We propose to develop integrated high field (3T) magnetic resonance imaging (MRI) and positron emission tomography (PET) methods for assessing the effects of molecularly targeted anti-angiogenesis and cytoxic treatments in breast cancer clinical trials. Our goal is to provide the breast cancer community with practical data acquisition and analysis protocols that facilitate the translation of advanced imaging technologies into patient management and clinical trials. Dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) can report on vascular status, tissue volume fractions, and cellularity, while fluorodeoxythymidine PET (FLT-PET) can report on cell proliferation. We propose to combine these MRI and PET data to provide anatomical, physiological, and molecular assessments of the response of breast tumors to novel anti-angiogenic and cytoxic treatments in clinical trials. To accomplish these goals we will pursue the following specific aims: 1. We will develop high field breast MRI protocols that measure tissue cellularity and vascularity. We will then develop methods for the rigorous registration of these MRI measures with quantitative PET characterization of cell proliferation. We will develop the algorithms and software architecture necessary for synthesizing the imaging data with (traditional) clinical data to assisting in clinical decision making. 2. In an ongoing Phase II study we will employ DCE-MRI, DW-MRI, and FLT-PET to assess the degree of tumor response after one and two cycles of Carboplatin and nab-Paclitaxel with or without Vorinostat in HER2-negative primary operable breast cancer. 3. In our planned Phase II study we will employ DCE-MRI, DW-MRI, and FLT-PET to assess the degree of tumor response after one and two cycles of neoadjuvant cisplatin, paclitaxel and the TOI inhibitor everolimus in patients with triple negative breast tumors. As the anti-cancer agents employed in these clinical trials are implicated in apoptosis and/or inhibition of cellular proliferation and/or inhibition of angiogenesis, we hypothesize that changes in metrics of cellular proliferation and vascularity, when merged with traditional clinical biomarkers, will provide significantly more accurate predictions on patient response than traditional methods of tumor response including RECIST.
We propose to develop integrated magnetic resonance imaging (MRI) and positron emission tomography (PET) methods for assessing the effects of molecularly targeted treatments in breast cancer clinical trials. We hypothesize that the synthesis of imaging metrics reporting on vascularity, cellularity, and cell proliferation will provide predictive measurements of tumor response to treatment in appropriately selected clinical trials. Our goal is to provide the breast cancer community with practical data acquisition and analysis protocols that facilitate the translation of advanced imaging technologies into patient management and clinical trials.
|Li, Xia; Arlinghaus, Lori R; Ayers, Gregory D et al. (2014) DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings. Magn Reson Med 71:1592-602|
|Fedorov, Andriy; Fluckiger, Jacob; Ayers, Gregory D et al. (2014) A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation. Magn Reson Imaging 32:321-9|
|Zhu, He; Arlinghaus, Lori R; Whisenant, Jennifer G et al. (2014) Sequence design and evaluation of the reproducibility of water-selective diffusion-weighted imaging of the breast at 3?T. NMR Biomed 27:1030-6|
|Atuegwu, Nkiruka C; Li, Xia; Arlinghaus, Lori R et al. (2014) Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results. Med Phys 41:052302|
|Fluckiger, Jacob U; Loveless, Mary E; Barnes, Stephanie L et al. (2013) A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results. Phys Med Biol 58:1983-98|
|Klomp, Dennis W J; Dula, Adrienne N; Arlinghaus, Lori R et al. (2013) Amide proton transfer imaging of the human breast at 7T: development and reproducibility. NMR Biomed 26:1271-7|
|Weis, Jared A; Miga, Michael I; Arlinghaus, Lori R et al. (2013) A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy. Phys Med Biol 58:5851-66|
|Abramson, Richard G; Li, Xia; Hoyt, Tamarya Lea et al. (2013) Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results. Magn Reson Imaging 31:1457-64|
|Smith, David S; Li, Xia; Gambrell, James V et al. (2012) Robustness of quantitative compressive sensing MRI: the effect of random undersampling patterns on derived parameters for DCE- and DSC-MRI. IEEE Trans Med Imaging 31:504-11|
|Levy, Mia A; Rubin, Daniel L (2011) Current and future trends in imaging informatics for oncology. Cancer J 17:203-10|
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