DIGITAL TWIN FRAMEWORKS FOR PERSONALIZED CANCER PROGRESSION MODELING USING LONGITUDINAL DATA

Abstract

Digital twin technology represents a paradigm shift in personalized cancer care by creating dynamic computational replicas of individual patients that evolve with real-time clinical data. This research develops a comprehensive digital twin framework that integrates longitudinal imaging, molecular profiling, treatment records, and patient-reported outcomes to model personalized cancer progression trajectories. We constructed digital twins for 394 cancer patients across multiple tumor types, incorporating serial CT/MRI scans, repeated biomarker measurements, genomic evolution tracking, and continuous physiological monitoring over median follow-up of 28 months. The framework employs mechanistic mathematical models of tumor growth combined with machine learning for parameter personalization, updating predictions as new data accumulates. Validation demonstrated that personalized digital twins predicted tumor volume changes with mean absolute percentage error of 12.3% at 3-month horizon and 18.7% at 6-month horizon, substantially outperforming population-based models (31.4% and 47.2% respectively). Treatment response prediction improved from 68% accuracy using baseline features to 84% accuracy when incorporating digital twin trajectory modeling. The framework successfully identified treatment resistance 4.2 weeks earlier on average than conventional radiological assessment, enabling proactive therapeutic modifications. Counterfactual simulations using digital twins predicted outcomes under alternative treatment strategies with 76% concordance to actual clinical courses when treatments were subsequently changed. These results demonstrate that digital twin frameworks can transform cancer care from reactive treatment of observed progression to proactive optimization based on personalized predictive models that continuously learn from accumulating patient data.