A mass transfer model for computational prediction of proliferation and therapy outcome of non-Hodgkin lymphoma

Mathematical modeling constitutes an emerging area of oncological research aiming to predict spatial and temporal evolution of tumors, by describing many different phenomena, which occur at different scales. Among these, modeling at the macroscopic scale has an interesting potential of application, when applied in a framework where actual diagnostic imaging is used to identify the metabolic tumor volume undergoing proliferation. Diffuse large B-cell lymphoma (DLBCL) is a cancer of B cells, a type of lymphocyte that is responsible for producing antibodies. The most common form of Non-Hodgkin lymphoma among adults, DLBCL can arise in any part of the body and may play a very aggressive malignancy. This paper aims to enforce a mass transfer modeling approach in order to gain deeper insight into the dynamics of tumor growth at the tissue scale and to develop a predictive, quantitative method for each patient at hand. A cohort of 18 patients has been successfully enrolled to validate the model. Results confirm that tumor proliferation, at the macroscopic scale, scores many nonlinear features, and show that the proposed model could be used by oncologists as a decision support tool towards personalized treatment optimization of solid tumors.