Academic Radiology
Volume 15, Issue 3 , Pages 326-333 , March 2008

Lung Nodule CAD Software as a Second Reader: A Multicenter Study

  • Charles S. White

      Affiliations

    • University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201
    • Corresponding Author InformationAddress correspondence to: C.W.
    • ,
  • Robert Pugatch

      Affiliations

    • University of Maryland Medical Center, 22 S. Greene Street, Baltimore, MD 21201
    • ,
  • Thomas Koonce

      Affiliations

    • Little Rock Hematology and Oncology Center, Little Rock, AR
    • ,
  • Steven W. Rust

      Affiliations

    • Battelle, Columbus, OH
    • ,
  • Ekta Dharaiya

      Affiliations

    • Philips Medical Systems, Highland Heights, OH.

Received 4 September 2007 ,Accepted 26 September 2007.

References 

  1. Rubin GD, Lyo JK, Paik DS, et al. Pulmonary nodule on multi-detector row CT scans: performance comparison of radiologists and computer-aided detection. Radiology. 2005;234:274–283
  2. Yankelwitz D, Wisnivesky JP, Henschke CI. Stage of lung cancer in relation to its size: part 1 (Insights). Chest. 2005;127:1132–1135
  3. Wisnivesky JP, Yankelwitz D, Henschke CI. Stage of lung cancer in relation to its size: part 2 (Evidence). Chest. 2005;127:1136–1139
  4. Jennings GS, Winer-Muram HT, Tarver RD, et al. Lung tumor growth: assessment with CT-comparison of diameter and cross-sectional area with volume measurements. Radiology. 2004;231:866–871
  5. Swensen SJ, Jett JR, Sloan JA, et al. Screening of lung cancer with low dose spiral computed tomography. Am J Respir Crit Care Med. 2002;20:911–920
  6. Peldschus K, Herzog P, Wood S, et al. Computer-aided diagnosis as a second reader: spectrum of findings in CT studies if chest interpreted as normal. Chest. 2005;123:1517–1523
  7. Beiden SV, Wagner RF, Campbell G. Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis. Acad Radiol. 2000;7:341–349
  8. Armato SG, Feng L, Giger ML, et al. Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology. 2002;225:685–692
  9. Awai K, Murao K, Ozawa A, et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists detection performance. Radiology. 2004;230:347–352
  10. Gurung J, Maataoui A, Khan M, et al. Automated detection of lung nodules in multidetector CT: influence of different reconstruction protocols on performance of a software prototype. Rofo. 2006;178:71–77
  11. Marten K, Grillhosl A, Seyfarth T, et al. Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings. Eur Radiol. 2005;15:203–212
  12. Kim JS, Kim JH, Cho J, et al. Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval—initial results. Radiology. 2005;236:295–299
  13. Yuan R, Vos PM, Cooperberg PL. Computer-aided detection in screening CT for pulmonary nodules. AJR Am J Roentegenol. 2006;186:1280–1287
  14. Armato SG, McLennan G, McNitt-Gray MF, et al. Lung image database consortium: developing a resource for the medical imaging research community. Radiology. 2004;232:739–748
  15. Marten K, Engelke C. Computer-aided detection and automated CT volumetry of pulmonary nodules. Eur Radiol. 2007;17:888–901

1 Supported by a grant from Philips Medical Systems, Highland Heights, OH.

PII: S1076-6332(07)00582-X

doi: 10.1016/j.acra.2007.09.027

Academic Radiology
Volume 15, Issue 3 , Pages 326-333 , March 2008

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