Abstract
Cutaneous squamous cell carcinoma (cSCC) possesses metastatic potential and causes significant mortality. However, clinical assessment of metastasis risk of primary cSCC is challenging. In this study, we have used artificial intelligence (AI) algorithm to identify primary cSCCs with risk for metastasis. Residual neural network-architectures were trained and fine-tuned on tumor tiles extracted from clinician annotated, hematoxylin and eosin (HE)-stained whole slide images representing primary metastatic (n=45) and non-metastatic (n=59) cSCCs.
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