ERC Starting Grant · 2025
Digital twins for cancer progression based on multiscale structured models and mechanistic learning
The personalization of oncology needs predictive methods to anticipate major pathological events and optimize clinical care for each patient. To address this critical demand, computational oncology has produced biomechanistic models for patient-specific cancer forecasting. Although these models are based on cancer growth (increase in extension), the main driver of potentially lethal disease and, hence, the basis for clinical decisions is cancer progression (increase in malignancy). However, despite known correlations between cancer progression and growth, the biophysical mechanisms that explain how cancer progression emerges and alters cancer growth remain unknown. DIGIPRO will address this…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.