Approximately two thirds of patients with intrahepatic cancer present with unresectable disease. Our studies show that high dose conformal radiation combined with chemotherapy appears to prolong the survival of patients with unresectable intrahepatic cancers. However, attempts to increase radiation dose still further have been limited by the development of radiation-induced liver disease (RILD). The pathology of RILD is veno-occlusive disease. In the past, efforts to develop models to estimate the likelihood of developing RILD have been based primarily on the planned radiation dose distribution for the normal liver. These analyses have demonstrated that increasing mean liver dose correlates with the likelihood of developing RILD. While these models have permitted the safe delivery of far higher doses of radiation than have previously been possible, they also suggest that there is a broad range of individual patient sensitivity that is not reflected by predictions made solely based on the physical dose distribution. If individual patient sensitivity could be better estimated before or during a course of treatment, it would permit higher doses of radiation to be delivered safely to the tumors of patients whose liver is relatively radiation resistant, thus improving survival without increasing complications. As the basic pathophysiology of RILD is venous occlusion, we develop the hypothesis that early monitoring of venous perfusion would have the potential to select patients with pre-clinical signs of perfusion changes prior to the onset of symptomatic radiation-induced injury. In response to NIH/NCI PAR-05-114, we propose to develop a perfusion model that allows us to predict anatomically distributed liver perfusion changes after the completion of radiation therapy based upon the radiation treatment plan and the values of perfusion prior to and during radiation therapy. Also, we compare the liver perfusion estimated by dynamic contrast enhanced MRI with a standard liver function index measured by Indocyanine green. Our proposed approach is highly innovative and represents a new paradigm to investigate radiation toxicity in the liver. It has potential to assess and predict individual sensitivity to radiation. If this quick trial is successful, we can transition this study into a five-years study. Eventually, we could use this perfusion and dose model to guide individual radiation therapy intervention. Our previous studies show that high dose conformal radiation combined with chemotherapy appears to prolong the survival of patients with unresectable intrahepatic cancers. However, attempts to increase radiation dose still further have been limited by radiation-induced liver injury. Our long term goal is to develop a new imaging approach for prediction of radiation-induced syptomatic liver injury. Therefore, higher dose of radiaton can be safely delivered to tumor in patients who can be better tolenrent to radiation, thereby improving survival. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA126137-01A2
Application #
7393961
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (51))
Program Officer
Tandon, Pushpa
Project Start
2008-09-29
Project End
2010-08-31
Budget Start
2008-09-29
Budget End
2009-08-31
Support Year
1
Fiscal Year
2008
Total Cost
$208,575
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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Wang, Hesheng; Cao, Yue (2012) Spatially regularized T(1) estimation from variable flip angles MRI. Med Phys 39:4139-48
Wang, Peng; Popovtzer, Aron; Eisbruch, Avraham et al. (2012) An approach to identify, from DCE MRI, significant subvolumes of tumors related to outcomes in advanced head-and-neck cancer. Med Phys 39:5277-85
Cao, Yue (2011) The promise of dynamic contrast-enhanced imaging in radiation therapy. Semin Radiat Oncol 21:147-56
Cao, Yue; Li, Diana; Shen, Zhou et al. (2010) Sensitivity of quantitative metrics derived from DCE MRI and a pharmacokinetic model to image quality and acquisition parameters. Acad Radiol 17:468-78