Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma

Predicting cancer patient disease progression is a key step towards personalized medicine and secondary prevention. Risk stratification systems based on clinico-pathological criteria aim to identify high-risk patients, but accurate predictions remain challenging. Deep learning models present new opportunities for patient risk prediction, yet their interpretability has been largely unexplored. We developed a transformer-based approach for predicting progression of cutaneous squamous cell carcinoma (cSCC) patients based on diagnostic histopathology tumor slides. Our initial model showed AUROC = 0.92 on a held-out test set, with average AUROC of 0.65 on external validation cohorts. To further increase generalizability and reduce potential privacy concerns, we trained the model in a federated manner across three clinical centers, reaching AUROC = 0.82 across all cohorts, with image-based risk scores achieving hazard ratios up to 7.42 (p < 0.01) in multivariable analyses. Through interpretability analysis, we identified spatial and morphological features predictive of progression, suggesting that tumor boundary information and tissue heterogeneity characterize progressive cSCCs. Trained exclusively on routine diagnostic slides and offering biological insights, our model can improve secondary prevention and understanding of cSCC while enabling deployment across clinical centers without administrative overheads or privacy concerns.