The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation

The LIDC is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource. A unique multi-center data collection process and communication system were developed to share image data and to capture the location and spatial extent of lung nodules as marked by expert radiologists. A two-phase data collection process was designed to allow multiple radiologists at different centers to asynchronously review and annotate each CT image series. Four radiologists reviewed each case using this process. In the first or "blinded" phase, each radiologist reviewed the CT series independently. In the second or "unblinded" review phase, the results from all four blinded reviews are compiled and presented to each radiologist for a second review. This allows each radiologist to review their own annotations along with those of the other radiologists. The results from each radiologist's unblinded review were compiled to form the final unblinded review. There is no forced consensus in this process. An XML-based message system was developed to communicate the results of each reading. This two-phase data collection process was designed, tested and implemented across the LIDC. It has been used for more than 130 CT cases that have been read and annotated by four expert readers and are publicly available at (http://ncia.nci.nih.gov). A data collection process was developed, tested and implemented that allowed multiple readers to review each case multiple times and that allowed each reader to observe the annotations of other readers.

[1]  R. Swensson,et al.  Improving diagnostic accuracy: a comparison of interactive and Delphi consultations. , 1977, Investigative radiology.

[2]  K. Hopper,et al.  Analysis of interobserver and intraobserver variability in CT tumor measurements. , 1996, AJR. American journal of roentgenology.

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

[4]  L. Schwartz,et al.  Evaluation of tumor measurements in oncology: use of film-based and electronic techniques. , 2000, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  N. Müller,et al.  Lung nodule enhancement at CT: multicenter study. , 2000, Radiology.

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

[7]  Jane P. Ko,et al.  Interobserver variations on interpretation of multislice CT lung cancer screening studies, and the implications for computer-aided diagnosis , 2002, SPIE Medical Imaging.

[8]  L. Broemeling,et al.  Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[9]  Richard C. Pais,et al.  Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography. , 2004, Academic radiology.

[10]  W. Heindel,et al.  Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT , 2004, European Radiology.

[11]  E. Hoffman,et al.  Lung image database consortium: developing a resource for the medical imaging research community. , 2004, Radiology.

[12]  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.

[13]  B. Zheng,et al.  Pulmonary nodule detection with low-dose CT of the lung: agreement among radiologists. , 2005, AJR. American journal of roentgenology.

[14]  Sumit K. Shah,et al.  Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. , 2005, Academic radiology.

[15]  Sumit K. Shah,et al.  Pulmonary nodule characterization: a comparison of conventional with quantitative and visual semi-quantitative analyses using contrast enhancement maps. , 2006, European journal of radiology.

[16]  Richard C. Pais,et al.  Evaluation of Lung MDCT Nodule Annotation Across Radiologists and Methods 1 , 2006 .

[17]  O. Miettinen,et al.  Survival of Patients with Stage I Lung Cancer Detected on CT Screening , 2008 .

[18]  L. Schwartz,et al.  Lung cancer: computerized quantification of tumor response--initial results. , 2006, Radiology.

[19]  M. L. R. D. Christenson,et al.  Inherent Variability of CT Lung Nodule Measurements In Vivo Using Semiautomated Volumetric Measurements , 2007 .

[20]  L. Tanoue CT Screening for Lung Cancer: Five-year Prospective Experience , 2007 .