Breast cancer treatment with neoadjuvant chemotherapy (NAC) can provide an opportunity for achieving a major decrease in recurrence and death rates by down-staging the tumor while improving breast preservation. However, the dilemma for patients is that NAC is only effective in about 70% of patients and the response is determined late or on completion of therapy with pathologic assessment of surgically excised tissue following NAC used to determine long-term disease-free survival. Ineffective therapy decreases quality of life, increases costs, and delays commencement of effective treatment. In this proposal, development of a noninvasive imaging biomarker which could provide for very early prediction of long-term outcome would revolutionize the clinical care of breast cancer patients. Furthermore, imaging would provide patient care to be individualized by providing an opportunity to adapt systemic treatment to a particular patient. This proposal will undertake a comprehensive approach to develop imaging protocols and methods for applying diffusion-weighted MRI for management of breast cancer patients through availability of high-quality clinical data, analytical algorithm development, advances in quality control and software implementation: 1) Use of clinical data obtained from two multi-center prospective clinical trials (Cancer Research sponsored UK trial entitled """"""""Establishing the Efficacy of Advanced Semi-automated Functional MR Imaging in the Early Prediction of Response of Locally Advanced Breast Cancer to NAC"""""""" as well as the I-Spy 2 clinical trial entitled """"""""An adaptive breast cancer trial design in the setting of NAC"""""""";2) Implementation of quality assurance methods using a novel temperature controlled phantom;3) Development of analytical algorithms using deformable registration for novel, voxel-based as well as ROI-based analysis of DW-MRI data sets to enhance the sensitivity of the imaging response biomarker;4) Development and validation of a software application for turn-key analysis of DW-MRI data. MRI-derived quantitative measurements of response will be evaluated as early response predictors of clinical outcome measures using novel analytical approaches, i.e. functional diffusion mapping (fDM) on registered data sets along with alternative ROI statistics. Quality assurance methods will be developed from multi-center use of our MR phantom. An industrial partnership/collaboration with a major image workstation manufacturer will assist with development of a platform with a complete software algorithmic solution for use as a clinical decision tool. Development of an early quantitative imaging biomarker based on DW-MRI data would provide for individualized patient care.
Neoadjuvant chemotherapy of breast cancer patients allows a patient with the opportunity to forego upfront surgical research in an effort to first reduce the size of the lesion. This approach is ineffective in about 30% of patients, but response is normally determined late or on completion of therapy (4-6 months). Ineffective therapy decreases quality of life, increases costs, and delays commencement of effective treatment. Development of diffusion-MRI as an early imaging biomarker would allow for individualized patient treatment.
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