Ductal carcinoma in situ (DCIS)--a type of breast cancer whose growth
is confined to the duct lumen--is a significant precursor to invasive
breast carcinoma. DCIS is commonly detected as a subtle pattern of
calcifications in mammograms. Radiologic imaging (including
mammography) is used to plan surgical resection of the tumor
(lumpectomy), but multiple surgeries are often required to fully
eliminate DCIS. On the other hand, pathologists use pre-surgical
biopsies to stage the DCIS, assess its metastatic potential, and
choose adjuvant therapies. There is currently no technique to combine
these data to improve surgical and therapeutic planning. Mechanistic,
patient-tailored computational models may provide such a link between
multiple data types. In this talk, we focus on developing and
calibrating biologically-grounded mathematical models to individual
patients, encouraging (and validated!) results in quantitatively
predicting clinical progression, the implications for making and
quantitatively testing biological hypotheses, and the role of
mathematical modeling in facilitating a deeper understanding of
pathology and mammography.