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Last Updated: 09/21/17
Quantitative Imaging Network (QIN)

Oregon Health & Science University

Shutter-Speed Model DCE-MRI for Assessment of Response to Cancer Therapy

Wei Huang, Ph.D
Grant Number: U01 CA154602

In clinical oncology and drug development there is genuine need for sensitive and reproducible quantitative imaging methods for early prediction of therapy response and accurate assessment of post-therapy residual cancer. Such methods have the potential to spare non-responding patients from ineffective therapies and associated long and short term toxicity, improve clinical management of individual patient, and accelerate efficacy evaluation of novel therapies. Dynamic-Contrast-Enhanced (DCE) MRI can provide an excellent measure of therapy-induced changes in tumor microvasculature. The Standard-Model (SM) for pharmacokinetic DCE-MRI analysis incorrectly assumes effectively infinitely fast equilibrium inter-compartmental water exchange kinetics and, as a result, often underestimates the pharmacokinetic parameters Ktrans and ve. The Shutter-Speed Model (SSM) accounts for finite water exchange kinetics effects and corrects the imaging biomarker underestimations. SSM parameters have proven more sensitive to vascular changes than the SM parameters. In addition, SSM DCE-MRI allows quantification of a novel imaging biomarker, τi (mean intracellular water lifetime), which is a potential marker of metabolic activity. In Specific Aim 1, applied to a phase I/II clinical trial of soft tissue sarcoma and standard of care neoadjuvant chemotherapy treatment of breast cancer, SSM DCE-MRI is compared with SM DCE-MRI, diffusion-weighted MRI, and tumor size measurement for assessment of therapy response. In Aim 2, the effects of data acquisition and processing schemes, including temporal resolution, measurement of native tissue T1, quantification of arterial input function, and DCE-MRI acquisition duration, on DCE-MRI biomarker values are investigated within the context of therapeutic monitoring. In Aims 1 and 2, pathologic response results are used as endpoints for correlation with imaging results and statistical analyses. In Aim 3, caBIG-compliant software tools are developed that utilize a single and/or a set of imaging biomarkers to aid clinical research.

Reference List

  1. Thibault G, Tudorica A, Afzal A, Chui SYC, Naik A, Troxell ML, Kemmer KA, Oh KY, Roy N, Jafarian N, Holtorf ML, Huang W, Song X. DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. Tomography 2017;3:23-32. PMID: 28691102
  2. Huang W, Beckett BR, Tudorica A, Meyer JM, Afzal A, Chen Y, Mansoor A, Hayden JB, Doung YC, Hung AY, Holtorf ML, Aston TJ, Ryan CW. Evaluation of soft tissue sarcoma response to preoperative chemoradiotherapy using dynamic contrast-enhanced magnetic resonance imaging. Tomography 2016;2:308-316. PMID: 28066805
  3. Huang W, Chen Y, Fedorov A, Li X, Jajamovich GH, Malyarenko DI, Aryal MP, LaViolette PS, Oborski MJ, O’Sullivan F, Abramson RG, Jafari-Khouzani K, Afzal A, Tudorica A, Moloney B, Gupta SN, Besa C, Kalpathy-Cramer J, Mountz JM, Laymon CM, Muzi M, Kinahan PE, Schmainda K, Cao Y, Chenevert TL, Taouli B, Yankeelov TE, Fennessy FMM, Li X. The impact of arterial input function determination variations on prostate dynamic contrast-enhanced magnetic resonance imaging pharmacokinetic modeling: a multicenter data analysis challenge. Tomography 2016;2:56-66. PMID: 27200418
  4. Tudorica A, Oh KY, Chui SYC, Roy N, Troxell ML, Naik A, Kemmer K, Chen Y, Holtorf ML, Afzal A, Springer CS, Li X, Huang W. Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI. Transl Oncol 2016;9:8-17. PMID: 26947876.
  5. Jajamovich GH, Huang W, Besa C, Li X, Afzal A, Dyvorne HA, Taouli B. DCE-MRI of hepatocellular carcinoma: perfusion quantification with Tofts model versus shutter-speed model — initial experience. Magn Reson Mater Phy Biol Med 2016;29:49-58. PMID: 26646522.
  6. Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Fedorov A, Tudorica A, Gupta SN, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Barbodiak DP, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpathy-Cramer J. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Transl Oncol 2014;7:153-166. PMID: 24772219
  7. Springer CS, Li X, Tudorica LA, Oh KY, Roy N, Chui SYC, Naik AM, Holtorf ML, Afzal A, Rooney WD, Huang W. Intratumor mapping of intracellular water lifetime: metabolic images of breast cancer? NMR Biomed 2014;27:760-773. PMID: 24798066