The overall objective of this program project is to improve high dose conformal radiation treatments for cancer by individualizing therapy beyond simple anatomy. The previous grant studied plan optimization, conformal dose distributions, patient related geometrical uncertainties and motion, and dose/volume tolerance boundaries for treatment limiting normal tissues while performing tumor dose escalation without increasing complications. With increasing knowledge of motion and setup uncertainty throughout treatment, and new imaging techniques leading to early predictors of response and toxicity, we now have the opportunity to optimize and individualize each patient's treatment by incorporating events throughout the treatment course. Thus, this program project will work to individualize and optimize the entire planning/delivery process for the patient. tThe first project will develop and evaluate comprehensive adaptive optimization methods, enabling optimization of plans for the entire treatment course incorporating information and events whenever they become available. The second project will study individualization of geometric and dosimetric patient models throughout the treatment course, moving from population-based geometric modeling to individualized models for adaptive treatment delivery. The third project will continue clinical studies of brain and head/neck tumors treated with dose-sculpting techniques, using chemoradiation and improved imaging studying early predictors of response. The fourth project will continue liver and lung studies using dose escalation, chemoradiation, and imaging for predictors of response. The projects are supported by Core - administrative and statistical support, Core - adaptive therapy, Core - quantitative image use, and Core - computer software support. The investigators possess a unique combination of skills and experience which will lead to successful performance of this work, with 18 years experience in clinical 3-D treatment planning, conformal therapy, and clinical studies of dose escalation and normal tissue complications.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA059827-15
Application #
7882430
Study Section
Subcommittee G - Education (NCI)
Program Officer
Deye, James
Project Start
1997-02-01
Project End
2012-06-30
Budget Start
2010-07-01
Budget End
2012-06-30
Support Year
15
Fiscal Year
2010
Total Cost
$2,628,104
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
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
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

Showing the most recent 10 out of 289 publications