Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Nodule Classification on Low-Dose Unenhanced CT and Standard-Dose Enhanced CT: Inter-Protocol Agreement and Analysis of Interchangeability

  • Lee, Kyung Hee (Department of Radiology, Seoul National University Bundang Hospital) ;
  • Lee, Kyung Won (Department of Radiology, Seoul National University Bundang Hospital) ;
  • Park, Ji Hoon (Department of Radiology, Seoul National University Bundang Hospital) ;
  • Han, Kyunghwa (Department of Radiology, Research Institute of Radiological Science, Yonsei University College of Medicine) ;
  • Kim, Jihang (Department of Radiology, Seoul National University Bundang Hospital) ;
  • Lee, Sang Min (Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center) ;
  • Park, Chang Min (Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine)
  • Received : 2017.08.23
  • Accepted : 2017.11.13
  • Published : 2018.06.01
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Abstract

Objective: To measure inter-protocol agreement and analyze interchangeability on nodule classification between low-dose unenhanced CT and standard-dose enhanced CT. Materials and Methods: From nodule libraries containing both low-dose unenhanced and standard-dose enhanced CT, 80 solid and 80 subsolid (40 part-solid, 40 non-solid) nodules of 135 patients were selected. Five thoracic radiologists categorized each nodule into solid, part-solid or non-solid. Inter-protocol agreement between low-dose unenhanced and standard-dose enhanced images was measured by pooling ${\kappa}$ values for classification into two (solid, subsolid) and three (solid, part-solid, non-solid) categories. Interchangeability between low-dose unenhanced and standard-dose enhanced CT for the classification into two categories was assessed using a pre-defined equivalence limit of 8 percent. Results: Inter-protocol agreement for the classification into two categories {${\kappa}$, 0.96 (95% confidence interval [CI], 0.94-0.98)} and that into three categories (${\kappa}$, 0.88 [95% CI, 0.85-0.92]) was considerably high. The probability of agreement between readers with standard-dose enhanced CT was 95.6% (95% CI, 94.5-96.6%), and that between low-dose unenhanced and standard-dose enhanced CT was 95.4% (95% CI, 94.7-96.0%). The difference between the two proportions was 0.25% (95% CI, -0.85-1.5%), wherein the upper bound CI was markedly below 8 percent. Conclusion: Inter-protocol agreement for nodule classification was considerably high. Low-dose unenhanced CT can be used interchangeably with standard-dose enhanced CT for nodule classification.

Keywords

Acknowledgement

Supported by : Ministry of Health & Welfare

References

  1. MacMahon H, Naidich DP, Goo JM, Lee KS, Leung AN, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 2017;284:228-243 https://doi.org/10.1148/radiol.2017161659
  2. Lung CT screening reporting and data system (Lung-RADS). American College of Radiology. Web site. https://www.acr. org/Clinical-Resources/Reporting-and-Data-Systems/Lung- Rads. Accessed Apr 11, 2017
  3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409 https://doi.org/10.1056/NEJMoa1102873
  4. Jacobs C, van Rikxoort EM, Scholten ET, de Jong PA, Prokop M, Schaefer-Prokop C, et al. Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system. Invest Radiol 2015;50:168-173 https://doi.org/10.1097/RLI.0000000000000121
  5. van Riel SJ, Sanchez CI, Bankier AA, Naidich DP, Verschakelen J, Scholten ET, et al. Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management. Radiology 2015;277:863-871 https://doi.org/10.1148/radiol.2015142700
  6. Diederich S, Lenzen H, Windmann R, Puskas Z, Yelbuz TM, Henneken S, et al. Pulmonary nodules: experimental and clinical studies at low-dose CT. Radiology 1999;213:289-298 https://doi.org/10.1148/radiology.213.1.r99oc29289
  7. Gartenschlager M, Schweden F, Gast K, Westermeier T, Kauczor H, von Zitzewitz H, et al. Pulmonary nodules: detection with low-dose vs conventional-dose spiral CT. Eur Radiol 1998;8:609-614 https://doi.org/10.1007/s003300050445
  8. Kim H, Park CM, Chae HD, Lee SM, Goo JM. Impact of radiation dose and iterative reconstruction on pulmonary nodule measurements at chest CT: a phantom study. Diagn Interv Radiol 2015;21:459-465 https://doi.org/10.5152/dir.2015.14541
  9. Rusinek H, Naidich DP, McGuinness G, Leitman BS, McCauley DI, Krinsky GA, et al. Pulmonary nodule detection: low-dose versus conventional CT. Radiology 1998;209:243-249 https://doi.org/10.1148/radiology.209.1.9769838
  10. Obuchowski NA, Subhas N, Schoenhagen P. Testing for interchangeability of imaging tests. Acad Radiol 2014;21:1483-1489 https://doi.org/10.1016/j.acra.2014.07.004
  11. Ridge CA, Yildirim A, Boiselle PM, Franquet T, Schaefer-Prokop CM, Tack D, et al. Differentiating between subsolid and solid pulmonary nodules at CT: inter- and intraobserver agreement between experienced thoracic radiologists. Radiology 2016;278:888-896 https://doi.org/10.1148/radiol.2015150714
  12. Obuchowski NA. Can electronic medical images replace hardcopy film? Defining and testing the equivalence of diagnostic tests. Stat Med 2001;20:2845-2863 https://doi.org/10.1002/sim.929
  13. Yamashiro T, Miyara T, Honda O, Kamiya H, Murata K, Ohno Y, et al. Adaptive iterative dose reduction using three dimensional processing (AIDR3D) improves chest CT image quality and reduces radiation exposure. PLoS One 2014;9:e105735 https://doi.org/10.1371/journal.pone.0105735
  14. Lee SW, Kim Y, Shim SS, Lee JK, Lee SJ, Ryu YJ, et al. Image quality assessment of ultra low-dose chest CT using sinogramaffirmed iterative reconstruction. Eur Radiol 2014;24:817-826 https://doi.org/10.1007/s00330-013-3090-9
  15. Li B, Behrman RH. Comment on the "report of AAPM TG 204: size-specific dose estimates (SSDE) in pediatric and adult body CT examinations" [report of AAPM TG 204, 2011]. Med Phys 2012;39:4613-4614; author reply 4615-4616 https://doi.org/10.1118/1.4725756
  16. Brady SL, Kaufman RA. Investigation of American Association of physicists in medicine report 204 size-specific dose estimates for pediatric CT implementation. Radiology 2012;265:832-840 https://doi.org/10.1148/radiol.12120131
  17. McCollough C, Cody D, Edyvean S, Geise R, Gould B, Keat N, et al. The measurement, reporting, and management of radiation dose in CT. Virginia: American Association of Physicists in Medicine, 2008:1-34
  18. Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J. Fleischner society: glossary of terms for thoracic imaging. Radiology 2008;246:697-722 https://doi.org/10.1148/radiol.2462070712
  19. MacMahon H, Austin JH, Gamsu G, Herold CJ, Jett JR, Naidich DP, et al. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 2005;237:395-400 https://doi.org/10.1148/radiol.2372041887
  20. Yang Z, Zhou M. Kappa statistic for clustered matched-pair data. Stat Med 2014;33:2612-2633 https://doi.org/10.1002/sim.6113
  21. Fleiss J. Measuring nominal scale agreement among many raters. Psychol Bull 1971;76:378-382 https://doi.org/10.1037/h0031619
  22. Lee H, Kim B, Kim KJ, Seo J, Park S, Shin YG, et al. Introduction of heat map to fidelity assessment of compressed CT images. Med Phys 2011;38:4667-4671 https://doi.org/10.1118/1.3611046
  23. Gould MK, Donington J, Lynch WR, Mazzone PJ, Midthun DE, Naidich DP, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of chest physicians evidence-based clinical practice guidelines. Chest 2013;143(5 Suppl):e93S-e120S https://doi.org/10.1378/chest.12-2351
  24. Naidich DP, Bankier AA, MacMahon H, Schaefer-Prokop CM, Pistolesi M, Goo JM, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology 2013;266:304-317 https://doi.org/10.1148/radiol.12120628
  25. Lee CT. What do we know about ground-glass opacity nodules in the lung? Transl Lung Cancer Res 2015;4:656-659
  26. Lee SM, Park CM, Goo JM, Lee HJ, Wi JY, Kang CH. Invasive pulmonary adenocarcinomas versus preinvasive lesions appearing as ground-glass nodules: differentiation by using CT features. Radiology 2013;268:265-273 https://doi.org/10.1148/radiol.13120949
  27. Sun H, Wang W. Differentiating between subsolid and solid pulmonary nodules at CT: what is our main task? Radiology 2016;281:976-978 https://doi.org/10.1148/radiol.2016160593
  28. Vardhanabhuti V, Loader RJ, Mitchell GR, Riordan RD, Roobottom CA. Image quality assessment of standard-and low-dose chest CT using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative reconstruction algorithms. AJR Am J Roentgenol 2013;200:545-552 https://doi.org/10.2214/AJR.12.9424
  29. Katsura M, Matsuda I, Akahane M, Yasaka K, Hanaoka S, Akai H, et al. Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol 2013;48:206-212

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