A Multiscale Model for Breast-Conservative Therapy: Computational Framework and Clinical Validation

Breast cancer is the most common cancer among women worldwide and affects 12% of all the women in the USA. There exist different surgical approaches in order to defeat this kind of cancer: the traditional mastectomy (Breast Removal Surgery) and the more recent Breast-Conservative Therapy (BCT), whose goal is to preserve the breast contour and ameliorate the psycological impact of surgery on the patients. This work aims to exploit the BCT field developing a 3D patient-specific multiscale model that could predict the breast shape after lumpectomy, from surgery to complete healing. This model consists of two parts: a hyperelastic Neo-Hookean Finite-Element Model of the breast tissues and skin, and a Cellular Automata model that mimics the biology of healing after surgery. The resulting multiscale model shows results that agree with our theoretical assumptions and gives as outcome the breast contour after surgery depending on the anatomy of the patient and the input from the surgeon. This work is, in fact, the result of an interdisciplinary collaboration between surgeons, mathematicians and computer scientists. A clinical protocol that involves patients eligible for BCT was developed in order to validate this multiscale model with clinical data. The results obtained show the performance of the model and our findings based on the data of the first patient who took part of the study. The model validation gave us an error of maximum 2.5 cm for the surface comparison, which implies the need of further improvements. The Cellular Automata model showed fairly accurate results with the preliminary data, but we need more patients in order to obtain conclusions that are statistically consistent.Computer Science, Department o