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
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
Luo, Yi; McShan, Daniel L; Matuszak, Martha M et al. (2018) A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy. Med Phys :
Simeth, Josiah; Johansson, Adam; Owen, Dawn et al. (2018) Quantification of liver function by linearization of a two-compartment model of gadoxetic acid uptake using dynamic contrast-enhanced magnetic resonance imaging. NMR Biomed 31:e3913
Mendiratta-Lala, Mishal; Masch, William; Shankar, Prasad R et al. (2018) MR Imaging Evaluation of Hepatocellular Carcinoma Treated with Stereotactic Body Radiation Therapy (SBRT): Long Term Imaging Follow-Up. Int J Radiat Oncol Biol Phys :
Ohri, Nitin; Tomé, Wolfgang A; Méndez Romero, Alejandra et al. (2018) Local Control After Stereotactic Body Radiation Therapy for Liver Tumors. Int J Radiat Oncol Biol Phys :
Mendiratta-Lala, Mishal; Gu, Everett; Owen, Dawn et al. (2018) Imaging Findings Within the First 12 Months of Hepatocellular Carcinoma Treated With Stereotactic Body Radiation Therapy. Int J Radiat Oncol Biol Phys 102:1063-1069
Wang, Shulian; Campbell, Jeff; Stenmark, Matthew H et al. (2018) A model combining age, equivalent uniform dose and IL-8 may predict radiation esophagitis in patients with non-small cell lung cancer. Radiother Oncol 126:506-510

Showing the most recent 10 out of 289 publications