Title: Diffuse Optical Tomography Imaging of Chemotherapy-Induced Changes in Breast Tissue Metabolism
Description: Breast cancer is fast becoming the leading cause of mortality in women worldwide. As of this year, there are more than 3.1 million women with a history of breast cancer in the U.S., and about 41,760 women are expected to die from this disease. Neoadjuvant chemotherapy (NAC) has become a well-established therapy in the treatment of patients with locally advanced or primarily inoperable breast cancer. It consists of 3-9 months of drug treatment to shrink the tumor size before surgical removal of any remaining mass. A pathological complete response (pCR) is defined as complete disappearance of the tumor before surgery and correlates with 5-year overall survival of the treated patient. However, only 15-40% of subjects who undergo NAC will achieve a pCR, while the remaining patients do not benefit from a therapy that has considerable side effects. In this Ph.D. thesis, I explore the potential of diffuse optical tomography (DOT) for breast cancer imaging and NAC monitoring. The overall objective is two-fold.

First, I seek to identify breast cancer patients who will not respond to NAC shortly after the initiation of a 5-9 months therapy regimen. Identifying these patients early will allow a switch to a more promising therapy and avoiding months of ineffective therapy with a drug regimen that has considerable side effects. Second, I use the optical data simultaneously obtained from the contralateral, non-tumor bearing breast to better understand the factors that modulate breast density and the source of its contrast in DOT. This work analyzed DOT data from 105 patients with stage II-III breast cancer under NAC regimen. Data processing and image analysis protocols were developed to more effectively evaluate static tissue contrast and dynamic functional imaging of the breast. Notably, we observed that there are differences in the time evolution of DOT features between pCR and non-pCR tumors under NAC, and DOT features can contribute to the successful prediction of pCR status from pretreatment imaging.

Lastly, our analysis demonstrated a positive correlation between DOT feature and mammographic density classification, which could lead to research on the potential use of DOT as a predictor of breast cancer as well as an assessment tool to longitudinally evaluate the efficacy of chemoprevention strategies. These findings represent important steps towards the translation of DOT into current clinical workflow to contribute to better-personalized breast cancer therapies and breast cancer risk management.