ABSTRACT:
Breast cancer exhibits substantial biological heterogeneity, resulting in variable therapeutic response and clinical outcomes. Preclinical evaluation of treatment strategies is constrained by high costs, ethical considerations, and limited scalability, restricting systematic exploration of personalized therapy parameters. Computational modeling and digital twin technologies offer an opportunity to create controlled in silico environments to support translational research and precision oncology. We developed a Virtual Tumor Intervention Simulation Platform (VTISP) for breast cancer research, enabling controlled, repeatable, and personalized simulation of tumor dynamics and drug response. The platform is designed to support comparative evaluation of therapeutic strategies under standardized virtual conditions. The computational framework models breast tumor growth, immune interactions, and therapeutic interventions using a virtual twin architecture. The system incorporates imaging-derived tumor characteristics and biologically inspired parameters to initialize patient-specific or synthetic breast tumor profiles. Therapeutic interventions are implemented through configurable modules that allow systematic variation of drug type, dose, timing, and combination strategies. Baseline progression and treatment-specific simulations are conducted under controlled virtual conditions. Outputs include time-resolved tumor burden metrics, response kinetics, and dynamic visualizations of tumor–therapy interactions to support comparative analysis across intervention scenarios. The platform generated reproducible breast tumor growth and treatment response trajectories across baseline and intervention conditions. Simulations demonstrated differential response patterns under varying drug dosing and scheduling parameters, reflecting therapy-dependent tumor dynamics. Comparative analyses across controlled parameter sweeps showed consistent trends in virtual tumor burden reduction and response kinetics, supporting feasibility for exploring treatment sensitivity and response variability. VTISP provides a scalable and controlled in silico framework for breast cancer drug testing and tumor response analysis. This approach has potential to complement traditional preclinical models, accelerate hypothesis generation, and support data-driven optimization of treatment strategies in breast cancer research