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Initial Award Abstract: Effective chemotherapy can potentially treat micro-metastases as well as the primary tumor, reducing the risk of later metastasis and improving overall survival. Chemotherapy implemented prior to surgery (neoadjuvant chemotherapy) allows for the additional benefits of (1) potential tumor down-staging and the option of breast-conserving surgical methods and (2) the ability to assess the tumor's response to chemotherapy while the tumor is still present. Improved methods of monitoring the tumor's response to chemotherapy are needed. Towards that end, we are interested in diffusion-weighted magnetic resonance imaging (DW-MRI) as a non-invasive, non-contrast, and non-ionizing method that could predict early treatment response to neoadjuvant chemotherapy. DW-MRI is sensitive to the random motion of water molecules, allowing for the detection of microscopic changes in cell density and cell content.
This research addresses the questions: (1) Can DW-MRI provide information regarding tumor response that is valuable in predicting immediate and long-term patient outcomes?, (2) Does this information add value to established markers?, and (3) How can DW-MRI analysis be made more automated? These aims will be addressed by: (1) retrospective studies at our breast cancer center in women over age 18 with locally advanced breast cancer and treated with neoadjuvant chemotherapy, (2) multivariate statistical analysis, and (3) image post-processing and computerized algorithms to facilitate the use of DW-MRI in evaluating response to neoadjuvant chemotherapy in the clinical setting.
The overarching goal of our research is to examine and develop the utility of DW-MRI in predicting treatment response and to work towards improving overall outcomes for patients with breast cancer. Earlier determination of response to neoadjuvant chemotherapy could allow ineffective treatment regimens to be discontinued, sparing patients exposure to side effects. Patients could also be switched to alternative chemotherapy regimens, potentially improving treatment response and overall survival from breast cancer.
Final Report: Information regarding tumor size can be obtained from contrast-enhanced magnetic resonance imaging. This size-based information can be valuable in monitoring tumor response to chemotherapy: a significantly decreased tumor size is interpreted as a good response, which can correlate with better long-term patient outcomes; however, this correlation is imperfect. In-vivo, three-dimensional information regarding tumor cellularity, measured as the apparent diffusion coefficient (ADC), can be obtained from diffusion-weighted magnetic resonance imaging (DW-MRI) and we wanted to investigate if tumor ADC could improve the ability to predict tumor response to chemotherapy.
The project funded by this grant has allowed us to investigate the ability of tumor ADC to predict treatment response in patients enrolled in neoadjuvant studies at our institution. Our results from limited patient numbers suggest that tumor ADC may be a predictor of recurrence-free survival and time to recurrence. A major barrier to analysis in current studies is image quality. The echo planar DW-MRI sequences used on scanners today are prone to distortion and other artifacts, impairing the ability to register contrast-enhanced MRI and DW-MRI information and making it sometimes difficult to obtain an accurate ADC measurement. This problem was addressed by collaborating with industry and academia and scanning with a new DW-MRI pulse sequence. The sequence improved in-plane spatial resolution and allowed for artifacts from air-tissue interfaces to be reduced. This sequence could improve DW-MRI acquisition in the breast, allowing for ADC to be more accurately monitored throughout treatment.
Funding from this grant also allowed for improved automation of ADC measurements to be explored. ADC is not the same throughout the tumor and this mixture of different ADCs is not adequately captured by a mean ADC. Methods were developed to calculate parameters related to variation in tumor ADC, and to quantify and map variations in longitudinal ADC change throughout the tumor. In order for ADC information to be analyzed, a region of interest (ROI) must first be defined in the breast and this task can be cumbersome. Tumor ROIs obtained from this manual method were compared to ROIs obtained using an automated method that utilized information regarding tumor volume. A faster method to segment breast tissue was also developed and this method could improve the ability to measure ADC in normal fibroglandular tissue, allowing for changes in normal tissue ADC to be measured throughout treatment.
The results of this study suggest that ADC measurement can be improved and made more time-effective, but these technical advances must be compared to standard methods. The results of this project also suggest that ADC may be valuable in predicting treatment response but validation in large, prospective studies is needed.
- Breast Cancer
Common Scientific Outline (CSO) Research Areas
- 4.2 Early Detection, Diagnosis, and Prognosis Technology and/or Marker Evaluation - Fundamental Parameters