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Quantifying the effect of slice thickness, intravenous contrast and tube current on muscle segmentation: Implications for body composition analysis

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To quantify the effect of IV contrast, tube current and slice thickness on skeletal muscle cross-sectional area (CSA) and density (SMD) on routine CT.

Methods

CSA and SMD were computed on 216 axial CT images obtained at the L3 level in 72 patients with variations in IV contrast, slice thickness and tube current. Intra-patient mean difference (MD), 95 % CI and limits of agreement were calculated using the Bland-Altman approach. Inter- and intra-analyst agreement was evaluated.

Results

IV contrast significantly increased CSA by 1.88 % (MD 2.33 cm2; 95 % CI 1.76–2.89) and SMD by 5.99 % (p<0.0001). Five mm slice thickness significantly increased mean CSA by 1.11 % compared to 2 mm images (1.32 cm2; 0.78–1.85) and significantly decreased SMD by 11.64 % (p<0.0001). Low tube current significantly decreased mean CSA by 4.79 % (6.44 cm2; 3.78–9.10) and significantly increased SMD by 46.46 % (p<0.0001). Inter- and intra-analyst agreement was excellent.

Conclusions

IV contrast, slice thickness and tube current significantly affect CSA and SMD. Investigators designing and analysing clinical trials using CT for body composition analysis should report CT acquisition parameters and consider the effect of slice thickness, IV contrast and tube current on myometric data.

Key Points

• Intravenous contrast, slice thickness and tube current significantly affect myometric data.

• Image acquisition parameter variations may obscure intrapatient muscle differences on serial measurements.

• Investigators using CT for body composition analysis should report CT acquisition parameters.

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References

  1. Martin L, Birdsell L, Macdonald N et al (2013) Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index. J Clin Oncol 31:1539–1547

    Article  PubMed  Google Scholar 

  2. Gomez-Perez SL, Haus JM, Sheean P et al (2016) Measuring Abdominal Circumference and Skeletal Muscle From a Single Cross-Sectional Computed Tomography Image: A Step-by-Step Guide for Clinicians Using National Institutes of Health ImageJ. JPEN J Parenter Enteral Nutr 40:308–318

    Article  PubMed  Google Scholar 

  3. Malietzis G, Johns N, Al-Hassi HO et al (2016) Low Muscularity and Myosteatosis Is Related to the Host Systemic Inflammatory Response in Patients Undergoing Surgery for Colorectal Cancer. Ann Surg 263:320–325

    Article  PubMed  Google Scholar 

  4. Moisey LL, Mourtzakis M, Cotton BA et al (2013) Skeletal muscle predicts ventilator-free days, ICU-free days, and mortality in elderly ICU patients. Crit Care 17:R206

    Article  PubMed  PubMed Central  Google Scholar 

  5. Prado CM, Lieffers JR, McCargar LJ et al (2008) Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study. Lancet Oncol 9:629–635

    Article  PubMed  Google Scholar 

  6. Englesbe MJ, Lee JS, He K et al (2012) Analytic morphomics, core muscle size, and surgical outcomes. Ann Surg 256:255–261

    Article  PubMed  Google Scholar 

  7. Boutin RD, Yao L, Canter RJ, Lenchik L (2015) Sarcopenia: Current Concepts and Imaging Implications. AJR Am J Roentgenol 205:W255–W266

    Article  PubMed  Google Scholar 

  8. Tamandl D, Paireder M, Asari R, Baltzer PA, Schoppmann SF, Ba-Ssalamah A (2015) Markers of sarcopenia quantified by computed tomography predict adverse long-term outcome in patients with resected oesophageal or gastro-oesophageal junction cancer. Eur Radiol. https://doi.org/10.1007/s00330-015-3963-1

  9. Sjoblom B, Gronberg BH, Wentzel-Larsen T et al (2016) Skeletal muscle radiodensity is prognostic for survival in patients with advanced non-small cell lung cancer. Clin Nutr. https://doi.org/10.1016/j.clnu.2016.03.010

  10. Rollins KE, Tewari N, Ackner A et al (2016) The impact of sarcopenia and myosteatosis on outcomes of unresectable pancreatic cancer or distal cholangiocarcinoma. Clin Nutr 35:1103–1109

    Article  PubMed  Google Scholar 

  11. Chu MP, Lieffers J, Ghosh S et al (2015) Skeletal muscle radio-density is an independent predictor of response and outcomes in follicular lymphoma treated with chemoimmunotherapy. PLoS One 10:e0127589

    Article  PubMed  PubMed Central  Google Scholar 

  12. McDonald AM, Swain TA, Mayhew DL et al (2016) CT Measures of Bone Mineral Density and Muscle Mass Can Be Used to Predict Noncancer Death in Men with Prostate Cancer. Radiology. https://doi.org/10.1148/radiol.2016160626:160626

  13. Lee H, Troschel FM, Tajmir S et al (2017) Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis. J Digit Imaging 30:487–498

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cohen JG, Goo JM, Yoo RE et al (2016) The effect of late-phase contrast enhancement on semi-automatic software measurements of CT attenuation and volume of part-solid nodules in lung adenocarcinomas. Eur J Radiol 85:1174–1180

    Article  PubMed  Google Scholar 

  15. Athelogou M, Kim HJ, Dima A et al (2016) Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge. Acad Radiol 23:940–952

    Article  PubMed  Google Scholar 

  16. Boutin RD, Kaptuch JM, Bateni CP, Chalfant JS, Yao L (2016) Influence of IV Contrast Administration on CT Measures of Muscle and Bone Attenuation: Implications for Sarcopenia and Osteoporosis Evaluation. AJR Am J Roentgenol. https://doi.org/10.2214/AJR.16.16387:1-9

  17. Mitsiopoulos N, Baumgartner RN, Heymsfield SB, Lyons W, Gallagher D, Ross R (1998) Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography. J Appl Physiol (1985) 85:115–122

    Article  CAS  Google Scholar 

  18. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310

    Article  CAS  PubMed  Google Scholar 

  19. Singh S, Kalra MK, Do S et al (2012) Comparison of hybrid and pure iterative reconstruction techniques with conventional filtered back projection: dose reduction potential in the abdomen. J Comput Assist Tomogr 36:347–353

    Article  PubMed  Google Scholar 

  20. Singh S, Kalra MK, Gilman MD et al (2011) Adaptive statistical iterative reconstruction technique for radiation dose reduction in chest CT: a pilot study. Radiology 259:565–573

    Article  PubMed  Google Scholar 

  21. Singh S, Kalra MK, Hsieh J et al (2010) Abdominal CT: comparison of adaptive statistical iterative and filtered back projection reconstruction techniques. Radiology 257:373–383

    Article  PubMed  Google Scholar 

  22. Weijs PJ, Looijaard WG, Dekker IM et al (2014) Low skeletal muscle area is a risk factor for mortality in mechanically ventilated critically ill patients. Crit Care 18:R12

    Article  PubMed  PubMed Central  Google Scholar 

  23. Mueller N, Murthy S, Tainter CR et al (2016) Can Sarcopenia Quantified by Ultrasound of the Rectus Femoris Muscle Predict Adverse Outcome of Surgical Intensive Care Unit Patients as well as Frailty? A Prospective, Observational Cohort Study. Ann Surg 264:1116–1124

    Article  PubMed  PubMed Central  Google Scholar 

  24. Temel JS, Abernethy AP, Currow DC et al (2016) Anamorelin in patients with non-small-cell lung cancer and cachexia (ROMANA 1 and ROMANA 2): results from two randomised, double-blind, phase 3 trials. Lancet Oncol 17:519–531

    Article  CAS  PubMed  Google Scholar 

  25. Miljkovic I, Zmuda JM (2010) Epidemiology of myosteatosis. Curr Opin Clin Nutr Metab Care 13:260–264

    Article  PubMed  PubMed Central  Google Scholar 

  26. Goodpaster BH, Kelley DE, Thaete FL, He J, Ross R (2000) Skeletal muscle attenuation determined by computed tomography is associated with skeletal muscle lipid content. J Appl Physiol (1985) 89:104–110

    Article  CAS  Google Scholar 

  27. Mamane S, Mullie L, Piazza N et al (2016) Psoas Muscle Area and All-Cause Mortality After Transcatheter Aortic Valve Replacement: The Montreal-Munich Study. Can J Cardiol 32:177–182

    Article  PubMed  Google Scholar 

  28. Reisinger KW, van Vugt JL, Tegels JJ et al (2015) Functional compromise reflected by sarcopenia, frailty, and nutritional depletion predicts adverse postoperative outcome after colorectal cancer surgery. Ann Surg 261:345–352

    Article  PubMed  Google Scholar 

  29. Yip C, Dinkel C, Mahajan A, Siddique M, Cook GJ, Goh V (2015) Imaging body composition in cancer patients: visceral obesity, sarcopenia and sarcopenic obesity may impact on clinical outcome. Insights Imaging 6:489–497

    Article  PubMed  PubMed Central  Google Scholar 

  30. Prado CM, Heymsfield SB (2014) Lean tissue imaging: a new era for nutritional assessment and intervention. JPEN J Parenter Enteral Nutr 38:940–953

    Article  PubMed  PubMed Central  Google Scholar 

  31. Solomon JB, Christianson O, Samei E (2012) Quantitative comparison of noise texture across CT scanners from different manufacturers. Med Phys 39:6048–6055

    Article  PubMed  Google Scholar 

  32. Soderberg M, Gunnarsson M (2010) Automatic exposure control in computed tomography--an evaluation of systems from different manufacturers. Acta Radiol 51:625–634

    Article  PubMed  Google Scholar 

  33. Rosset A, Spadola L, Ratib O (2004) OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 17:205–216

    Article  PubMed  PubMed Central  Google Scholar 

  34. Dello SA, Lodewick TM, van Dam RM et al (2013) Sarcopenia negatively affects preoperative total functional liver volume in patients undergoing liver resection. HPB (Oxford) 15:165–169

    Article  Google Scholar 

  35. Reisinger KW, Derikx JP, van Vugt JL et al (2016) Sarcopenia is associated with an increased inflammatory response to surgery in colorectal cancer. Clin Nutr 35:924–927

    Article  PubMed  Google Scholar 

  36. Tegels JJ, van Vugt JL, Reisinger KW et al (2015) Sarcopenia is highly prevalent in patients undergoing surgery for gastric cancer but not associated with worse outcomes. J Surg Oncol 112:403–407

    Article  PubMed  Google Scholar 

  37. Haak D, Page CE, Deserno TM (2016) A Survey of DICOM Viewer Software to Integrate Clinical Research and Medical Imaging. J Digit Imaging 29:206–215

    Article  PubMed  Google Scholar 

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Acknowledgements

Georg Fuchs thanks the Rolf W. Günther Foundation for their generous support.

Author information

Author notes

  1. Matthias Eikermann

    Present address: Harvard Medical Faculty Physicians (HMFP), Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA, 02215, USA

Authors and Affiliations

  1. Department of Radiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, 10117, Berlin, Germany

    Georg Fuchs

  2. Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA

    Georg Fuchs, Yves R. Chretien, Julia Mario, Synho Do, Bob Liu, Kai Yang & Florian J. Fintelmann

  3. Department of Anesthesia, Critical Care and Pain Medicine, 55 Fruit Street, Boston, MA, 02114, USA

    Matthias Eikermann

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Correspondence to Florian J. Fintelmann.

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The scientific guarantor of this publication is Prof. Dr. med. Florian Fintelmann.

Conflict of interest

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Yves R. Chretien, M.D., Ph.D. has significant statistical expertise (Ph.D. in Statistics from Harvard University) and kindly provided statistical advice for this manuscript.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

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Fuchs, G., Chretien, Y.R., Mario, J. et al. Quantifying the effect of slice thickness, intravenous contrast and tube current on muscle segmentation: Implications for body composition analysis. Eur Radiol 28, 2455–2463 (2018). https://doi.org/10.1007/s00330-017-5191-3

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  • DOI: https://doi.org/10.1007/s00330-017-5191-3

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