The long-term goal of this proposal is to increase intrahepatic and lung tumor dose and intrahepatic tumor? resectability by optimizing dose distributions within the tumor and individualizing assessment of normal? tissue toxicity though innovative normal tissue imaging. This goal will be carried out through two specific? aims.
Aim 1 is to optimize the use of radiation therapy in the treatment of intrahepatic cancer. We will? escalate high dose non-uniform radiation with the goal of increasing both local control and resectability.? During treatment we will measure global changes in hepatic function using indocyanine green extraction,? and regional changes in blood flow using dynamic contrast-enhanced CT scanning to assess early liver injury.? These data will permit us to develop a model in which early changes in liver function can be correlated with? liver toxicity after treatment. We also propose to use this model to develop a new protocol in which we reoptimize? planning during the course of treatment to minimize normal liver injury.
Aim 2 is to optimize the? use of radiation therapy in the treatment of Stage III unresectable lung cancer. We will assess the optimal? timing of chemotherapy and escalated radiation in a randomized phase II trial. During treatment we will? measure changes in FDG-PET, to assess early tumor response, and full ventilation-perfusion SPECT? scanning and pulmonary function tests to assess early lung injury. These data will permit us to develop a? model in which early changes in FDG-PET imaging and lung function can be correlated with tumor response? and lung toxicity, respectively, after treatment. We also propose to use this model to develop a new protocol? in which we re-optimize planning during the course of treatment to maximize tumor control and to minimize? normal tissue injury. We anticipate that these studies will increase the local control and resectability of? intrahepatic cancers and the local control of lung tumors while individualizing and, thus, minimizing the risk? of normal tissue injury.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA059827-13
Application #
7658879
Study Section
Subcommittee G - Education (NCI)
Project Start
Project End
Budget Start
2008-07-01
Budget End
2009-06-30
Support Year
13
Fiscal Year
2008
Total Cost
$395,478
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Wang, Weili; Huang, Lei; Jin, Jian-Yue et al. (2018) IDO Immune Status after Chemoradiation May Predict Survival in Lung Cancer Patients. Cancer Res 78:809-816
Suresh, Krithika; Owen, Dawn; Bazzi, Latifa et al. (2018) Using Indocyanine Green Extraction to Predict Liver Function After Stereotactic Body Radiation Therapy for Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 100:131-137
Feng, Mary; Suresh, Krithika; Schipper, Matthew J et al. (2018) Individualized Adaptive Stereotactic Body Radiotherapy for Liver Tumors in Patients at High Risk for Liver Damage: A Phase 2 Clinical Trial. JAMA Oncol 4:40-47
Owen, Daniel Rocky; Boonstra, Phillip S; Viglianti, Benjamin L et al. (2018) Modeling Patient-Specific Dose-Function Response for Enhanced Characterization of Personalized Functional Damage. Int J Radiat Oncol Biol Phys 102:1265-1275
Deist, Timo M; Dankers, Frank J W M; Valdes, Gilmer et al. (2018) Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers. Med Phys 45:3449-3459
Johansson, Adam; Balter, James; Cao, Yue (2018) Rigid-body motion correction of the liver in image reconstruction for golden-angle stack-of-stars DCE MRI. Magn Reson Med 79:1345-1353
Johansson, Adam; Balter, James M; Cao, Yue (2018) Abdominal DCE-MRI reconstruction with deformable motion correction for liver perfusion quantification. Med Phys 45:4529-4540
Tseng, Huan-Hsin; Luo, Yi; Ten Haken, Randall K et al. (2018) The Role of Machine Learning in Knowledge-Based Response-Adapted Radiotherapy. Front Oncol 8:266
Jochems, Arthur; El-Naqa, Issam; Kessler, Marc et al. (2018) A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol 57:226-230
Rosen, Benjamin S; Hawkins, Peter G; Polan, Daniel F et al. (2018) Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1319-1329

Showing the most recent 10 out of 289 publications