BRAIN TUMOR DIGITAL TWIN SYSTEM 2026: AN END-TO-END CLINICAL AI PLATFORM FOR MULTIMODALTUMOR ANALYSIS, TREATMENT SIMULATION, ANDCOGNITIVE OUTCOME PREDICTION

Glioblastoma multiforme (GBM) remains the most aggressive primary brain malignancy, characterized by rapid infiltration, high recurrence rates, and limited therapeutic response, resulting in a median survival of approximately 14.6 months. Current clinical workflows rely on fragmented imaging analyses and lack integrated predictive frameworks capable of modeling tumor dynamics and treatment response. This project introduces an AI-driven Brain Tumor Digital Twin platform that converts a single structural MRI into a patient-specific computational replica of tumor biology and progression. The system integrates eight deep learning architectures for automated multiregional tumor segmentation and radiomic feature extraction (>500 features), enabling predictive modeling of tumor growth and therapy response. Tumor evolution is simulated through a reaction–diffusion partial differential equation derived from diffusion tensor imaging data. The framework additionally incorporates models for distinguishing tumor-associated cognitive decline from Alzheimer’s disease and provides augmented reality–assisted surgical guidance. By enabling rapid analysis (