Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Development and Validation of Generalized Linear Regression Models to Predict Vessel Enhancement on Coronary CT Angiography

  • Masuda, Takanori (Department of Radiological Technology, Tsuchiya General Hospital) ;
  • Nakaura, Takeshi (Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University) ;
  • Funama, Yoshinori (Department of Medical Physics, Faculty of Life Sciences, Kumamoto University) ;
  • Sato, Tomoyasu (Department of Diagnostic Radiology, Tsuchiya General Hospital) ;
  • Higaki, Toru (Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University) ;
  • Kiguchi, Masao (Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University) ;
  • Matsumoto, Yoriaki (Department of Radiological Technology, Tsuchiya General Hospital) ;
  • Yamashita, Yukari (Department of Radiological Technology, Tsuchiya General Hospital) ;
  • Imada, Naoyuki (Department of Radiological Technology, Tsuchiya General Hospital) ;
  • Awai, Kazuo (Department of Diagnostic Radiology, Graduate School of Biomedical Sciences, Hiroshima University)
  • Received : 2018.01.17
  • Accepted : 2018.04.24
  • Published : 2018.12.01
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Abstract

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

Keywords

References

  1. Gilard M, Cornily JC, Pennec PY, Joret C, Le Gal G, Mansourati J, et al. Accuracy of multislice computed tomography in the preoperative assessment of coronary disease in patients with aortic valve stenosis. J Am Coll Cardiol 2006;47:2020-2024 https://doi.org/10.1016/j.jacc.2005.11.085
  2. Cademartiri F, Mollet NR, Lemos PA, Saia F, Midiri M, de Feyter PJ, et al. Higher intracoronary attenuation improves diagnostic accuracy in MDCT coronary angiography. AJR Am J Roentgenol 2006;187:W430-W433 https://doi.org/10.2214/AJR.05.1406
  3. Becker CR, Hong C, Knez A, Leber A, Bruening R, Schoepf UJ, et al. Optimal contrast application for cardiac 4-detector-row computed tomography. Invest Radiol 2003;38:690-694 https://doi.org/10.1097/01.rli.0000084886.44676.e4
  4. Fei X, Du X, Yang Q, Shen Y, Li P, Liao J, et al. 64-MDCT coronary angiography: phantom study of effects of vascular attenuation on detection of coronary stenosis. AJR Am J Roentgenol 2008;191:43-49 https://doi.org/10.2214/AJR.07.2653
  5. Bae KT, Heiken JP, Brink JA. Aortic and hepatic contrast medium enhancement at CT. Part I. Prediction with a computer model. Radiology 1998;207:647-655 https://doi.org/10.1148/radiology.207.3.9609886
  6. Bae KT, Heiken JP, Brink JA. Aortic and hepatic peak enhancement at CT: effect of contrast medium injection rate--pharmacokinetic analysis and experimental porcine model. Radiology 1998;206:455-464 https://doi.org/10.1148/radiology.206.2.9457200
  7. Fleischmann D, Hittmair K. Mathematical analysis of arterial enhancement and optimization of bolus geometry for CT angiography using the discrete fourier transform. J Comput Assist Tomogr 1999;23:474-484 https://doi.org/10.1097/00004728-199905000-00026
  8. Fleischmann D, Rubin GD, Bankier AA, Hittmair K. Improved uniformity of aortic enhancement with customized contrast medium injection protocols at CT angiography. Radiology 2000;214:363-371 https://doi.org/10.1148/radiology.214.2.r00fe18363
  9. Awai K, Hiraishi K, Hori S. Effect of contrast material injection duration and rate on aortic peak time and peak enhancement at dynamic CT involving injection protocol with dose tailored to patient weight. Radiology 2004;230:142-150 https://doi.org/10.1148/radiol.2301021008
  10. Bae KT, Seeck BA, Hildebolt CF, Tao C, Zhu F, Kanematsu M, et al. Contrast enhancement in cardiovascular MDCT: effect of body weight, height, body surface area, body mass index, and obesity. AJR Am J Roentgenol 2008;190:777-784 https://doi.org/10.2214/AJR.07.2765
  11. Kondo H, Kanematsu M, Goshima S, Tomita Y, Kim MJ, Moriyama N, et al. Body size indexes for optimizing iodine dose for aortic and hepatic enhancement at multidetector CT: comparison of total body weight, lean body weight, and blood volume. Radiology 2010;254:163-169 https://doi.org/10.1148/radiol.09090369
  12. Nakaura T, Awai K, Yanaga Y, Namimoto T, Utsunomiya D, Hirai T, et al. Low-dose contrast protocol using the test bolus technique for 64-detector computed tomography coronary angiography. Jpn J Radiol 2011;29:457-465 https://doi.org/10.1007/s11604-011-0579-5
  13. Imai E, Horio M, Nitta K, Yamagata K, Iseki K, Tsukamoto Y, et al. Modification of the Modification of Diet in Renal Disease (MDRD) study equation for Japan. Am J Kidney Dis 2007;50:927-937 https://doi.org/10.1053/j.ajkd.2007.09.004
  14. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, et al.; Collaborators developing the Japanese equation for estimated GFR. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis 2009;53:982-992 https://doi.org/10.1053/j.ajkd.2008.12.034
  15. Hendel RC, Patel MR, Kramer CM, Poon M, Hendel RC, Carr JC, et al. ACCF/ACR/SCCT/SCMR/ASNC/NASCI/SCAI/SIR 2006 appropriateness criteria for cardiac computed tomography and cardiac magnetic resonance imaging: a report of the American College of Cardiology Foundation Quality Strategic Directions Committee Appropriateness Criteria Working Group, American College of Radiology, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, American Society of Nuclear Cardiology, North American Society for Cardiac Imaging, Society for Cardiovascular Angiography and Interventions, and Society of Interventional Radiology. J Am Coll Cardiol 2006;48:1475-1497 https://doi.org/10.1016/j.jacc.2006.07.003
  16. Kidoh M, Nakaura T, Awai K, Utsunomiya D, Sakaino N, Harada K, et al. Compact-bolus dynamic CT protocol with a test bolus technique in 64-MDCT coronary angiography: comparison of fixed injection rate and duration protocol. Jpn J Radiol 2013;31:115-122 https://doi.org/10.1007/s11604-012-0158-4
  17. Awai K, Kanematsu M, Kim T, Ichikawa T, Nakamura Y, Nakamoto A, et al. The optimal body size index with which to determine iodine dose for hepatic dynamic CT: a prospective multicenter study. Radiology 2016;278:773-781 https://doi.org/10.1148/radiol.2015142941
  18. Fleischmann D. How to design injection protocols for multiple detector-row CT angiography (MDCTA). Eur Radiol 2005;15 Suppl 5:E60-E65 https://doi.org/10.1007/s10406-005-0166-x
  19. Masuda T, Funama Y, Imada N, Sato T, Yamagami T, Tatsugami F, et al. Prediction of aortic enhancement on coronary CTA images using a test bolus of diluted contrast material. Acad Radiol 2014;21:1542-1546 https://doi.org/10.1016/j.acra.2014.07.002
  20. Bae KT. Intravenous contrast medium administration and scan timing at CT: considerations and approaches. Radiology 2010;256:32-61 https://doi.org/10.1148/radiol.10090908
  21. Svensson A, Ripsweden J, Ruck A, Aspelin P, Cederlund K, Brismar BT. Heart rate variability and heat sensation during CT coronary angiography: low-osmolar versus iso-osmolar contrast media. Acta Radiol 2010;51:722-726 https://doi.org/10.3109/02841851.2010.488249

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