This application is in response to a specific request to establish a generalized CT-derived database representing ground truth in lung cancer and is not hypothesis-driven. Our broad goal is to help in the building of this database, and through that effort assist with the methodical development of appropriate lung cancer screening tools and protocols. Our group, with recognized experience in cooperative national projects, and with a broad perspective, will provide for the consortium : a well characterized group of study subjects with lung cancer, and with common lung cancer mimics such as histoplasmosis, supported by excellent radiologists and pathologists. expertise in the development of CT imaging protocols. a functional electronic transfer system for CT data sets from multiple sites, analysis and archiving of such data sets, expertise in DICOM standards, and in the issuing of web-based reports. methods for temporal matching of CT data points, important in the longitudinal follow-up of patients, and in matching excised inflated lobe data and histopathological data to the original patient CT. expertise in computational morphology, (i.e. the mathematical description of complex structures, their visualization, and their derived CT images). We intend to apply this to a subset of resected lung tumors to help define pathological and CT ground truth. Image reconstruction algorithms. This is critically important for the identification and implementation of needed improvements in CT methods to maximize the chance of detection of subtle early lesions within the lung parenchyma and airways. data from two different CT manufacturers multi-slice helical CT scanners. with mathematically derived virtual lung models, including early lung cancer development, for use in design of scanning and reconstruction methods.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA091085-02
Application #
6515042
Study Section
Special Emphasis Panel (ZCA1-SRRB-Y (J3))
Program Officer
Croft, Barbara
Project Start
2001-07-20
Project End
2006-06-30
Budget Start
2002-07-16
Budget End
2003-06-30
Support Year
2
Fiscal Year
2002
Total Cost
$272,540
Indirect Cost
Name
University of Iowa
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
041294109
City
Iowa City
State
IA
Country
United States
Zip Code
52242
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
Namati, Eman; Thiesse, Jacqueline; de Ryk, Jessica et al. (2008) Alveolar dynamics during respiration: are the pores of Kohn a pathway to recruitment? Am J Respir Cell Mol Biol 38:572-8
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
Namati, Eman; De Ryk, Jessica; Thiesse, Jacqueline et al. (2007) Large image microscope array for the compilation of multimodality whole organ image databases. Anat Rec (Hoboken) 290:1377-87
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
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

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