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
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
Jochems, Arthur; El-Naqa, Issam; Kessler, Marc et al. (2018) A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy. Acta Oncol 57:226-230
Rosen, Benjamin S; Hawkins, Peter G; Polan, Daniel F et al. (2018) Early Changes in Serial CBCT-Measured Parotid Gland Biomarkers Predict Chronic Xerostomia After Head and Neck Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1319-1329

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