Computational model of tumor growth for in silico trials
Aunnasha Sengupta, Diksha Sharma, Aldo G. Badano · 2021
Digital breast tomosynthesis (DBT) can improve the detectability of breast cancer by eliminating overlapping breast tissues that affects the performance of digital mammography (DM) systems. It is yet to be established if DBT can detect lesions at earlier progression stages than DM. To pursue this investigation using in-silico methods, it is necessary to develop computational models that mimic the growth of cancerous lesions. We report on a novel computational model that mimics the progression of breast tumors based on underlying biological and physiological phenomena. Our model includes anisotropic growth and irregularly shaped lesions commonly seen in breast cancer. Our method relies on the assumption that tumor shape is ultimately determined by pressure fields given by surrounding anatomical structures causing the lesion to preferentially proliferate in certain directions. By varying the direction of tumor growth via pressure maps, we simulated various anisotropic lesions seen in clinical cases. We used the open-source, freely available VICTRE imaging pipeline to obtain DM images of growing lesions within breast models and depict several time points in the growth of the tumor as seen by this imaging modality.