The goal of Core 0 is to provide strong and consistent support for all aspects of image analysis, processing and management for the research projects and cores. A common theme that spans all the research projects is to investigate the use of anatomic, physiological and metabolic imaging and other data to guide individualized patient treatments. Projects 1 and 2 will collect a variety of images,, including GT, anatomic and physiological MRI, FDG and 110 MET PET, and hepatic function and lung ventilation and perfusion SPECT, for brain, head and neck, liver and lung prior to, during and after treatment for individualized adpative treatment. Project 3 will develop and investigate methods to determine image-based indicators for assessment of outcomes and relate them to patterns of failure. Project 4, which aims to develop and investigate response-driven, knowledge-based decision support for adaptive plan optimization, will take the processed image data as inputs for decision making support. Although each project has a specific plan to use these images, they all share a common set of needs. Core G will provide service support for the needs of image analysis, processing and management.
Our specific aims are to 1) provide a shared infrastructure and software tools for supporting quantitative image analysis, processing, visualization and management, as well as for facilitating image data flow (input and output) between the treatment planning system, image PACS and image analysis system (FIAT);2)provide service support for image analysis, processing and modeling for all projects;and 3) provide support for image retrieval, storage and management for all projects. In-house software tools (Functional Image Analysis Tools(FIAT) and deformable image registration tools) will be further enhanced to provide support for image registration, pre-processing for noise and artifact reduction, pharmacokinetic modeling of dyanmic contrast enhanced MRI, dynamic SPECT and dynamic PET, image segmentation and data reduction, voxel-based and volumetric analysis, image statistical analysis, and image presentation and visualization. All image data will be centrally managed to facilitate efficient storage and retrieval of the data by multiple projects.

Public Health Relevance

Physiological and metabolic imaging are emerging as a critical means to provide guidence for cancer treatment and to assess therapy response. This Gore will provide services to extract important information from multiple-modality images to support physicians's decision making in the clinical studies and supporting research of this research program.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA059827-16A1
Application #
8609646
Study Section
Special Emphasis Panel (ZCA1-RPRB-C (O1))
Project Start
1997-02-01
Project End
2019-04-30
Budget Start
2014-05-15
Budget End
2015-04-30
Support Year
16
Fiscal Year
2014
Total Cost
$291,662
Indirect Cost
$102,637
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
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

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