Neoadjuvant or preoperative chemotherapy is becoming an important part in breast-cancer treatment and management. In parallel it is important to develop reliable monitoring methods to evaluate the response. Dynamic contrast enhanced MRI (DCE-MRI) has been proven as the most accurate imaging modality to predict residual tumor responses. In this study we will apply serial DCE-MRI studies to evaluate the pre-treatment lesion characteristic features, along with the early response patterns determined in follow-up MRI studies to aim for accurate prediction of pathologic complete remission (pCR), which is the most relevant prognostic factor in patients receiving neoadjuvant chemotherapy. At our institution we have an active on-going neoadjuvant chemotherapy protocol. In previous patient cohorts we have demonstrated that MRI is very helpful to provide response-monitoring information for timely adjustment of regimens. Based on these results we have determined the treatment protocols to be used in the current study. Patients will receive 2 cycles AC treatment (doxorubicin and cyclophosphamide) then followed by 4 cycles Taxane regimen (Nab-paclitaxel with Carboplatin, with trastuzumab for HER2-positive cancer and bevacizumab for HER2-negative cancer). In the current proposal we will take the role of MRI one step further, to investigate whether the pre-treatment tumor characteristics, including morphology, texture, and vascular parameters, as well as metabolic information, can be used to predict whether a patient will achieve pCR. The information that we obtain will reveal the differences between these two groups, and that can be used to build a model to predict the likelihood of achieving pCR. If a patient is expected to show a good response, the protocol can be given as it is. If a patient is expected not to show a good response to reach pCR, a modified protocol should be considered. The response monitoring data may also provide information for modifying the regimen to reach an improved efficacy. The early response after 2 cycles AC, then after addition of 1 cycle of taxane will be evaluated. Overall we will evaluate the accuracy of using pre-treatment MRI characteristic parameters, and early tumor response patterns during the treatment course, to differentiate between patients who achieve pCR vs. those who do not. It is hypothesized that a higher accuracy in prediction of pCR can be achieved using combined pre-treatment lesion characteristics and early tumor response patterns compared to that using either set of data alone.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA127927-05
Application #
8209167
Study Section
Cancer Biomarkers Study Section (CBSS)
Program Officer
Henderson, Lori A
Project Start
2008-04-01
Project End
2013-03-31
Budget Start
2012-02-01
Budget End
2013-03-31
Support Year
5
Fiscal Year
2012
Total Cost
$489,463
Indirect Cost
$165,701
Name
University of California Irvine
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
046705849
City
Irvine
State
CA
Country
United States
Zip Code
92697
Wang, Juan; Fang, Zhiyuan; Lang, Ning et al. (2017) A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks. Comput Biol Med 84:137-146
Lang, Ning; Su, Min-Ying; Xing, Xiaoying et al. (2017) Morphological and dynamic contrast enhanced MR imaging features for the differentiation of chordoma and giant cell tumors in the Axial Skeleton. J Magn Reson Imaging 45:1068-1075
Lang, Ning; Yuan, Huishu; Yu, Hon J et al. (2017) Diagnosis of Spinal Lesions Using Heuristic and Pharmacokinetic Parameters Measured by Dynamic Contrast-Enhanced MRI. Acad Radiol 24:867-875
Chen, Jeon-Hor; Liao, Fuyi; Zhang, Yang et al. (2017) 3D MRI for Quantitative Analysis of Quadrant Percent Breast Density: Correlation with Quadrant Location of Breast Cancer. Acad Radiol 24:811-817
Chan, Siwa; Chen, Jeon-Hor; Li, Shunshan et al. (2017) Evaluation of the association between quantitative mammographic density and breast cancer occurred in different quadrants. BMC Cancer 17:274
Kim, Min Jung; Su, Min-Ying; Yu, Hon J et al. (2016) US-localized diffuse optical tomography in breast cancer: comparison with pharmacokinetic parameters of DCE-MRI and with pathologic biomarkers. BMC Cancer 16:50
Chen, Jeon-Hor; Chan, Siwa; Lu, Nan-Han et al. (2016) Opportunistic Breast Density Assessment in Women Receiving Low-dose Chest Computed Tomography Screening. Acad Radiol 23:1154-61
Fwu, Peter T; Chen, Jeon-Hor; Li, Yifan et al. (2015) Quantification of Regional Breast Density in Four Quadrants Using 3D MRI-A Pilot Study. Transl Oncol 8:250-7
Chen, Jeon-Hor; Gulsen, Gultekin; Su, Min-Ying (2015) Imaging Breast Density: Established and Emerging Modalities. Transl Oncol 8:435-45
Wu, Anna H; Spicer, Darcy; Garcia, Agustin et al. (2015) Double-Blind Randomized 12-Month Soy Intervention Had No Effects on Breast MRI Fibroglandular Tissue Density or Mammographic Density. Cancer Prev Res (Phila) 8:942-51

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