Longitudinal Synthetic Data Generation by Artificial Intelligence to Accelerate Clinical and Translational Research in Breast Cancer

作者：Elena Zazzetti, S. D'amico, Flavia Jacobs, R. de Sanctis, L. Chiudinelli, M. Gaudio, G. Asti, Mattia Delleani, Elisabetta Sauta, M. Quintavalla, A. Bruseghini, L. Lanino, G. Maggioni, A. Campagna, V. Savevski, M. D. Della Porta, A. Zambelli · 发表于：JCO Clinical Cancer Informatics · 年份：2025 · DOI：10.1200/CCI-25-00033 · 被引用次数：6 · 研究领域：Medicine

PURPOSE Real-world data (RWD) are critical for breast cancer (BC) research but are limited by privacy concerns, missing information, and data fragmentation. This study explores synthetic data (SD) generated through advanced generative models to address these challenges and create harmonized longitudinal data sets. METHODS A data set of 1052 patients with human epidermal growth factor receptor 2–positive and triple-negative BC from the Informatics for Integrating Biology and the Bedside (i2b2) platform was used. Advanced generative models, including generative adversarial networks (GANs), variational autoencoders (VAEs), and language models (LMs), were applied to generate synthetic longitudinal data sets replicating disease progression, treatment patterns, and clinical outcomes. The Synthethic Validation Framework (SAFE) powered by Train was used to evaluate the fidelity, utility, and privacy. SD were tested across three settings: (1) integration with i2b2 for privacy-preserving data sets; (2) multistate disease modeling to predict clinical outcomes; and (3) generation of synthetic control groups for clinical trials. RESULTS The synthetic data sets exhibited high fidelity (score 0.94) and ensured privacy, with temporal patterns validated through time-series analyses and Uniform Manifold Approximation and Projection embeddings. In setting A, SD accurately mirrored RWD on the i2b2 platform while maintaining privacy. In setting B, incorporating SD improved the predictive performa...