New Publication Christoforou A.T., et al. 2025 Computers in Biology and Medicine

Data-driven decision-making in radiation oncology (RO) relies on integrating real-world data effectively. Synthetic data (SD), generated through machine learning, offers a solution by mimicking real-world data without compromising privacy. This paper presents a general framework for generating, evaluating, and selecting high-quality tabular SD for clinical use, focusing on survival datasets in RO. Five retrospectively collected survival-based RO datasets (n = 1038 recurrent prostate cancer, n = 117 primary localised prostate cancer, n = 48 primary nodal positive (metastasised) prostate cancer, n = 1269 head and neck cancer, n = 353 gliomas) underwent cleaning and preparation. SD was generated using four different machine-learning models, with each model producing multiple variants. These were evaluated for privacy, clinical behaviour, and feature distribution using robust and interpretable metrics, with a single SDset being selected for each real-world dataset using a weighted ranking system. Read more