Research

Epidemiological data suggests that different ancestral groups are disproportionally affected
by breast cancer, they are diagnosed at a younger age and diagnosed more frequently with
aggressive subtypes of breast cancer. Socioeconomic differences were previously attributed to the increased incidence of cancer, however, emerging evidence indicates the tumor microenvironment may render patients to be more susceptible to adverse effects of this disease. The ECM provides a favorable environment for the growth of tumor cells, and the differences in the microenvironment may be the decisive factor for the incidence of breast cancer. Utilizing machine learning techniques, tumors are evaluated for morphological variables with emphasis on the ECM. This technology has the potential to be applied to other associated occurrences in tumor prevalence. The long-term goal is to develop an explainable framework from tumor and ECM data which has the potential to determine patient outcomes through identification of cancer patients or at-risk individuals.