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

University of Michigan (team #3)

Quantitative MR models of HN Cancers for Physiological Adaption of RT

Yue Cao, Ph.D. and Avraham Eisbruch, MD
yuecao@med.umich.edu; eisbruch@umich.edu
Grant Number: U01CA183848

Current state-of-art therapy for high-risk, advanced head-and-neck cancers (HNC) (e.g., HPV-, smoker), concurrent radiation therapy with chemotherapy and followed by adjuvant chemotherapy, still leads to 30-50% of local and regional failure. Physiological imaging based adaptive radiation boosting of the resistant sub-volumes of the tumor has the potential to improve outcomes. However, clinical utilization of metabolic and physiological imaging is challenging due to issues such as reproducibility of physiological images, tumor heterogeneity, and lack of tools to support therapy adaptation.

This project aims to develop and investigate quantitative image tools using pattern recognition and machine learning techniques to identify the sub-volumes of HNC with low blood volume (LBV) derived from DCE MRI and low ADC quantified from diffusion-weighted MRI. Currently, these tools are used to support a randomized phase II clinical trial for boosting the potential “risk for failure” sub-volumes of the tumor in the advanced HNC. This trial involves two sites, University of Michigan Hospital and VA hospital at Ann Arbor. This phase II clinical trial allows the team to test feasibility of using their QI tools in the clinical environment. Also, the clinical trial allows the team to further identify issues and barriers that need to be overcome before deploying them in a multi-center clinical trial.

Several specific scientific investigations have been carried out:

  1. Individual patient QA of parametric maps derived from DCE image series [1]
  2. Standardization and automation of delineation of low BV sub-volumes in HNC for DCE image series evaluation of the data acquired from different scanners [2-3]
  3. Reduction and evaluation of susceptibility effects on diffusion weighted images in HN and deployment of a diffusion sequence having less sensitive to susceptibility effects [4]
  4. Optimization of the Head and Neck MRI Protocol for the RT workflow
  5. Investigation of spatial correspondence of physiological imaging parameters in HNC, e.g., blood volume, ADC and FDG uptake [5-6]

QIN Challenge and Collaborative Projects

Michigan team #3 has participated in several QIN challenges: 1) arterial input function for DCE analysis led by Dr. Wei Huang, 2) diffusion quantification challenge led by Dr. David Newitt; 3) T1 measurement challenge led by Dr. Octavia Bane, and 4) DSC challenge led by Dr. Kathleen Schmainda. The first three projects have led to printed, accepted or submitted papers [7-9]. The manuscript for the 4th project is in preparation.

Development and Evaluation of Other QI Tools

The team has developed other QI tools that aim for broader clinical applications, e.g., hyper-cellularity volume delineation from high b-value diffusion weighted imaging for GBM [10], and T1 repeatability test in patients with brain tumors [11].

  1. Y. Cao, “DCE-Perfusion and Diffusion-Weighted MR Imaging for Clinical Decision Support in Head and Neck cancer”, on Panel 3 of Advanced Quantitative Imaging for the Radiation Oncologist: Response Assessment and Targeting for Clinical Trials and Practice, A View from the NCI’s Quantitative Imaging Network in the 58th Annual meeting of ASTRO, Sept 25-28, 2016, Boston, MA
  2. D. You, M. Aryal, S. Samuels, A. Eisbruch, and Y. Cao. Wavelet-Based Temporal Feature Extraction from DCE-MRI to Identify Sub-Volumes of Low Blood Volume in Head-And-Neck Cancer. Med Phys. SU-E-J-241. 2015.
  3. Daekeun You, Madhava Aryal, Stuart Samuels, Avraham Eisbruch, and Yue Cao. Wavelet-based Temporal Feature Extraction from DCE-MRI to Identify Significant Subvolumes of Tumors Related to Outcomes of Radiation Therapy in Head-and-Neck Cancer. Tomography, Vol 2(4): 341-352, NCI QIN issue, 2016.
  4. Madhava P Aryal, Timothy Ritter, Michelle Mierzwa, Avraham Eisbruch, and Yue Cao Readout Segmented EPI in Diffusion Weighted Imaging of Head and Neck Cancer: Comparison with Single-shot EPI. Abstract, The 4th MRI in RT workshop, June 18-19, 2016, Ann Arbor, MI
  5. J.Y. Lee, S. Samuels, M.P. Aryal, C. Lee, Y. Cao, A. Eisbruch. Characterizing Regions of Hypoperfusion and Restricted Diffusion in Head and Neck Cancer Patients Enrolled on a Prospective Phase 2 Randomized Trial. Int J Rad Onc Biol Phys, 96(2S), S71, 2016.
  6. Feifei Teng, Jae Lee, Choonik Lee, Madhava Aryal, Peter Hawkins, Michelle Mierzwa, Avraham Eisbruch, Yue Cao. Adaptive boost target definition in high-risk head and neck cancer based on multi-imaging risk biomarkers. Int J Rad Onc Biol Phys (in review) 2017.
  7. W. Huang, Y. Chen, A. Fedorov, X. Li, G. H. Jajamovich, D. I. Malyarenko, M. P. Aryal, P. S. LaViolette, M. J. Oborski, F.O’Sullivan, R. G. Abramson, K. Jafari-Khouzani, A. Afzal, A.Tudorica, B. Moloney, S. N. Gupta, C. Besa, J. Kalpathy-Cramer, J. M. Mountz, C. M. Laymon, M. Muzi, K. Schmainda, Y. Cao, T. L. Chenevert, B. Taouli, T. E. Yankeelov, F. Fennessy, and X. Li. The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography, Vol 2(1):56-66 (2016). DOI: 10.18383/j.tom.2015.00184.
  8. O. Bane, S. Hectors, M. Wagner, L. R. Arlinghaus, M. Aryal, Y. Cao, T. L. Chenevert, F. Fennessy, W. Huang, N. Hylton, J. Kalpathy-Cramer, K. E. Keenan, D. Malyarenko, R. Mulkern, D. Newitt, S. E. Russek, K. F. Stupic, A. Tudorica, L. Wilmes, T. Yankeelow, Y. Yen, M. Boss and B. Taouli. Accuracy, Repeatability and Interplatform reproducibility of T1 quantification methods used for DCE-MRI: results from a multicenter phantom study. Magnetic Resonance in Medicine, 2017 (minor revision).
  9. D. C. Newitt, D. Malyarenko, T. L. Chenevert, C. C. Quarles, L. Bell, A. Fedorov, F. Fennessy, M. A. Jacobs, M. Solaiyappan, S. Hectors, B. Taouli, M. Muzi, P. Kinahan, K. M. Schmainda, M. A. Prah, E. N. Taber, C. Kroenke, W. Huang, L. R. Arlinghaus, T. E. Yankeelov, Y. Cao, M. Aryal, Y.-F.Yen, J. Kalpathy-Cramer, A. Shukla-Dave, M. Fung, J. Liang, M. Boss, N. Hylton. Multi-site concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. Submitted to JMI. (2017).
  10. P. P. Pramanik, H. A. Parmar, A. G. Mammoser, L. R. Junck, M. M. Kim, C. I. Tsien, T. S. Lawrence, and Y. Cao. Hypercellularity Components of Glioblastoma Identified by High b-value Diffusion-Weighted Imaging. Int J Rad Onbc Biol Phys, 92(4):811-819, 2015. PMID: 26104935. PMCID: PMC4507284
  11. Madhava P Aryal, Thomas L Chenevert, Yue Cao. Impact of Uncertainty in Longitudinal T1 Measurements on Quantification of Dynamic Contrast Enhanced MRI. NMR in Biomedicine, 29(4):411-9, 2016.

Laboratory weblink:

https://medicine.umich.edu/dept/radonc/research/research-labs/physics-laboratories/yue-cao-laboratory

Yue Cao, Ph.D.