The overall objective of Project 3 is to quantify, and through improvements, maximize the precision with which target volumes within patients can be localized at the time of treatment. A fundamental goal of conformal radiation therapy is to confine regions of high dose as tightly as possible to target volumes. currently, immobile biologic target volumes need to be expanded to form planning target volumes to account for uncertainties in patient setup between treatments and for organ motion within patients. However, to escalate dose safely beyond current standards, it is necessary to minimize the volume of tissue irradiated. In this project it is hypothesized that through use of improved patient immobilization, real-time diagnostic and megavoltage flat-panel imagers, software to compute corrections for repositioning patients, and a computer controlled treatment machine, it will be possible to accurately and rapidly setup a patient before and/or during each treatment session. It is further hypothesized that, through use of a combination of radio-opaque markers visible on diagnostic and megavoltage studies, the influence of organ motion on target volume localization can be accounted for on a quantitative basis. Thus, Project 3 has two specific aims, which will be addressed through completion of three major subtasks associated with each aim.
Aim 1 : To develop and implement procedures that enable the reduction of """"""""patient"""""""" setup inaccuracies to a level consistent with the precision with which patient position can be measured; with subtasks related to 1a) patient immobilization utilizing improved devices and techniques, 1b) patient localization by means of diagnostic x-ray sources located at the treatment unit in conjunction with innovative composite diagnostic- megavoltage, online flat-panel imagers, and 1c) computer-assisted patient repositioning using a redesigned computer-controlled table top system.
Aim 2 : To account for """"""""target"""""""" motion due to patient organ motion in an otherwise immobilized patient; through subtasks which 2a) determine the stability of inter-treatment patient structure and organ localization, 2b) directly quantify target volume motion in the prostate, liver and lung using implanted radio-opaque markers, and 2c) employ advanced techniques to compensate for organ motion on a daily basis through adjustment of treated volumes or use of physiological gating techniques.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA059827-04
Application #
5209297
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
4
Fiscal Year
1996
Total Cost
Indirect Cost
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
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

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