Enhanced metastasis risk prediction in cutaneous squamous cell carcinoma using deep learning and computational histopathology.

ABSTRACT: Cutaneous squamous cell carcinoma (cSCC) is the most common skin cancer with metastatic potential and development of metastases carries a poor prognosis. To address the need for reliable risk stratification, we developed cSCCNet, a deep learning model using digital pathology of primary cSCC to predict metastatic risk. A retrospective cohort of 227 primary cSCC from four centres is used for model development. cSCCNet automatically selects the tumour area in standard histopathological slides and then stratifies primary cSCC into high- vs. low-risk categories, with heatmaps indicating most predictive tiles contributing to explainability. On a 20% hold-out testing cohort, cSCCNet achieves an area under the curve (AUC) of 0.95 and 95% accuracy in predicting risk of metastasis, outperforming gen

SUBMITTER: Peleva E 

PROVIDER: S-EPMC12405460 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature