In situ tumor model for longitudinal in silico imaging trials.

In this article, we introduce a computational model for simulating the growth of breast cancer
lesions accounting for the stiffness of surrounding anatomical structures.
Approach: In our model, ligaments are classified as the most rigid structures while the softer parts of the
breast are occupied by fat and glandular tissues As a result of these variations in tissue elasticity, the rapidly
proliferating tumor cells are met with differential resistance. It is found that these cells are likely to circumvent
stiffer terrains such as ligaments, instead electing to proliferate preferentially within the more yielding confines of
the breast's soft topography. By manipulating the interstitial tumor pressure in direct proportion to the elastic
constants of the tissues surrounding the tumor, this model thus creates the potential for realizing a database
of unique lesion morphology sculpted by the distinctive topography of each local anatomical infrastructure.
We modeled the growth of simulated lesions within volumes extracted from fatty breast models, developed by
Graff et al., with a resolution of 50 μm generated with the open-source and readily available VICTRE (Virtual
Imaging Clinical Trials for Regulatory Evaluation) imaging pipeline. To visualize and validate the realism of
the lesion models, we leveraged the imaging component of the VICTRE pipeline, which replicates the Siemens
Mammomat Inspiration mammography system in a digital format. This system was instrumental in generating
digital mammogram (DM) images for each breast model containing the simulated lesions.
Results: By utilizing the DM images, we were able to effectively illustrate the imaging characteristics of the
lesions as they integrated with the anatomical backgrounds.
Significance: The lesion growth model will facilitate and enhance longitudinal in silico trials investigating the
progression of breast cancer.

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