In the framework of oncology care, therapeutic outcomes can be explored through computational modeling. The proliferation of solid tumors and the action of anticancer therapies arise from the interplay between local growth kinetics, the diffusion of tumor cells within surrounding tissues, and the transport, effectiveness, and metabolism of administered drugs. In this study, we formalize the constitutive assumptions that allow these mechanisms to be represented within a common CFD platform. Computational modeling can accelerate oncological research by predicting patient-specific treatment responses with enhanced precision, ensuring that simulations remain consistent with biological and clinical constraints. As the adoption of quantitative metrics is essential to support emerging digital-health paradigms, we introduce a modified mass Damköhler number Da as a descriptor of therapeutic efficacy. By formulating this metric across a combination of therapeutic branches, we establish a framework that predicts how treatment performance may improve through controlled modifications of dosage while conserving total drug exposure. Dosages that are even slightly incremented (10%) in correspondence of strong negative Da transients (from about 1 ⋅ 1 0 6 on, in a study case) allowed in a case of study for improvement of residual lesion volume reduction by a fraction of cm 3 , while accelerating complete healing by some 15 days in another. This study provides a set of engineering-oriented tools for characterizing and forecasting tumor dynamics and treatment response, offering a structured approach for analyzing the competition between proliferation and drug challenge in solid-tumor therapy. • CFD-PDE framework for solid-tumor growth and therapy, with metrics of proliferation–drug competition. • Per-pulse dose shifts within ±10% exploit nonlinear PK/PD to cut tumor volume or time to response. • FEM-based model enables virtual patients and closed-loop prognosis for clinical translation.
Gianpaolo Ruocco (Sat,) studied this question.