Abstract

This proposal is a response to RFA CA-01-001, Lung Image Database Resource for Imaging Research. Herein we propose to participate in formulating the multi-institutional lung imaging database acquisition and quality control specification, and begin collecting cases and populating a local database according to the specification resulting from the multi-institutional consensus guidelines. The database will be served for public sharing either through direct Internet access from our lab, or via NIH centralized resources as determined later. We believe that there are multiple reasons why we should be chosen as one of the institutions to participate in this project. 1) We have been participants, at times leaders, in the fields of image processing that this database is designed to advance. These fields include computer assisted diagnosis (CAD) of cancer from mammograms and now applied to CT scans, detection of metastatic and primary cancer changes in response to chemo and radiation therapy via registration and subtraction of interval CT exams, and the use of CT side information to reduce PET?s overall system point spread function to improve quantitative analysis of lesions smaller than 1 cm. Such prior expertise will insure that database acquisition specifications contain nearly all necessary elements required for future use. 2) The clinical collaborators on this project have already had significant experience recruiting lung patients for another lung database project, the National Emphysema Treatment Trials (NETT). In this project Michigan ranked second in the number of patients screened for the study, and first in the enrollment of patients that passed the screen. 3) The design of patient research database construction methodology that safely cleans patient identifiers from the data has already been completed for a pending POl application. 4) The Department of Radiology and the University Hospitals are already committed to the acquisition of new generation CT and PET scanners within the next 2 years.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
3U01CA091099-05S1
Application #
7199595
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Croft, Barbara
Project Start
2001-08-17
Project End
2006-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
5
Fiscal Year
2006
Total Cost
$77,010
Indirect Cost

  Institution

Name
University of Michigan Ann Arbor
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109

  Publications

Armato 3rd, Samuel G; McLennan, Geoffrey; Bidaut, Luc et al. (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys 38:915-31
Armato 3rd, Samuel G; Roberts, Rachael Y; Kocherginsky, Masha et al. (2009) Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of ""truth"". Acad Radiol 16:28-38
Bland, Peyton H; Laderach, Gary E; Meyer, Charles R (2008) Implementation and use of a web-based interface for confidential communication of data between the clinical and research environments. Proc Soc Photo Opt Instrum Eng 6919:nihpa162285
Bland, Peyton H; Laderach, Gary E; Meyer, Charles R (2007) A web-based interface for communication of data between the clinical and research environments without revealing identifying information. Acad Radiol 14:757-64
McNitt-Gray, Michael F; Armato 3rd, Samuel G; Meyer, Charles R et al. (2007) The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation. Acad Radiol 14:1464-74
Armato 3rd, Samuel G; Roberts, Rachael Y; McNitt-Gray, Michael F et al. (2007) The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined ""truth"". Acad Radiol 14:1455-63
Armato 3rd, Samuel G; McNitt-Gray, Michael F; Reeves, Anthony P et al. (2007) The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans. Acad Radiol 14:1409-21
Reeves, Anthony P; Biancardi, Alberto M; Apanasovich, Tatiyana V et al. (2007) The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. Acad Radiol 14:1475-85
Meyer, Charles R; Johnson, Timothy D; McLennan, Geoffrey et al. (2006) Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol 13:1254-65
Dodd, Lori E; Wagner, Robert F; Armato 3rd, Samuel G et al. (2004) Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: contemporary research topics relevant to the lung image database consortium. Acad Radiol 11:462-75

Showing the most recent 10 out of 11 publications