Early detection and diagnosis of lung cancer are very important because they may lead to an improved prognosis for lung cancer patients. Our goal in this project is to develop and evaluate an integrated interactive computer-aided diagnostic (CAD) scheme for early detection and diagnosis of lung cancer in multi-slice computed tomography (CT), which will incorporate a detection scheme and a diagnosis scheme into a clinically oriented system. The integration of the two CAD schemes will provide radiologists with a highly practical and convenient diagnostic environment, and it will also provide technically improved reliability and compactness compared with the use of two separate CAD schemes. The interaction between radiologists and a CAD scheme enables radiologists to correct the computer's detection errors, such as to identify cancers missed by the computer, and to ignore most of the non-nodules incorrectly detected by the computer. As a result, it is expected that radiologists would become more accurate and productive in both detection and diagnosis of lung cancers by use of the integrated CAD scheme. Many innovative techniques and schemes are to be developed in this project, including (1) an accurate segmentation technique based on spiral scanning and dynamic programming; (2) an automated rule-based classifier using multiple composite features with minimized overtraining effects; (3) a nodule detection scheme using the nodule segmentation technique, the automated rule-based classifier, and a selective nodule enhancement filter; (4) a three-category nodule diagnosis scheme for distinguishing cancers from benign nodules and nonnodules; and (5) integration of the detection and diagnosis schemes, with the capability of interaction between the CAD scheme and its users. It is anticipated that the computerized detection scheme will detect 90% of nodules, with less than 4 non-nodules per CT scan, and that the diagnosis scheme will achieve an Az value (area under the receiver operating characteristic curve) of 0.90 or higher for distinction between cancers and non-cancers. Finally, an observer performance study will be conducted with 20 participating radiologists to examine the clinical usefulness of the integrated interactive CAD scheme. It is expected from the observer study that radiologists would significantly improve their detection and diagnosis accuracy for lung cancer with the aid of the interactive CAD scheme. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA113870-01A2
Application #
7145598
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Croft, Barbara
Project Start
2006-08-01
Project End
2009-07-31
Budget Start
2006-08-01
Budget End
2007-07-31
Support Year
1
Fiscal Year
2006
Total Cost
$204,155
Indirect Cost
Name
University of Chicago
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
005421136
City
Chicago
State
IL
Country
United States
Zip Code
60637
Guo, Wei; Li, Qiang (2014) Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT. Med Phys 41:091906
Qian, Xiaohua; Wang, Jiahui; Guo, Shuxu et al. (2013) An active contour model for medical image segmentation with application to brain CT image. Med Phys 40:021911
Guo, Wei; Li, Qiang; Boyce, Sarah J et al. (2012) A computerized scheme for lung nodule detection in multiprojection chest radiography. Med Phys 39:2001-12
Guo, Wei; Li, Qiang (2012) High performance lung nodule detection schemes in CT using local and global information. Med Phys 39:5157-68
Wang, Jiahui; Dobbins 3rd, James T; Li, Qiang (2012) Automated lung segmentation in digital chest tomosynthesis. Med Phys 39:732-41
Wang, Jiahui; Li, Feng; Li, Qiang (2009) Automated segmentation of lungs with severe interstitial lung disease in CT. Med Phys 36:4592-9
Wang, Jiahui; Li, Feng; Doi, Kunio et al. (2009) Computerized detection of diffuse lung disease in MDCT: the usefulness of statistical texture features. Phys Med Biol 54:6881-99
Li, Qiang; Li, Feng; Doi, Kunio (2008) Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier. Acad Radiol 15:165-75
Li, Qiang (2007) Recent progress in computer-aided diagnosis of lung nodules on thin-section CT. Comput Med Imaging Graph 31:248-57
Li, Qiang; Doi, Kunio (2007) Comparison of typical evaluation methods for computer-aided diagnostic schemes: Monte Carlo simulation study. Med Phys 34:871-6

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