Skip to main content

Advertisement

SpringerLink
Log in

Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings

  • Chest
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

The purpose of this study was to evaluate the performance of a computer-assisted diagnostic (CAD) tool using various reconstruction slice thicknesses (RST). Image data of 20 patients undergoing multislice CT for pulmonary metastasis were reconstructed at 4.0, 2.0 and 0.75 mm RST and assessed by two blinded radiologists (R1 and R2) and CAD. Data were compared against an independent reference standard. Nodule subgroups (diameter >10, 4–10, <4 mm) were assessed separately. Statistical methods were the ROC analysis and Mann-Whitney U test. CAD was outperformed by readers at 4.0 mm (Az = 0.18, 0.62 and 0.69 for CAD, R1 and R2, respectively; P<0.05), comparable at 2.0 mm (Az = 0.57, 0.70 and 0.69 for CAD, R1 and R2, respectively), and superior using 0.75 mm RST (Az = 0.80, 0.70 and 0.70 and sensitivity = 0.74, 0.53 and 0.53 for CAD, R1 and R2, respectively; P<0.05). Reader performances were significantly enhanced by CAD (Az = 0.93 and 0.95 for R1 + CAD and R2 + CAD, respectively, P<0.05). The CAD advantage was best for nodules <10 mm (detection rates = 93.3, 89.9, 47.9 and 47.9% for R1 + CAD, R2 + CAD, R1 and R2, respectively). CAD using 0.75 mm RST outperformed radiologists in nodules below 10 mm in diameter and should be used to replace a second radiologist. CAD is not recommended for 4.0 mm RST.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Ries L (1999) SEER Cancer statistics review, 1973–1996. National Cancer Institution, Bethesda

    Google Scholar 

  2. Henschke CI, McCauley DI, Yankelevitz DF, Naidich DP, McGuinness G, Miettinen OS, Libby D, Pasmantier M, Koizumi J, Altorki N, Smith JP (2001) Early lung cancer action project: a summary of the findings on baseline screening. Oncologist 6:147–152

    CAS  PubMed  Google Scholar 

  3. Kaneko M, Eguchi K, Ohmatsu H, Kakinuma R, Naruke T, Suemasu K, Moriyama N (1996) Peripheral lung cancer: screening and detection with low-dose spiral-CT versus radiography. Radiology 201:798–802

    CAS  PubMed  Google Scholar 

  4. Sone S, Li F, Yang ZG, Takashima S, Maruyama Y, Hasegawa M, Wang JC, Kawakami S, Honda T (2000) Characteristics of small lung cancers invisible on conventional chest radiography and detected by population based screening using spiral CT. Br J Radiol 73:137–145

    CAS  PubMed  Google Scholar 

  5. Yankelevitz DF, Gupta R, Zhao B, Henschke CI (1999) Small pulmonary nodules: evaluation with repeat CT—preliminary experience. Radiology 212:561–566

    CAS  PubMed  Google Scholar 

  6. Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI (2000) Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 217:251–256

    CAS  PubMed  Google Scholar 

  7. Ko JP, Betke M (2001) Chest CT: automated nodule detection and assessment of change over time—preliminary experience. Radiology 218:267–273

    CAS  PubMed  Google Scholar 

  8. Zhao B, Gamsu G, Ginsber MS, Jiang L, Schwartz LH (2003) Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. J Appl Clin Med Phys 4:248–260

    Article  PubMed  Google Scholar 

  9. Naidich DP, Rusinek H, McGuinness G, Leitman B, McCauley DI, Henschke CI (1993) Variables affecting pulmonary nodule detection with computed tomography: evaluation with three-dimensional computer simulation. J Thorac Imaging 8:291–299

    CAS  PubMed  Google Scholar 

  10. Henschke CI, Yankelevitz DF, Libby D, Kimmel M (2002) CT screening for lung cancer: the first 10 years. Cancer J 8:S47–S54

    PubMed  Google Scholar 

  11. Swensen SJ, Jett JR, Sloan JA, Midthun DE, Hartman TE, Sykes AM, Aughenbaugh GL, Zink FE, Hillman SL, Noetzel GR, Marks RS, Clayton AC, Pairolero PC (2002) Screening for lung cancer with low-dose spiral computed tomography. Am J Respir Crit Care Med 165:508–513

    PubMed  Google Scholar 

  12. Diederich S, Wormanns D, Semik M, Thomas M, Lenzen H, Roos N, Heindel W (2002) Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers. Radiology 222:773–781

    PubMed  Google Scholar 

  13. Swensen SJ, Jett JR, Hartman TE, Midthun DE, Sloan JA, Sykes AM, Aughenbaugh GL, Clemens MA (2003) Lung cancer screening with CT: Mayo Clinic experience. Radiology 226:756–761

    PubMed  Google Scholar 

  14. Friese SA, Rieber A, Fleiter T, Brambs HJ, Claussen CD (1994) Pulmonary nodules in spiral volumetric and single slice computed tomography. Eur J Radiol 18:48–51

    CAS  PubMed  Google Scholar 

  15. Fan L, Novak CL, Naidich DP, Wei G, Quian J, Ko JP (2002) Improving optimal radiologic interpretation of low-dose multi-slice lung CT studies using ICAD. Radiology 225:475 (RSNA abstract)

    Google Scholar 

  16. Press WH, Flamery BP, Teukolsky SA, Vetterling WTne (1992) Numerical recipes, the art of scientific computing. Cambridge University Press, Cambridge

    Google Scholar 

  17. Hanley JA, McNeil BJ (1982) The meaning and use of area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    CAS  PubMed  Google Scholar 

  18. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating curves: a nonparametric approach. Biometrics 44:837–845

    CAS  PubMed  Google Scholar 

  19. Markus H, Bland JM, Rose G, Sitzer M, Siebler M (1996) How good is intercenter agreement in the identification of embolic signals in carotid artery disease? Stroke 27:1249–1252

    CAS  PubMed  Google Scholar 

  20. Fiebich M, Wietholt C, Renger BC, Armato S III, Hoffmann K, Wormanns D, Diederich S (1999) Automatic detection of pulmonary nodules in low-dose screening thoracic CT examinations. Proc SPIE 3661:1434–1439

    Article  Google Scholar 

  21. van Ginneken B, ter Haar Romeny BM, Viergever MA (2001) Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging 20:1228–1241

    Article  PubMed  Google Scholar 

  22. Masutani Y, Macmahon H, Doi K (2002) Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis. IEEE Trans Med Imaging 21:1517–1523

    Article  PubMed  Google Scholar 

  23. Uppaluri R, Hoffman EA, Sonka M, Hunninghake GW, McLennan G (1999) Interstitial lung disease: a quantitative study using the adaptive multiple feature method. Am J Respir Crit Care Med 159:519–525

    CAS  PubMed  Google Scholar 

  24. Wormanns D, Kohl G, Klotz E, Marheine A, Beyer F, Heindel W, Diederich S (2004) Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 14:86–92

    Article  PubMed  Google Scholar 

  25. Giger ML, Bae KT, MacMahon H (1994) Computerized detection of pulmonary nodules in computed tomography images. Invest Radiol 29:459–465

    CAS  PubMed  Google Scholar 

  26. Armato SG III, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys 28:1552–1561

    Article  PubMed  Google Scholar 

  27. Fan L, Novak CL, Qian J, Kohl G, Naidich DP (2001) Automatic detection of lung nodules from multi-slice low-dose CT images. Proc SPIE 4322:1828–1835

    Article  Google Scholar 

  28. Satoh H, Ukai Y, Niki N, Eguchi K, Mori K, Ohmatsu H, Kakinuma R, Kaneko M, Moriyama N (1999) Computer aided diagnosis system for lung cancer based on retrospective helical CT images. Proc SPIE 3661:1324–1335

    Article  Google Scholar 

  29. Okumura T, Miwa T, Kako J, Yamamoto S, Matsumoto M, Tateno Y, Linuma T, Matsumoto T (1998) Image processing for computer-aided diagnosis of lung cancer screening system by CT (LSCT). Proc SPIE 3338:1314–1322

    Article  Google Scholar 

  30. Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Trans Med Imaging 20:595–604

    Article  CAS  PubMed  Google Scholar 

  31. Lou S, Chang C, Lin K, Chen T (1999) Object-based deformation technique for 3-D CT lung nodule detection. Proc SPIE 3661:1544–1552

    Article  Google Scholar 

  32. Brown MS, McNitt-Gray MF, Goldin JG, Suh RD, Sayre JW, Aberle DR (2001) Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Trans Med Imaging 20:1242–1250

    Article  CAS  PubMed  Google Scholar 

  33. Taguchi H, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, Eguchi K, Kaneko M, Moriyama N (1999) Lung cancer detection based on helical CT images using curved surface morphology analysis. Proc SPIE 3661:1307–1314

    Article  Google Scholar 

  34. Wormanns D, Fiebich M, Saidi M, Diederich S, Heindel W (2002) Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system. Eur Radiol 12:1052–1057

    Article  PubMed  Google Scholar 

  35. Croisille P, Souto M, Cova M, Wood S, Afework Y, Kuhlman J, Zerhouni E (1995) Pulmonary nodules: improved detection with vascular segmentation and extraction with spiral CT. Radiology 197:397–401

    CAS  PubMed  Google Scholar 

  36. Armato SG III, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19:1303–1311

    PubMed  Google Scholar 

  37. Armato SG III, Li F, Giger ML, MacMahon H, Sone S, Doi K (2002) Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program. Radiology 225:685–692

    PubMed  Google Scholar 

  38. Rubin G, Naidich D, Sherbondy A, Lyo J, Napel S (2003) Inadequacy of lung nodule reference standard based upon standard methods of expert consensus review using cine-paging of transverse thin-section MDCT lung scans. Radiology 229:441–442 (RSNA abstract)

    Google Scholar 

  39. Marten K, Seyfarth T, Auer F, Wiener E, Grillhösl A, Obenauer S, Rummeny EJ, Engelke C (2004) Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists. Eur Radiol 14:1930–1938

    Article  PubMed  Google Scholar 

  40. Herzog P, Seyfarth T, Shen H, Schoenberg S, Reiser M (2003) Performance of a computer-aided diagnosis tool for the detection of pulmonary nodules at multidetector-row CT data. Radiology 229:292 (RSNA abstract)

    Google Scholar 

  41. Miller D, Burns R, Schneider A, O’Shaughnessy K, Wood S, Castellino R (2003) A multi-reader, multi-case ROC study demonstrating improved performance detecting lung nodules using computer-aided detection (CAD) in multi-slice CT (MSCT) of the chest. Radiology 229:291–292 (RSNA abstract)

    Google Scholar 

Download references

Acknowledgments

We thank Professor Charles E. Metz from the University of Chicago, USA and Professor John Martin Bland from the University of York, UK for invaluable advice on the statistical analysis.

Author information

Authors and Affiliations

  1. Department of Radiology, Klinikum rechts der Isar, Technical University Munich, Ismaningerstrasse 22, 81675, Munich, Germany

    Katharina Marten, Andreas Grillhösl, Tobias Seyfarth, Ernst J. Rummeny & Christoph Engelke

  2. Department of Radiology, Georg-August University, Robert-Koch-Strasse 40, 37075, Göttingen, Germany

    Silvia Obenauer

Authors
  1. Katharina Marten
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andreas Grillhösl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tobias Seyfarth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Silvia Obenauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ernst J. Rummeny
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christoph Engelke
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katharina Marten.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Marten, K., Grillhösl, 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 15, 203–212 (2005). https://doi.org/10.1007/s00330-004-2544-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-004-2544-5

Keywords

Search

Navigation