Lung image database consortium: developing a resource for the medical imaging research community.

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.

[1]  W. J. Tuddenham,et al.  Glossary of terms for thoracic radiology: recommendations of the Nomenclature Committee of the Fleischner Society. , 1984, AJR. American journal of roentgenology.

[2]  D. Cavouras,et al.  Image analysis methods for solitary pulmonary nodule characterization by computed tomography. , 1992, European journal of radiology.

[3]  M. Giger,et al.  Computerized Detection of Pulmonary Nodules in Computed Tomography Images , 1994, Investigative radiology.

[4]  J. Austin,et al.  Glossary of terms for CT of the lungs: recommendations of the Nomenclature Committee of the Fleischner Society. , 1996, Radiology.

[5]  J. Austin,et al.  Primary carcinoma of the lung overlooked at CT: analysis of findings in 14 patients. , 1996, Radiology.

[6]  J. Gurney,et al.  Missed lung cancer at CT: imaging findings in nine patients. , 1996, Radiology.

[7]  H. Ohmatsu,et al.  Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. , 1996, Radiology.

[8]  C E Floyd,et al.  The Effect of Data Sampling on the Performance Evaluation of Artificial Neural Networks in Medical Diagnosis , 1997, Medical decision making : an international journal of the Society for Medical Decision Making.

[9]  Gerald Q. Maguire,et al.  Comparison and evaluation of retrospective intermodality brain image registration techniques. , 1997, Journal of computer assisted tomography.

[10]  Noboru Niki,et al.  Pulmonary organs analysis for differential diagnosis based on thoracic thin-section CT images , 1997 .

[11]  Noboru Niki,et al.  Quantitative surface characterization of pulmonary nodules based on thin-section CT images , 1997 .

[12]  C. Henschke,et al.  Neural networks for the analysis of small pulmonary nodules. , 1997, Clinical imaging.

[13]  Feng Li,et al.  Mass screening for lung cancer with mobile spiral computed tomography scanner , 1998, The Lancet.

[14]  Shinji Yamamoto,et al.  Image processing for computer-aided diagnosis of lung cancer screening system by CT (LSCT) , 1998, Medical Imaging.

[15]  Robert M. Nishikawa,et al.  Variations in measured performance of CAD schemes due to database composition and scoring protocol , 1998, Medical Imaging.

[16]  Kenneth R. Hoffmann,et al.  Automatic detection of pulmonary nodules in low-dose screening thoracic CT examinations , 1999, Medical Imaging.

[17]  H. Ohmatsu,et al.  Detection failures in spiral CT screening for lung cancer: analysis of CT findings. , 1999, Radiology.

[18]  M. McNitt-Gray,et al.  The effects of co-occurrence matrix based texture parameters on the classification of solitary pulmonary nodules imaged on computed tomography. , 1999, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[19]  M. McNitt-Gray,et al.  A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. , 1999, Medical physics.

[20]  Noboru Niki,et al.  Lung cancer detection based on helical CT images using curved-surface morphology analysis , 1999, Medical Imaging.

[21]  Kang-Ping Lin,et al.  Object-based deformation technique for 3D CT lung nodule detection , 1999, Medical Imaging.

[22]  O. Miettinen,et al.  Early Lung Cancer Action Project: overall design and findings from baseline screening , 1999, The Lancet.

[23]  Rajiv Gupta,et al.  Small pulmonary nodules: evaluation with repeat CT--preliminary experience. , 1999, Radiology.

[24]  Noboru Niki,et al.  Quantitative analysis of internal texture for classification of pulmonary nodules in three-dimensional thoracic images , 2000, Medical Imaging: Image Processing.

[25]  G. Rubin,et al.  Data explosion: the challenge of multidetector-row CT. , 2000, European journal of radiology.

[26]  Li Fan,et al.  Automatic detection of lung nodules from multislice low-dose CT images , 2001, SPIE Medical Imaging.

[27]  M. McNitt-Gray,et al.  Patient-specific models for lung nodule detection and surveillance in CT images , 2001, IEEE Transactions on Medical Imaging.

[28]  Margrit Betke,et al.  Chest CT: automated nodule detection and assessment of change over time--preliminary experience. , 2001, Radiology.

[29]  Noboru Niki,et al.  Computer-aided differential diagnosis of pulmonary nodules based on a hybrid classification approach , 2001, SPIE Medical Imaging.

[30]  L. Clarke,et al.  National Cancer Institute initiative: Lung image database resource for imaging research. , 2001, Academic radiology.

[31]  Dag Wormanns,et al.  Improvement of method for computer-assisted detection of pulmonary nodules in CT of the chest , 2001, SPIE Medical Imaging.

[32]  S. Armato,et al.  Automated detection of lung nodules in CT scans: preliminary results. , 2001, Medical physics.

[33]  Max A. Viergever,et al.  Computer-aided diagnosis in chest radiography: a survey , 2001, IEEE Transactions on Medical Imaging.

[34]  S. Saini,et al.  Effect of multislice CT technology on scanner productivity. , 2001, AJR. American journal of roentgenology.

[35]  Noboru Niki,et al.  Computerized characterization of contrast enhancement patterns for classifying pulmonary nodules , 2001, SPIE Medical Imaging.

[36]  Hiroshi Fujita,et al.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique , 2001, IEEE Transactions on Medical Imaging.

[37]  S. Armato,et al.  Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. , 2002, Radiology.

[38]  Noriyuki Moriyama,et al.  Gadolinium-enhanced MR imaging of the liver: optimizing imaging delay for hepatic arterial and portal venous phases--a prospective randomized study in patients with chronic liver damage. , 2002, Radiology.

[39]  Berkman Sahiner,et al.  Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. , 2002, Medical physics.

[40]  L. Clarke,et al.  Cancer imaging informatics workshop report from the Biomedical Imaging Program of the National Cancer Institute. , 2003, Academic radiology.

[41]  B. Hillman,et al.  Economic, legal, and ethical rationales for the ACRIN national lung screening trial of CT screening for lung cancer. , 2003, Academic radiology.

[42]  Arunabha S. Roy,et al.  Automated lung nodule classification following automated nodule detection on CT: a serial approach. , 2003, Medical physics.

[43]  R. F. Wagner,et al.  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. , 2004, Academic radiology.