Predicting a metachronous cutaneous squamous cell carcinoma: a competing-risk model based on nationwide linked registries

Backgroundfollowing a first cutaneous squamous cell carcinoma (CSCC), one-third of patients develop new primaries, escalating their risk of metastasis and poor outcomes. However, current follow-up strategies are not risk-stratified, representing a critical gap in patient management. Objectiveto develop and validate a prognostic model to quantify individualized absolute risk of a first metachronous CSCC after an index tumor, accurately accounting for the high competing risk of mortality in this typically elderly population. Methodswe conducted a nationwide, population-based cohort study of 11,737 patients with a first histologically confirmed CSCC (Netherlands Cancer Registry, 2007-2008) with up to 10 years of follow-up. Data on subsequent tumors was retrieved via linkage to the Automated National Pathological Anatomy Archive (Palga). A Fine-Gray competing-risk model was developed using routinely available clinical and pathological predictors (age, sex, hematologic malignancy, basal cell carcinoma (BCC) and actinic keratosis (AK) history, presence of synchronous CSCC, primary tumor location, and differentiation). Model performance was assessed 10-fold cross-validation, quantifying discrimination (time-dependent C-index) and calibration. Resultsduring follow-up, 3,288 (28%) developed a first metachronous CSCC. The model identified key predictors: markers of cumulative UV-exposure (included AK history, [≥]5 prior BCCs), and immunosuppression (chronic lymphocytic leukaemia/small lymphocytic leukaemia). Male sex, presence of synchronous CSCC at baseline were also associated with higher risk. While discrimination was modest (cross-validated 5-year C-index: 0.64), the model demonstrated excellent calibration. Conclusionsthis competing-risk model provides individualized, well-calibrated absolute risk estimates for a first metachronous CSCC. Based on routinely available clinical features, it offers insight into how established predictors shape risk in this high-susceptibility population. External validation and the identification of novel predictors are necessary to further refine the model and support personalized dermatologic care.