Although metabolic and physiological imaging have shown prognostic and predictive value for radiation therapy (RT) outcome and may serve to support therapy modification, clinical utilization is challenging due to issues such as reproducibility of physiological images, heterogeneity of the image parameters in the tumor, and lack of tools to support therapy modification. We hypothesize that, given the underlying heterogeneity of tumors and their response to treatment, physiological imaging can be best utilized for individualizing RT by robustly and quantitatively identifying subvolumes that have a better predictive value than the imaging characteristics of the tumor as a whole, and are the likely sites of local failure. These subvolumes can serve spatial guide for radiation dose redistribution such as focal boosting to improve outcomes. We have developed and investigated fuzzy logic pattern recognition techniques for identifying these subvolumes of head-and-neck (HN) cancer from heterogeneous distributions of tumor blood volume (BV) across patients and over multiple time points. Based on our findings that large poorly-perfused subvolumes of HN tumors before treatment that persist during the early course of chemo-RT have the potential to predict local failure better than the change in the mean BV in the tumor, we will further develop the subvolume definition method, extend it to diffusion- weighted (DW) MR imaging, and evaluate and validate it in a randomized phase II clinical trial of poor- prognosis HN cancers.
Our aims are: (1) Develop quantitative and automated methods to extract significant subvolumes of HN tumors from dynamic contrast enhanced (DCE) and DW MRI for prediction of local and regional failure;(2) Prospectively assess variability and reproducibility of the subvolume intensity and definition extracted by our methods using test-retest data;and (3) Prospectively evaluate and validate that the method yields subvolumes predictive of local-regional failure in a randomized phase II trial of radiation dose boosting for poor-prognosis HN cancer. Impact: Unless clinics can acquire and analyze these images effectively and quantitatively, and use predictive results in an accurate fashion, the benefit of these powerful imaging techniques to make a meaningful difference in tumor RT may be completely obscured. This project strives to achieve these goals. The framework developed in the project can be translated into other body sites as well as other imaging modalities. By partnering with a commercial company, we will make this method available for the users in the community.

Public Health Relevance

While physiological, metabolic, and molecular imaging can help predict how an individual patient may respond differently to treatment than others with the same disease, methods to identify poorly responding parts of tumors are not yet well developed. This project investigates quantitative ways to find the regions of a tumor that need further treatment to support individualized adaptive radiation treatment.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1-SRLB-J (J2))
Program Officer
Nordstrom, Robert J
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Michigan Ann Arbor
Schools of Medicine
Ann Arbor
United States
Zip Code
You, Daekeun; Kim, Michelle M; Aryal, Madhava P et al. (2018) Tumor image signatures and habitats: a processing pipeline of multimodality metabolic and physiological images. J Med Imaging (Bellingham) 5:011009
Teng, Feifei; Aryal, Madhava; Lee, Jae et al. (2018) Adaptive Boost Target Definition in High-Risk Head and Neck Cancer Based on Multi-imaging Risk Biomarkers. Int J Radiat Oncol Biol Phys 102:969-977
Bane, Octavia; Hectors, Stefanie J; Wagner, Mathilde et al. (2018) Accuracy, repeatability, and interplatform reproducibility of T1 quantification methods used for DCE-MRI: Results from a multicenter phantom study. Magn Reson Med 79:2564-2575
Newitt, David C; Malyarenko, Dariya; Chenevert, Thomas L et al. (2018) Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network. J Med Imaging (Bellingham) 5:011003
Teng, Feifei; Tsien, Christina I; Lawrence, Theodore S et al. (2017) Blood-tumor barrier opening changes in brain metastases from pre to one-month post radiation therapy. Radiother Oncol 125:89-93
You, Daekeun; Aryal, Madhava; Samuels, Stuart E et al. (2016) Temporal Feature Extraction from DCE-MRI to Identify Poorly Perfused Subvolumes of Tumors Related to Outcomes of Radiation Therapy in Head and Neck Cancer. Tomography 2:341-352
Huang, Wei; Chen, Yiyi; Fedorov, Andriy et al. (2016) The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge. Tomography 2:56-66
Aryal, Madhava P; Chenevert, Thomas L; Cao, Yue (2016) Impact of uncertainty in longitudinal T1 measurements on quantification of dynamic contrast-enhanced MRI. NMR Biomed 29:411-9