PET-MRI for Assessing Treatment Response in Breast Cancer Clinical Trials

Summary

Principal Investigator: Thomas E Yankeelov
Abstract: DESCRIPTION (provided by applicant): 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. RELEVANCE: 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.
Funding Period: 2010-05-01 - 2015-04-30
more information: NIH RePORT

Top Publications

  1. pmc The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer
    Thomas E Yankeelov
    Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
    Semin Oncol 38:16-25. 2011
  2. pmc A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results
    Jacob U Fluckiger
    Department of Radiology, Northwestern University Chicago, IL 60611, USA
    Phys Med Biol 58:1983-98. 2013
  3. pmc Amide proton transfer imaging of the human breast at 7T: development and reproducibility
    Dennis W J Klomp
    Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands Institute of Imaging Science, Vanderbilt, Nashville, USA
    NMR Biomed 26:1271-7. 2013
  4. pmc Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
    Subramani Mani
    Division of Translational Informatics, Department of Medicine, University of New Mexico, Albuquerque, New Mexico 87131 0001, USA
    J Am Med Inform Assoc 20:688-95. 2013
  5. pmc DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings
    Xia Li
    Vanderbilt University Institute of Imaging Science VUIIS, Vanderbilt University, Nashville, Tennessee, USA
    Magn Reson Med 71:1592-602. 2014
  6. pmc A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy
    Jared A Weis
    Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
    Phys Med Biol 58:5851-66. 2013
  7. pmc Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results
    Richard G Abramson
    Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN Institute of Imaging Science, Vanderbilt University, Nashville, TN Vanderbilt Ingram Center, Vanderbilt University, Nashville, TN Electronic address
    Magn Reson Imaging 31:1457-64. 2013
  8. pmc Potential of compressed sensing in quantitative MR imaging of cancer
    David S Smith
    Institute of Imaging Science, Departments of Radiology and Radiological Sciences
    Cancer Imaging 13:633-44. 2013
  9. pmc A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation
    Andriy Fedorov
    Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Boston, Massachusetts 02115 Electronic address
    Magn Reson Imaging 32:321-9. 2014
  10. pmc Amide proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response
    Adrienne N Dula
    Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 2310, USA
    Magn Reson Med 70:216-24. 2013

Research Grants

  1. CANCER CENTER SUPPORT GRANT
    TONY R HUNTER; Fiscal Year: 2013
  2. Signaling in Inflammation, Stress, and Tumorigenesis
    GEORGE ROBERT STARK; Fiscal Year: 2013
  3. Chicago Prevention and Intervention Epicenter (Chicago PIE)
    ROBERT ALAN WEINSTEIN; Fiscal Year: 2013

Detail Information

Publications26

  1. pmc The role of magnetic resonance imaging biomarkers in clinical trials of treatment response in cancer
    Thomas E Yankeelov
    Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
    Semin Oncol 38:16-25. 2011
    ..We also discuss the limitations and future research directions required for these techniques to gain greater acceptance and to have their maximum impact...
  2. pmc A diffusion-compensated model for the analysis of DCE-MRI data: theory, simulations and experimental results
    Jacob U Fluckiger
    Department of Radiology, Northwestern University Chicago, IL 60611, USA
    Phys Med Biol 58:1983-98. 2013
    ..0 in 40% of the voxels as compared to only 16% for the DTK model. The DTK model presented here shows promise in estimating accurate kinetic parameters in the presence of passive contrast agent diffusion...
  3. pmc Amide proton transfer imaging of the human breast at 7T: development and reproducibility
    Dennis W J Klomp
    Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands Institute of Imaging Science, Vanderbilt, Nashville, USA
    NMR Biomed 26:1271-7. 2013
    ..Together, these results demonstrate that the APT effect can be reliably detected in the healthy human breast with a high level of precision at 7T...
  4. pmc Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
    Subramani Mani
    Division of Translational Informatics, Department of Medicine, University of New Mexico, Albuquerque, New Mexico 87131 0001, USA
    J Am Med Inform Assoc 20:688-95. 2013
    ..To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC)...
  5. pmc DCE-MRI analysis methods for predicting the response of breast cancer to neoadjuvant chemotherapy: pilot study findings
    Xia Li
    Vanderbilt University Institute of Imaging Science VUIIS, Vanderbilt University, Nashville, Tennessee, USA
    Magn Reson Med 71:1592-602. 2014
    ....
  6. pmc A mechanically coupled reaction-diffusion model for predicting the response of breast tumors to neoadjuvant chemotherapy
    Jared A Weis
    Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
    Phys Med Biol 58:5851-66. 2013
    ..84, p < 0.01), and show a statistically significant >4 fold reduction in model/data error (p = 0.02) as compared to the non-mechanically coupled model. ..
  7. pmc Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results
    Richard G Abramson
    Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN Institute of Imaging Science, Vanderbilt University, Nashville, TN Vanderbilt Ingram Center, Vanderbilt University, Nashville, TN Electronic address
    Magn Reson Imaging 31:1457-64. 2013
    ....
  8. pmc Potential of compressed sensing in quantitative MR imaging of cancer
    David S Smith
    Institute of Imaging Science, Departments of Radiology and Radiological Sciences
    Cancer Imaging 13:633-44. 2013
    ..We finally illustrate applications of the technique by describing examples of CS in dynamic contrast-enhanced MRI and dynamic susceptibility contrast MRI. ..
  9. pmc A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: a step towards practical implementation
    Andriy Fedorov
    Department of Radiology, Brigham and Women s Hospital, Harvard Medical School, Boston, Massachusetts 02115 Electronic address
    Magn Reson Imaging 32:321-9. 2014
    ..These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting. ..
  10. pmc Amide proton transfer imaging of the breast at 3 T: establishing reproducibility and possible feasibility assessing chemotherapy response
    Adrienne N Dula
    Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 2310, USA
    Magn Reson Med 70:216-24. 2013
    ..Together, these results suggest that APT imaging may report on treatment response in these patients...
  11. pmc Simultaneous PET-MRI in oncology: a solution looking for a problem?
    Thomas E Yankeelov
    Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
    Magn Reson Imaging 30:1342-56. 2012
    ..We consider the relative advantages and disadvantages afforded by PET-MRI and summarize current opinions and evidence as to the likely value of PET-MRI in the management of cancer...
  12. pmc Motion correction in diffusion-weighted MRI of the breast at 3T
    Lori R Arlinghaus
    Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
    J Magn Reson Imaging 33:1063-70. 2011
    ..To provide a quantitative assessment of motion and distortion correction of diffusion-weighted images (DWIs) of the breast and to evaluate the effects of registration on the mean apparent diffusion coefficient (mADC)...
  13. pmc Magnetic resonance in the era of molecular imaging of cancer
    John C Gore
    Vanderbilt University Institute of Imaging Science AA1105 MCN, Vanderbilt University Nashville, TN 37232 2310, USA
    Magn Reson Imaging 29:587-600. 2011
    ....
  14. pmc On the relationship between the apparent diffusion coefficient and extravascular extracellular volume fraction in human breast cancer
    Lori R Arlinghaus
    Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232, USA
    Magn Reson Imaging 29:630-8. 2011
    ..These data, combined with similar results from other disease sites in the literature, may indicate that the conventional interpretation of either ADC, v(e) or their relationship is not sufficient to explain experimental findings...
  15. pmc Quantitative effects of using compressed sensing in dynamic contrast enhanced MRI
    David S Smith
    Institute of Imaging Science, Vanderbilt University, Nashville, TN 37212, USA
    Phys Med Biol 56:4933-46. 2011
    ..2%, respectively; at 3×, 3.6% and -10%, respectively; at 4×, 7.8% and -12%, respectively. These results suggest that CS combined with appropriate reduced acquisitions may be an effective approach to improving image quality in DCE-MRI...
  16. pmc Current and future trends in imaging informatics for oncology
    Mia A Levy
    Department of Biomedical Informatics and Medicine, Division of Hematology and Oncology, Vanderbilt University, Nashville, TN, USA
    Cancer J 17:203-10. 2011
    ..With the ongoing development of novel imaging modalities and imaging biomarkers, we expect these systems will continue to evolve and mature...
  17. pmc A novel AIF tracking method and comparison of DCE-MRI parameters using individual and population-based AIFs in human breast cancer
    Xia Li
    Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
    Phys Med Biol 56:5753-69. 2011
    ..76, 0.84 and 0.68 for K(trans), v(p) and v(e), respectively. This work indicates that K(trans) and v(p) show good agreement between AIF(pop) and AIF(ind) while there is a weak agreement on v(e)...
  18. pmc Integration of diffusion-weighted MRI data and a simple mathematical model to predict breast tumor cellularity during neoadjuvant chemotherapy
    Nkiruka C Atuegwu
    Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 2310, USA
    Magn Reson Med 66:1689-96. 2011
    ..95 (P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 (P < 0.05)...
  19. pmc Robustness of quantitative compressive sensing MRI: the effect of random undersampling patterns on derived parameters for DCE- and DSC-MRI
    David S Smith
    Institute of Imaging Science, Vanderbilt University, Nashville, TN 37240 USA
    IEEE Trans Med Imaging 31:504-11. 2012
    ..Across 11 000 different CS reconstructions, we saw no outliers in the distribution of parameters, suggesting that, despite the random undersampling schemes, CS accelerated quantitative MRI may have a predictable level of performance...
  20. pmc Statistical comparison of dynamic contrast-enhanced MRI pharmacokinetic models in human breast cancer
    Xia Li
    Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee 37232 2310, USA
    Magn Reson Med 68:261-71. 2012
    ..Thus, these four metrics indicate that the FXL_v(p) and the FXR models provide the most complete statistical description of dynamic contrast-enhanced MRI time courses for the patients selected in this study...
  21. pmc Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning
    Subramani Mani
    Vanderbilt University, Nashville, TN, USA
    AMIA Annu Symp Proc 2011:868-77. 2011
    ..The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96...
  22. pmc Quantitative metrics in clinical radiology reporting: a snapshot perspective from a single mixed academic-community practice
    Richard G Abramson
    Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
    Magn Reson Imaging 30:1357-66. 2012
    ..Validation in real-world settings, ease of use, and reimbursement economics will all play a role in determining the rate of translation of AQMs into broad practice...
  23. pmc Longitudinal, intermodality registration of quantitative breast PET and MRI data acquired before and during neoadjuvant chemotherapy: preliminary results
    Nkiruka C Atuegwu
    Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee 37232 2310 and Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232 2675
    Med Phys 41:052302. 2014
    ..The authors propose a method whereby serially acquired DCE-MRI, DW-MRI, and FDG-PET breast data sets can be spatially and temporally coregistered to enable the comparison of changes in parameter maps at the voxel level...

Research Grants30

  1. CANCER CENTER SUPPORT GRANT
    TONY R HUNTER; Fiscal Year: 2013
    ....
  2. Signaling in Inflammation, Stress, and Tumorigenesis
    GEORGE ROBERT STARK; Fiscal Year: 2013
    ..abstract_text> ..
  3. Chicago Prevention and Intervention Epicenter (Chicago PIE)
    ROBERT ALAN WEINSTEIN; Fiscal Year: 2013
    ..The impact on ICU infection and prescribing characteristics of doctors will be assessed. To further assess the interventions, costs of averted outcomes and of the interventions will be compared. OPRIONAL OBEJCTIVE SCORE: 2 ..