Wexner Medical Center, Columbus, Ohio.
Background:
Deep learning has been used to classify basal cell carcinoma (BCC) on histopathologic images. Segmentation models, required for localization of tumor on Mohs surgery (MMS) frozen section slides, have yet to reach clinical utility.
Objective:
To train a segmentation model to localize BCC on MMS frozen section slides and to evaluate performance by BCC subtype.
Materials And Methods:
The study included 348 fresh frozen tissue slides, scanned as whole slide images, from patients treated with MMS for BCC. BCC foci were manually outlined using the Grand Challenge annotation platform. The data set was divided into 80% for training, 10% for validation, and 10% for the test data set. Segmentation was performed using the Ultralytics YOLOv8 model.
Results:
Sensitivity was .71 for all tumors, .87 for nodular BCC, .79 for superficial BCC, .74 for micronodular BCC, and .51 for morpheaform and infiltrative BCC. Specificity was .75 for all tumors, .59 for nodular BCC, .58 for superficial BCC, .83 for micronodular BCC, and .74 for morpheaform and infiltrative BCC.
Conclusion:
This study trained a segmentation model to localize BCC on MMS frozen section slides with reasonably high sensitivity and specificity, and this varied by BCC subtype. More accurate and clinically relevant performance metrics for segmentation studies are needed.
