European Radiology

, Volume 27, Issue 5, pp 1992–2001 | Cite as

Volume-based quantification using dual-energy computed tomography in the differentiation of thymic epithelial tumours: an initial experience

  • Suyon Chang
  • Jin Hur
  • Dong Jin Im
  • Young Joo Suh
  • Yoo Jin Hong
  • Hye-Jeong Lee
  • Young Jin Kim
  • Kyunghwa Han
  • Dae Joon Kim
  • Chang Young Lee
  • Ha Young Shin
  • Byoung Wook Choi
Chest
  • 278 Downloads

Abstract

Objectives

To investigate the diagnostic value of dual-energy computed tomography (DECT) in differentiating between low- and high-risk thymomas and thymic carcinomas.

Materials

Our institutional review board approved this study, and patients provided informed consent. We prospectively enrolled 37 patients (20 males, mean age: 55.6 years) with thymic epithelial tumour. All patients underwent DECT. For quantitative analysis, two reviewers measured the following tumour parameters: CT attenuation value in contrast Hounsfield units (CHU), iodine-related HU and iodine concentration (mg/ml). Pathological results confirmed the final diagnosis.

Results

Of the 37 thymic tumours, 23 (62.2 %) were low-risk thymomas, five (13.5 %) were high-risk thymomas and nine (24.3 %) were thymic carcinomas. According to quantitative analysis, iodine-related HU and iodine concentration were significantly different among low-risk thymomas, high-risk thymomas and thymic carcinomas (median: 29.78 HU vs. 14.55 HU vs. 19.95 HU, p = 0.001 and 1.92 mg/ml vs. 0.99 mg/ml vs. 1.18 mg/ml, p < 0.001, respectively).

Conclusion

DECT using a quantitative analytical method based on iodine concentration measurement can be used to differentiate among thymic epithelial tumours using single-phase scanning.

Key Points

IHU and IC were lower in high-risk thymomas/carcinomas than in low-risk thymomas

IHU and IC were lower in advanced-stage thymomas than in early-stage thymomas

Dual-energy CT helps differentiate among thymic epithelial tumours.

Keywords

Thymoma Thymic carcinoma Mediastinal mass Dual-energy computed tomography Iodine concentration 

Abbreviations

CHU

Contrast Hounsfield unit

DECT

Dual-energy computed tomography

HU

Hounsfield unit

IC

Iodine concentration

IHU

Iodine-related Hounsfield unit

VOI

Volume of interest

Notes

Acknowledgments

The scientific guarantor of this publication is Jin Hur, MD, PhD. 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. This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2012-R1A1A1013152). One of the authors has significant statistical expertise. Dr. Kyunghwa Han (Severance Hospital, Yonsei University College of Medicine) provided statistical advice in this study. Institutional Review Board approval was obtained. Written informed consent was obtained from all patients in this study. Methodology: prospective, diagnostic or prognostic study, performed at one institution.

References

  1. 1.
    Rosai J, Sobin LH (1999) Histological typing of tumours of the thymus. Springer, BerlinCrossRefGoogle Scholar
  2. 2.
    Detterbeck FC, Parsons AM (2004) Thymic tumours. Ann Thorac Surg 77:1860–1869CrossRefPubMedGoogle Scholar
  3. 3.
    Moran CA, Weissferdt A, Kalhor N et al (2012) Thymomas I: a clinicopathologic correlation of 250 cases with emphasis on the World Health Organization schema. Am J Clin Pathol 137:444–450CrossRefPubMedGoogle Scholar
  4. 4.
    Okumura M, Ohta M, Tateyama H et al (2002) The world health organization histologic classification system reflects the oncologic behavior of thymoma: a clinical study of 273 patients. Cancer 94:624–632CrossRefPubMedGoogle Scholar
  5. 5.
    Kim BK, Cho BC, Choi HJ et al (2008) A single institutional experience of surgically resected thymic epithelial tumors over 10 years: clinical outcomes and clinicopathologic features. Oncol Rep 19:1525–1531PubMedGoogle Scholar
  6. 6.
    Jeong YJ, Lee KS, Kim J, Shim YM, Han J, Kwon OJ (2004) Does CT of thymic epithelial tumors enable us to differentiate histologic subtypes and predict prognosis? AJR Am J Roentgenol 183:283–289CrossRefPubMedGoogle Scholar
  7. 7.
    Priola AM, Priola SM, Di Franco M, Cataldi A, Durando S, Fava C (2010) Computed tomography and thymoma: distinctive findings in invasive and noninvasive thymoma and predictive features of recurrence. Radiol Med 115:1–21CrossRefPubMedGoogle Scholar
  8. 8.
    Tomiyama N, Johkoh T, Mihara N et al (2002) Using the world health organization classification of thymic epithelial neoplasms to describe CT findings. AJR Am J Roentgenol 179:881–886CrossRefPubMedGoogle Scholar
  9. 9.
    Sadohara J, Fujimoto K, Muller NL et al (2006) Thymic epithelial tumors: comparison of CT and MR imaging findings of low-risk thymomas, high-risk thymomas, and thymic carcinomas. Eur J Radiol 60:70–79CrossRefPubMedGoogle Scholar
  10. 10.
    Han J, Lee KS, Yi CA et al (2003) Thymic epithelial tumors classified according to a newly established WHO scheme: CT and MR findings. Korean J Radiol 4:46–53CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Jung KJ, Lee KS, Han J, Kim J, Kim TS, Kim EA (2001) Malignant thymic epithelial tumors: CT-pathologic correlation. AJR Am J Roentgenol 176:433–439CrossRefPubMedGoogle Scholar
  12. 12.
    Johnson TR, Krauss B, Sedlmair M et al (2007) Material differentiation by dual energy CT: initial experience. Eur Radiol 17:1510–1517CrossRefPubMedGoogle Scholar
  13. 13.
    Graser A, Johnson TR, Chandarana H, Macari M (2009) Dual energy CT: preliminary observations and potential clinical applications in the abdomen. Eur Radiol 19:13–23CrossRefPubMedGoogle Scholar
  14. 14.
    Chae EJ, Song JW, Seo JB, Krauss B, Jang YM, Song KS (2008) Clinical utility of dual-energy CT in the evaluation of solitary pulmonary nodules: initial experience. Radiology 249:671–681CrossRefPubMedGoogle Scholar
  15. 15.
    Lee SH, Hur J, Kim YJ, Lee HJ, Hong YJ, Choi BW (2013) Additional value of dual-energy CT to differentiate between benign and malignant mediastinal tumors: an initial experience. Eur J Radiol 82:2043–2049CrossRefPubMedGoogle Scholar
  16. 16.
    Son JY, Lee HY, Kim JH et al (2016) Quantitative CT analysis of pulmonary ground-glass opacity nodules for distinguishing invasive adenocarcinoma from non-invasive or minimally invasive adenocarcinoma: the added value of using iodine mapping. Eur Radiol 26:43–54CrossRefPubMedGoogle Scholar
  17. 17.
    Chang S, Hur J, Im DJ et al (2015) Dual-energy CT-based iodine quantification for differentiating pulmonary artery sarcoma from pulmonary thromboembolism: a pilot study. Eur Radiol. doi: 10.1007/s00330-015-4140-2 Google Scholar
  18. 18.
    Masaoka A, Monden Y, Nakahara K, Tanioka T (1981) Follow-up study of thymomas with special reference to their clinical stages. Cancer 48:2485–2492CrossRefPubMedGoogle Scholar
  19. 19.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMedGoogle Scholar
  20. 20.
    Altman DG (1991) Practical statistics for medical research, 1st edn. Chapman and Hall, LondonGoogle Scholar
  21. 21.
    Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310CrossRefPubMedGoogle Scholar
  22. 22.
    Chalabreysse L, Roy P, Cordier JF, Loire R, Gamondes JP, Thivolet-Bejui F (2002) Correlation of the WHO schema for the classification of thymic epithelial neoplasms with prognosis: a retrospective study of 90 tumors. Am J Surg Pathol 26:1605–1611CrossRefPubMedGoogle Scholar
  23. 23.
    Strobel P, Marx A, Zettl A, Muller-Hermelink HK (2005) Thymoma and thymic carcinoma: an update of the WHO Classification 2004. Surg Today 35:805–811CrossRefPubMedGoogle Scholar
  24. 24.
    Okumura M, Miyoshi S, Fujii Y et al (2001) Clinical and functional significance of WHO classification on human thymic epithelial neoplasms: a study of 146 consecutive tumors. Am J Surg Pathol 25:103–110CrossRefPubMedGoogle Scholar
  25. 25.
    Yakushiji S, Tateishi U, Nagai S et al (2008) Computed tomographic findings and prognosis in thymic epithelial tumor patients. J Comput Assist Tomogr 32:799–805CrossRefPubMedGoogle Scholar
  26. 26.
    Suster S (2006) Diagnosis of thymoma. J Clin Pathol 59:1238–1244CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Marom EM, Milito MA, Moran CA et al (2011) Computed tomography findings predicting invasiveness of thymoma. J Thorac Oncol 6:1274–1281CrossRefPubMedGoogle Scholar
  28. 28.
    Zhang M, Kono M (1997) Solitary pulmonary nodules: evaluation of blood flow patterns with dynamic CT. Radiology 205:471–478CrossRefPubMedGoogle Scholar
  29. 29.
    Yi CA, Lee KS, Kim EA et al (2004) Solitary pulmonary nodules: dynamic enhanced multi-detector row CT study and comparison with vascular endothelial growth factor and microvessel density. Radiology 233:191–199CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Suyon Chang
    • 1
  • Jin Hur
    • 1
  • Dong Jin Im
    • 1
  • Young Joo Suh
    • 1
  • Yoo Jin Hong
    • 1
  • Hye-Jeong Lee
    • 1
  • Young Jin Kim
    • 1
  • Kyunghwa Han
    • 2
  • Dae Joon Kim
    • 3
  • Chang Young Lee
    • 3
  • Ha Young Shin
    • 4
  • Byoung Wook Choi
    • 1
  1. 1.Department of Radiology and Research Institute of Radiological Science, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
  2. 2.Yonsei Biomedical Research Institute, Department of Radiology, Research Institute of Radiological Science, Severance HospitalYonsei University College of MedicineSeoulRepublic of Korea
  3. 3.Department of Thoracic and Cardiovascular SurgeryYonsei University College of MedicineSeoulRepublic of Korea
  4. 4.Department of NeurologyYonsei University College of MedicineSeoulRepublic of Korea

Personalised recommendations