) Project 3 will analyze how to include patient related geometric uncertainties (due primarily to patient setup variability and organ motion) in the calculation, compilation and treatment of conformal radiotherapy dose distributions. These very important and fundamental realities associated with the therapeutic treatment of cancer patients with external beam ionizing radiation are not reflected in the standard computation of dose distributions for patient treatment plans. While knowledge of the magnitude of these uncertainties, when available, can be exploited to design geometric safety margins for the treatment of tumors, these same margins often compromise the dose that can be safely delivered to the patient's target volume(s) due to the irradiation of large volumes of normal tissue. Further, even given adequate treatment of clinical target volumes, the dose distribution actually received by the patient (especially normal tissue) is not accurately represented in the single dose calculation performed prior to treatment, using a static imaging study as the underlying anatomical model. This is of importance in the continuing studies of conformal therapy techniques, as many dose escalation and optimization schemes are based on (or constrained by) the perceived probability of expressing a treatment-related complication. This project has specific aims associated with the inclusion of patient-related setup uncertainties and organ motion in the calculation of realizable dose distributions, the compilation of delivered dose distributions that reflect individual patient and organ positions over the course of treatment, and ultimately the combination of these realizable dose calculations with patient specific, realized dose compilations in the development of dynamic refinement strategies for the optimization of individual patient treatments. Stylized, realizable dose treatment plans for individual patients should result in achieving required clinical target volume coverage, with more confident descriptions of normal tissue doses. Optimal safe treatments at a given prescription dose, or further tumor dose escalation at specified levels of normal tissue risk can then be attained. It is anticipated that the investigations will permit further optimization of treatments for individual patients.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA059827-07
Application #
6503477
Study Section
Project Start
2001-09-24
Project End
2002-07-31
Budget Start
Budget End
Support Year
7
Fiscal Year
2001
Total Cost
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
791277940
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