Multidimensional modeling of solid tumor proliferation following drug treatment: Toward computational prognosis as a tool to support oncology

Models capable of predicting how a solid tumor evolves and how it responds to drug therapy have applications in research, clinics, and education/training, since they allow for different therapy scenarios exploration (along with their evaluation), in a short time and at a limited cost. In research, modeling in this area is of paramount importance since it helps the time to target (whatever the target of the research may be, as the development of a new drug). In clinical applications, this is a fundamental tool to personalize the therapy of a specific patient. In education/training, such models allow personnel to understand the effects of a given therapy on the oncological patient. This chapter is intended to share and discuss the vision of an engineering approach toward a feasible virtualized oncological prognosis, including all the fundamental steps needed to develop a digital tool to support oncology. The model is applied to diffuse large B-cell (non-Hodgkin) lymphoma (DLBCL) proliferation in realistic human organs, and its validation and predictions are discussed with clinical examples. With the acquisition of diagnostic images (emphasizing the shape, the dimensions, and the position of the tumoral mass), the digital tool creates a personalized model predicting the tumor evolution for that particular patient, with or without a drug therapy.