Development of Magnetic Resonance Fingerprinting (MRF) to Assess Response to Neoadjuvant Chemotherapy in Breast Cancer - Abstract In women, breast cancer is the most commonly diagnosed cancer and leading cause of cancer related deaths worldwide, with approximately 2.3 million new cases and 685,000 deaths in 2020. Neoadjuvant chemotherapy (NAC) is commonly applied to reduce the tumor size before surgery for breast neoplasms. Unfortunately, due to the genetic and phenotypic heterogeneity of breast tumors, not all patients respond to conventional NAC. Currently, only about 22% of patients show pathologic complete response (pCR), while the remaining non-pCR patients show either partial response (54% of all patients) or no response to chemotherapy. Early prediction of tumor response to chemotherapy to identify non-responders could 1) reduce unnecessary side effects and costs related to ineffective therapy, and 2) help physicians tailor the treatment plan earlier to achieve better therapeutic outcomes and improve survival. Monitoring tumor response to chemotherapy is currently based on tumor size measured by physical exam, which is subjective, difficult to quantify, and most importantly, temporally delayed compared to underlying biological changes. Quantitative, repeatable and objective methods that could provide an early detection of tumor physiological changes before size changes could significantly improve treatment outcome and the quality of patient care. However, quantitative imaging poses significant technical challenges, which is rarely performed in the clinical setting. Here, we propose to leverage Magnetic Resonance Fingerprinting (MRF), a revolutionary new platform for quantitative MR that was invented by our team, to develop new imaging biomarkers for early assessment of treatment response in women with breast cancer. Our team has developed a breast MRF method to simultaneously generate quantitative 3D T1 and T2 maps in ~6 minutes with excellent reproducibility. We have also expanded our MRF method to simultaneous quantify T1, T2 and ADC maps of the brain with no image distortion. Here, we plan on optimizing this new relaxometry / diffusion MRF method specifically for women with breast cancer (Aim 1). Novel deep learning methods will be developed to provide a fast (<5 minute) and high resolution (1.2 mm isotropic) acquisition for whole-breast coverage along with an efficient post-processing pipeline based on cloud computation (Aim 2). Finally, we will evaluate the developed method for early prediction of treatment response in two patient cohorts with either HER2-positive or triple negative breast cancers (Aim 3). Upon successful completion of this project, the developed MRF technique will provide a practical quantitative breast exam for early prediction of treatment response to NAC and other treatment methods (hormone therapy, antibody-based target therapy, etc.) for women with breast cancer, with the ultimate goal to reduce ineffective treatment in eligible subjects and tailor the treatment methods for optimum therapeutic outcomes.
