Abstract

The clinical physics core will provide all dosimetry, treatment planning, specialized patient treatment support and quality assurance for the research projects. It will support Project 2 through beam measurement and data acquisition through the use of water phantoms, film dosimetry, anthropomorphic phantoms and diode dosimetry. Support for projects 3 and 4 will include diode and TLD dosimetry in patients, and design and fabrication of patient set-up and treatment aid devices. It will support Project 1 by testing the software developed there for use in our treatment planning system and establishing the methodology for its effective use. Diagnostic imaging from CT, MR, PET and Spect scanning will be integrated into accurate target volume and normal tissue descriptions to aid in the work of Projects 1, 2, 3, 4. The mechanics and entry of information associated with image correlation will also be performed in this core to aid in the efforts of all four projects. As the research plans in each project progress and dosimetry techniques or treatment planning tools are developed, the personnel in this core will test and routinely use them. Quality assurance programs for all four projects will be carried out by the members of this core.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA059827-04
Application #
5209300
Study Section
Project Start
Project End
Budget Start
Budget End
Support Year
4
Fiscal Year
1996
Total Cost
Indirect Cost

  Publications

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
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

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