Literature DB >> 18035276

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

Michael F McNitt-Gray1, Samuel G Armato, Charles R Meyer, Anthony P Reeves, Geoffrey McLennan, Richie C Pais, John Freymann, Matthew S Brown, Roger M Engelmann, Peyton H Bland, Gary E Laderach, Chris Piker, Junfeng Guo, Zaid Towfic, David P-Y Qing, David F Yankelevitz, Denise R Aberle, Edwin J R van Beek, Heber MacMahon, Ella A Kazerooni, Barbara Y Croft, Laurence P Clarke.   

Abstract

RATIONALE AND
OBJECTIVES: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers.
MATERIALS AND METHODS: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading.
RESULTS: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future.
CONCLUSIONS: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

Mesh:

Year:  2007        PMID: 18035276      PMCID: PMC2176079          DOI: 10.1016/j.acra.2007.07.021

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  18 in total

1.  Detection of pulmonary nodules at multirow-detector CT: effectiveness of double reading to improve sensitivity at standard-dose and low-dose chest CT.

Authors:  Dag Wormanns; Karl Ludwig; Florian Beyer; Walter Heindel; Stefan Diederich
Journal:  Eur Radiol       Date:  2004-11-04       Impact factor: 5.315

2.  Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features.

Authors:  Sumit K Shah; Michael F McNitt-Gray; Sarah R Rogers; Jonathan G Goldin; Robert D Suh; James W Sayre; Iva Petkovska; Hyun J Kim; Denise R Aberle
Journal:  Acad Radiol       Date:  2005-10       Impact factor: 3.173

3.  Pulmonary nodule detection with low-dose CT of the lung: agreement among radiologists.

Authors:  Joseph K Leader; Thomas E Warfel; Carl R Fuhrman; Sara K Golla; Joel L Weissfeld; Ricardo S Avila; Wesly D Turner; Bin Zheng
Journal:  AJR Am J Roentgenol       Date:  2005-10       Impact factor: 3.959

4.  Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements.

Authors:  Lawrence R Goodman; Meltem Gulsun; Lacey Washington; Paul G Nagy; Kelly L Piacsek
Journal:  AJR Am J Roentgenol       Date:  2006-04       Impact factor: 3.959

5.  Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography.

Authors:  Lien N Tran; Matthew S Brown; Jonathan G Goldin; Xiaohong Yan; Richard C Pais; Michael F McNitt-Gray; David Gjertson; Sarah R Rogers; Denise R Aberle
Journal:  Acad Radiol       Date:  2004-12       Impact factor: 3.173

6.  Improving diagnostic accuracy: a comparison of interactive and Delphi consultations.

Authors:  B J Hillman; S J Hessel; R G Swensson; P G Herman
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7.  CT screening for lung cancer: five-year prospective experience.

Authors:  Stephen J Swensen; James R Jett; Thomas E Hartman; David E Midthun; Sumithra J Mandrekar; Shauna L Hillman; Anne-Marie Sykes; Gregory L Aughenbaugh; Aaron O Bungum; Katie L Allen
Journal:  Radiology       Date:  2005-02-04       Impact factor: 11.105

8.  Analysis of interobserver and intraobserver variability in CT tumor measurements.

Authors:  K D Hopper; C J Kasales; M A Van Slyke; T A Schwartz; T R TenHave; J A Jozefiak
Journal:  AJR Am J Roentgenol       Date:  1996-10       Impact factor: 3.959

Review 9.  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.

Authors:  Lori E Dodd; Robert F Wagner; Samuel G Armato; Michael F McNitt-Gray; Sergey Beiden; Heang-Ping Chan; David Gur; Geoffrey McLennan; Charles E Metz; Nicholas Petrick; Berkman Sahiner; Jim Sayre
Journal:  Acad Radiol       Date:  2004-04       Impact factor: 3.173

10.  Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response.

Authors:  Jeremy J Erasmus; Gregory W Gladish; Lyle Broemeling; Bradley S Sabloff; Mylene T Truong; Roy S Herbst; Reginald F Munden
Journal:  J Clin Oncol       Date:  2003-07-01       Impact factor: 44.544

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  48 in total

1.  Fast lung nodule detection in chest CT images using cylindrical nodule-enhancement filter.

Authors:  Atsushi Teramoto; Hiroshi Fujita
Journal:  Int J Comput Assist Radiol Surg       Date:  2012-06-09       Impact factor: 2.924

2.  Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

Authors:  Luca Bogoni; Jane P Ko; Jeffrey Alpert; Vikram Anand; John Fantauzzi; Charles H Florin; Chi Wan Koo; Derek Mason; William Rom; Maria Shiau; Marcos Salganicoff; David P Naidich
Journal:  J Digit Imaging       Date:  2012-12       Impact factor: 4.056

3.  Computerized comprehensive data analysis of lung imaging database consortium (LIDC).

Authors:  Jun Tan; Jiantao Pu; Bin Zheng; Xingwei Wang; Joseph K Leader
Journal:  Med Phys       Date:  2010-07       Impact factor: 4.071

4.  Assessing operating characteristics of CAD algorithms in the absence of a gold standard.

Authors:  Kingshuk Roy Choudhury; David S Paik; Chin A Yi; Sandy Napel; Justus Roos; Geoffrey D Rubin
Journal:  Med Phys       Date:  2010-04       Impact factor: 4.071

5.  Mapping LIDC, RadLex™, and lung nodule image features.

Authors:  Pia Opulencia; David S Channin; Daniela S Raicu; Jacob D Furst
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

6.  Shape "break-and-repair" strategy and its application to automated medical image segmentation.

Authors:  Jiantao Pu; David S Paik; Xin Meng; Justus E Roos; Geoffrey D Rubin
Journal:  IEEE Trans Vis Comput Graph       Date:  2011-01       Impact factor: 4.579

7.  Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images.

Authors:  Ashis Kumar Dhara; Sudipta Mukhopadhyay; Pramit Saha; Mandeep Garg; Niranjan Khandelwal
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-09-04       Impact factor: 2.924

8.  Quantitative Imaging Network: Data Sharing and Competitive AlgorithmValidation Leveraging The Cancer Imaging Archive.

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Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

9.  Quantitative imaging to assess tumor response to therapy: common themes of measurement, truth data, and error sources.

Authors:  Charles R Meyer; Samuel G Armato; Charles P Fenimore; Geoffrey McLennan; Luc M Bidaut; Daniel P Barboriak; Marios A Gavrielides; Edward F Jackson; Michael F McNitt-Gray; Paul E Kinahan; Nicholas Petrick; Binsheng Zhao
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

10.  Computed tomography assessment of response to therapy: tumor volume change measurement, truth data, and error.

Authors:  Michael F McNitt-Gray; Luc M Bidaut; Samuel G Armato; Charles R Meyer; Marios A Gavrielides; Charles Fenimore; Geoffrey McLennan; Nicholas Petrick; Binsheng Zhao; Anthony P Reeves; Reinhard Beichel; Hyun-Jung Grace Kim; Lisa Kinnard
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

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