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Synthetic data generation from population-based breast cancer registries: opportunities and limitations for survival analysis applications
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DOI：10.1016/j.ejso.2026.111996.png)
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Abstract
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Synthetic data generation via Generative Adversarial Networks (GANs) has emerged as a promising strategy for privacy-preserving data sharing, cohort augmentation, and synthetic control arm construction in oncology. However, the extent to which GAN-derived cohorts preserve survival dynamics alongside covariate structure remains poorly characterised. This study evaluated structural fidelity, survival concordance, and prognostic preservation in a large synthetic breast cancer cohort derived from a population-based registry.
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