Renewal: Terahertz Polarization Imaging for Detecting Breast Tumor Margins

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Project Summary/Abstract Breast-conserving therapy (lumpectomy) is one of the most commonly performed breast cancer surgeries in the United States. The best outcome of the lumpectomy surgery is achieved when the surgical margins are free of cancer. When remnants of cancer are detected at the surgical margins after the initial operation, a second operation will be required to remove the cancer. Unfortunately, a significant number of patients undergo breast conserving surgery (BCS) at local hospitals that do not have access to immediate on-site pathology leading to high rates of re-excision or reoperation (greater than 30%). Therefore, there is a significant need for new intraoperative technology that can be made available for local hospitals and outpatient clinics. Our previous research concludes that while terahertz studies in pre-clinical models have shown strong differentiation between cancerous and fatty tissues, the more clinically relevant differentiation between cancerous and healthy non-fatty tissue remains challenging. To further build upon the successes of our previous award and improve the sensitivity of terahertz imaging cancer detection on the surgical margins, we have identified areas where we can significantly improve the instrumentation, the animal model, and the image analysis algorithms. As part of this renewal application, we will re-design our instrumentation to develop terahertz polarization- sensitive imaging methodology. In this new approach, all four polarizations of the waves will be incorporated to increase the spatial and spectral information about different types of tumor tissues (Aim 1). We will test the system in vivo in a carcinogen-induced model of breast cancer in rats and use biological tissue simulating phantoms to determine the sources of signal generation in the THz images (Aim 2). Finally, we will improve the detection algorithm accuracy by exploiting the spatial information embedded in the terahertz images with spatial statistics (Aim 3). The goal is to better detect the presence of healthy fibrous tissue due to their potential growth of healthy collagen adjacent to cancerous tissues in tumors. We anticipate that the new proposed approach will increase the image contrast between cancerous and healthy adjacent tissues, leading to better differentiation and classification of cancer on the tumor margins. This renewal application will allow us to develop an optimized approach that leverages multiple polarizations, the spatial information encoded in the terahertz images for analysis, and the validation of our approach on mammary tumors from rats. The success of the proposed research will allow us to expand our work to clinical trials using clinically translational and compatible terahertz technology.