Predicting Neoadjuvant Chemo Response/Prognosis Using Imaging Biomarkers

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DESCRIPTION (provided by applicant): The optimal goal of neoadjuvant chemotherapy (NAC) for breast cancer is to achieve pathologic complete response (pCR) with the least toxicity. As more effective therapies become available, one challenge is how to evaluate the response of tumor in a timely manner, so an optimal regimen can be given to the patient. Reliable imaging methods that can monitor and predict NAC response are critically needed. In addition to therapy response monitoring, imaging parameters are associated with phenotype of cancer and may be used as prognostic markers. We have been performing NAC studies for close to 10 years, and in this renewal we will continue to develop and validate imaging markers for evaluating and predicting therapeutic response, as well as for predicting prognosis. Three aims are proposed. Aim-1 will follow all previously enrolled NAC patients since 2003 (more than 180 patients) to obtain their prognostic information. Other than previously analyzed imaging parameters, two novel imaging markers that are shown to have prognostic value, i.e. the change of breast density (fibroglandular tissue volume) and the stromal tissue contrast enhancement, will be analyzed using a comprehensive quantitative method developed by us. The primary analysis is to use imaging parameters to predict local recurrence, but the disease-free and overall survival will be collected and used in exploratory analyses. Aim-2 and -3 will be performed using the prospectively collected data from a new clinical trial to enroll 75 NAC patients into an imaging monitoring study by using a combined MRI and scinti-mammography (MR-SMM) system. Despite of the research interest in investigating the role of complex multi- parametric imaging methods, there is always a need of an easy method that can measure the tumor size accurately; and SMM has a great potential. Aim-2 will compare the diagnostic performance of SMM and MRI at different times before, during, and after NAC. Also, the diagnostic accuracy of the post-NAC residual disease measured by SMM and MRI will be determined by comparing to pathological examination results as the goldstandard. Aim-3 will evaluate the respective and combined ability of MRI and SMM, based on pre-treatment and early changes in the first follow-up imaging, for predicting pathologic complete response (pCR). The diagnostic results obtained using MRI and SMM from tumors of varying stages at different times during NAC will provide important information for establishing the role of a standalone SMM system, as well as the combined MR-SMM system, for management of breast cancer patients.