Towards a decisional support system in breast cancer surgery based on mass transfer modeling

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 a great potential of application, when diagnostic imaging evaluation is used to identify the metabolic tumor volume undergoing proliferation. With breast carcinoma one of the most common cancer occurrences, the personal involvement for the patient and the cost for the national health system vary considerably with the adopted treatment. When a neoadjuvant (volume reducing) drug therapy is a direct option, the choice of drug may dictate the physical burden and the surgical strategy for subsequent lumpectomy/mastectomy. This paper employs a mass transfer modeling approach in order to gain deeper insight into breast carcinoma proliferation and therapy at the tissue scale. The aim is to develop a predictive, quantitative method for each given patient. Model flexibility is demonstrated, to facilitate model replication for large patient cohorts. The proposed procedure may serve as a basis for a decision support system for surgeons and towards personalized treatment optimization of breast tumors.