Digital twins for cancer progression based on multiscale structured models and mechanistic learning

HORIZON.1.1HORIZON-ERCID: 101220816
EC Contribution
€15,000
Consortium Size
1 orgs
Summary

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 crucial gap in knowledge. I will build a new class of biomechanistic models to describe the multiscale dynamics of cancer progression, enable its early detection at microscopic stage by using macroscopic clinical data, and construct clinically-practical digital twins to optimally adapt cancer monitoring for early detection of progression. To achieve this ambitious goal, I will focus on newly-diagnosed prostate cancer and (i) build novel micro and macroscale models jointly describing cancer growth and progression using a structured formulation over space, time, and malignancy dimensions; (ii) pioneer a multiscale structured model equipped with a mechanistic learning operator that uses macroscale model-based biomarkers of progression to reconstruct the microscale cancer malignancy profile; and (iii) combine adaptive and immersed isogeometric methods, reduced order models, and multi-objective risk-based optimization to construct practical digital twins of cancer progression and explore optimization of cancer monitoring. Thus, DIGIPRO will push the frontiers of cancer modeling, biology, and care, while also opening new horizons in mechanistic modeling of multiscale progressive phenomena in medicine (e.g., Alzheimer’s disease, atherosclerosis) and engineering (e.g., fracture, damage).

Consortium (1)