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Last Updated: 11/19/19
Quantitative Imaging Network (QIN)

Vanderbilt University

Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant

Richard Abramson, M.D., Thomas E. Yankeelov. Ph.D.;
Grant Number: U01 CA142565

The long-term vision of this program is to improve patient care by optimizing, validating, and extending quantitative MRI methods for the early prediction of breast cancer response to neoadjuvant therapy (NAT). During the first period of support, the team developed several experimental and computational tools for improving quantitative dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted MRI (DW-MRI) of the breast. These tools were successfully applied in clinical trials and the resulting data were incorporated into a statistical model to predict, after only one cycle of treatment, the eventual pathological complete response (pCR) of breast tumors to NAT. The team now has the opportunity to deploy these techniques in two multi-site clinical trials, focused on triple negative breast cancer (TNBC), to be opened simultaneously at Vanderbilt University and the University of Chicago. These trials offer the opportunity to validate and then extend their imaging techniques in both simple and complex trial environments. The team has identified the following Specific Aims to be accomplished:

Aim 1. Optimize quantitative DCE- and DW-MRI for two multi-site breast cancer clinical trials

Aim 2. Validate quantitative MRI for predicting breast cancer treatment response early during NAT

Aim 3. Extend quantitative MRI by predicting breast cancer treatment response during a complex NAT trial

The overarching hypothesis (guided by the results from the first period of support) is that the synthesis of quantitative DCE- and DW-MRI measured after the first cycle of NAT which should achieve an area under the receiver operating characteristic curve of at least 0.87 for predicting the eventual response of TNBC patients to NAT. If this hypothesis is validated, the team will be able to provide significant direction on developing personalized treatment strategies for this important patient population. Furthermore, they will be well-positioned to proceed to larger multi-site trials — a necessary step towards adoption into routine clinical algorithms.


  1. Hormuth DA, Sorace AG, Virostko J, Abramson RG, Bhujwalla ZM, Enriquez-Navas P, Gillies R, Hazle JD, Mason RP, Quarles CC, Weis JA, Whisenant JG, Xu J, Yankeelov TE. Translating pre-clinical magnetic resonance imaging methods to clinical oncology. Journal of Magnetic Resonance Imaging, 2019 Mar 29. doi: 10.1002/jmri.26731.

  2. Virostko J, Sorace AG, Wu C, Ekrut D, Jarrett AM, Upadhyaya RM, Avery S, Patt D, Boone Goodgame B, Yankeelov TE. Magnetization Transfer MRI of Breast Cancer in the Community Setting: Reproducibility and Preliminary Results in Neoadjuvant Therapy. Tomography, 2019;5:44-52.

  3. Wu C, Pinedo F, Hormuth DA II, Karczmar GS, Yankeelov TE. Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors. Magnetic Resonance in Medicine, 2019;81:2147-60.

  4. Hormuth II DA, Jarrett AM, Lima EABF, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism based modeling of tumor growth and treatment response constrained by multiparametric imaging data. JCO Clin Cancer Informatics, 2019;3:1-10.

  5. Jarrett A, Hormuth DA II Barnes SL, Feng X, Huang W, Yankeelov TE. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Physics in Medicine and Biology, 2018;63:105015.

  6. Mckenna MT, Weis JA, Brock A, Quaranta V, Yankeelov TE. Precision Medicine with Imprecise Therapy: Computational Modeling for Chemotherapy in Breast Cancer. Translational Oncology, 2018;11:732-742.

  7. Sorace AG, Wu C, Barnes SL, Jarrett A, Avery S, Patt D, Goodgame B, Luci JJ, Kang H, Abramson RG, Yankeelov TE, Virostko J. Repeatability, reproducibility, and accuracy of quantitative MRI of the breast in the community radiology setting. Journal of Magnetic Resonance Imaging, 2018, Mar 23. doi: 10.1002/jmri.26011.

  8. Woodall R, Eldridge SL, Hormuth DA, Sorace AG, Quarles CC, Yankeelov TE. The effects of intra-voxel contrast agent diffusion on the analysis of DCE-MRI data in realistic tissue domains. Magnetic Resonance in Medicine, 2018;21:17-29.

  9. Kang H, Hainline A, Arlinghaus LR, Elderidge SL, Li X, Abramson VG, Chakravarthy AB, Abramson RG, Bingham B, Fakhoury K, Yankeelov TE. Combining multi-parametric MRI with receptor information to optimize prediction of pathologic response to neoadjuvant therapy in breast cancer: Preliminary results. Journal of Medical Imaging, 2018;5:011015.

  10. Virostko J, Hainline A, Kang H, Arlinghaus L, Abramson RG, Barnes SL, Blume JD, Avery S, Patt D, Goodgame B, Yankeelov TE, Sorace AG. Dynamic Contrast-Enhanced MRI and Diffusion-Weighted MRI for Predicting the Response of Locally Advanced Breast Cancer to Neoadjuvant Therapy: A Meta-analysis. Journal of Medical Imaging, 2018;5:011011.

  11. Sorace AG, Partridge SC, Li X, Virostko J, Barnes SL, Huang W, Hippe DS, Yankeelov TE. Distinguishing Benign and Malignant Breast Tumors: Preliminary Comparison of Kinetic Modeling Approaches Using Multi-Institutional DCE-MRI Data from the IBMC 6883 Trial. Journal of Medical Imaging, 2018;5:011019.