Abstract

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.

  Funding Agency

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

  Institution

Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109

  Publications

Hawkins, Peter G; Boonstra, Philip S; Hobson, Stephen T et al. (2018) Prediction of Radiation Esophagitis in Non-Small Cell Lung Cancer Using Clinical Factors, Dosimetric Parameters, and Pretreatment Cytokine Levels. Transl Oncol 11:102-108
Sun, Yilun; Hawkins, Peter G; Bi, Nan et al. (2018) Serum MicroRNA Signature Predicts Response to High-Dose Radiation Therapy in Locally Advanced Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 100:107-114
Kong, Feng-Ming Spring; Li, Ling; Wang, Weili et al. (2018) Greater reduction in mid-treatment FDG-PET volume may be associated with worse survival in non-small cell lung cancer. Radiother Oncol :
Shilkrut, Mark; Sapir, Eli; Hanasoge, Sheela et al. (2018) Phase I Trial of Dose-escalated Whole Liver Irradiation With Hepatic Arterial Fluorodeoxyuridine/Leucovorin and Streptozotocin Followed by Fluorodeoxyuridine/Leucovorin and Chemoembolization for Patients With Neuroendocrine Hepatic Metastases. Am J Clin Oncol 41:326-331
Jackson, William C; Tao, Yebin; Mendiratta-Lala, Mishal et al. (2018) Comparison of Stereotactic Body Radiation Therapy and Radiofrequency Ablation in the Treatment of Intrahepatic Metastases. Int J Radiat Oncol Biol Phys 100:950-958
Miften, Moyed; Vinogradskiy, Yevgeniy; Moiseenko, Vitali et al. (2018) Radiation Dose-Volume Effects for Liver SBRT. Int J Radiat Oncol Biol Phys :
Konerman, Matthew C; Lazarus, John J; Weinberg, Richard L et al. (2018) Reduced Myocardial Flow Reserve by Positron Emission Tomography Predicts Cardiovascular Events After Cardiac Transplantation. Circ Heart Fail 11:e004473
Tseng, Huan-Hsin; Wei, Lise; Cui, Sunan et al. (2018) Machine Learning and Imaging Informatics in Oncology. Oncology :1-19
El Naqa, Issam; Ruan, Dan; Valdes, Gilmer et al. (2018) Machine learning and modeling: Data, validation, communication challenges. Med Phys 45:e834-e840
El Naqa, Issam; Johansson, Adam; Owen, Dawn et al. (2018) Modeling of Normal Tissue Complications Using Imaging and Biomarkers After Radiation Therapy for Hepatocellular Carcinoma. Int J Radiat Oncol Biol Phys 100:335-343

Showing the most recent 10 out of 289 publications