Enhanced Metastasis Risk Prediction in Cutaneous Squamous Cell Carcinoma Using Deep Learning and Computational Histopathology

TY - JOUR

T1 - Enhanced Metastasis Risk Prediction in Cutaneous Squamous Cell Carcinoma Using Deep Learning and Computational Histopathology

AU - Peleva, Emilia

AU - Chen, Yue

AU - Finke, Bernhard

AU - Rizvi, Hasan

AU - Healy, Eugene

AU - Lai, Chester

AU - Craig, Paul

AU - Rickaby, William

AU - Schoenherr, Christina

AU - Nourse, Craig

AU - Proby, Charlotte

AU - Inman, Gareth J.

AU - Leigh, Irene

AU - Harwood, Catherine A.

AU - Wang, Jun

PY - 2025/9/2

Y1 - 2025/9/2

N2 - 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 gene expression-based tools and clinicopathologic classifications. Multivariate analysis including common clinicopathologic classifications confirm cSCCNet as an independent predictor for metastasis, implying it identifies predictive factors beyond known clinicopathologic risk factors. Histopathological analysis including multiplex immunohistochemistry suggests that tumour differentiation, acantholysis, desmoplasia, and the spatial localisation of lymphocytes relative to tumour tissue may be important in predicting risk of developing metastasis. Although further validation including prospective evaluation is required, cSCCNet has potential as a reliable and accurate tool for metastatic risk prediction that could be easily integrated into existing histopathology workflows.

AB - 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 gene expression-based tools and clinicopathologic classifications. Multivariate analysis including common clinicopathologic classifications confirm cSCCNet as an independent predictor for metastasis, implying it identifies predictive factors beyond known clinicopathologic risk factors. Histopathological analysis including multiplex immunohistochemistry suggests that tumour differentiation, acantholysis, desmoplasia, and the spatial localisation of lymphocytes relative to tumour tissue may be important in predicting risk of developing metastasis. Although further validation including prospective evaluation is required, cSCCNet has potential as a reliable and accurate tool for metastatic risk prediction that could be easily integrated into existing histopathology workflows.

U2 - 10.1038/s41698-025-01065-7

DO - 10.1038/s41698-025-01065-7

M3 - Article

C2 - 40897830

SN - 2397-768X

VL - 9

JO - npj Precision Oncology

JF - npj Precision Oncology

M1 - 308

ER -