Skip to main content
SpringerLink
Log in

Diffusion-weighted magnetic resonance imaging of thymoma: ability of the Apparent Diffusion Coefficient in predicting the World Health Organization (WHO) classification and the Masaoka-Koga staging system and its prognostic significance on disease-free survival

  • Molecular Imaging
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To evaluate the usefulness of diffusion-weighted magnetic resonance for distinguishing thymomas according to WHO and Masaoka-Koga classifications and in predicting disease-free survival (DFS) by using the apparent diffusion coefficient (ADC).

Methods

Forty-one patients were grouped based on WHO (low-risk vs. high-risk) and Masaoka-Koga (early vs. advanced) classifications. For prognosis, seven patients with recurrence at follow-up were grouped separately from healthy subjects. Differences on ADC levels between groups were tested using Student-t testing. Logistic regression models and areas under the ROC curve (AUROC) were estimated.

Results

Mean ADC values were different between groups of WHO (low-risk = 1.58 ± 0.20 × 10-3mm2/sec; high-risk = 1.21 ± 0.23 × 10-3mm2/sec; p < 0.0001) and Masaoka-Koga (early = 1.43 ± 0.26 × 10-3mm2/sec; advanced = 1.31 ± 0.31 × 10-3mm2/sec; p = 0.016) classifications. Mean ADC of type-B3 (1.05 ± 0.17 × 10-3mm2/sec) was lower than type-B2 (1.32 ± 0.20 × 10-3mm2/sec; p = 0.023). AUROC in discriminating groups was 0.864 for WHO classification (cut-point = 1.309 × 10-3mm2/sec; accuracy = 78.1 %) and 0.730 for Masaoka-Koga classification (cut-point = 1.243 × 10-3mm2/sec; accuracy = 73.2 %). Logistic regression models and two-way ANOVA were significant for WHO classification (odds ratio[OR] = 0.93, p = 0.007; p < 0.001), but not for Masaoka-Koga classification (OR = 0.98, p = 0.31; p = 0.38). ADC levels were significantly associated with DFS recurrence rate being higher for patients with ADC ≤ 1.299 × 10-3mm2/sec (p = 0.001; AUROC, 0.834; accuracy = 78.0 %).

Conclusions

ADC helps to differentiate high-risk from low-risk thymomas and discriminates the more aggressive type-B3. Primary tumour ADC is a prognostic indicator of recurrence.

Key Points

• DW-MRI is useful in characterizing thymomas and in predicting disease-free survival.

• ADC can differentiate low-risk from high-risk thymomas based on different histological composition

• The cutoff-ADC-value of 1.309 × 10 -3 mm 2 /sec is proposed as optimal cut-point for this differentiation

• The ADC ability in predicting Masaoka-Koga stage is uncertain and needs further validations

• ADC has prognostic value on disease-free survival and helps in stratification of risk

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Priola AM, Priola SM, Cardinale L, Cataldi A, Fava C (2006) The anterior mediastinum: diseases. Radiol Med 111:312–342

    Article  CAS  PubMed  Google Scholar 

  2. Wright CD (2008) Management of thymomas. Crit Rev Oncol Hematol 65:109–120

    Article  PubMed  Google Scholar 

  3. Nishino M, Ashiku SK, Kocher ON, Thurer RL, Boiselle PM, Hatabu H (2006) The thymus: a comprehensive review. Radiographics 26:335–348

    Article  PubMed  Google Scholar 

  4. Henschke CI, Lee IJ, Wu N et al (2006) CT screening for lung cancer: prevalence and incidence of mediastinal masses. Radiology 239:586–590

    Article  PubMed  Google Scholar 

  5. Novello S, Fava C, Borasio P et al (2005) Three-year findings of an early lung cancer detection feasibility study with low-dose spiral computed tomography in heavy smokers. Ann Oncol 16:1662–1666

    Article  CAS  PubMed  Google Scholar 

  6. Priola AM, Priola SM, Giaj-Levra M et al (2013) Clinical implications and added costs of incidental findings in an early detection study of lung cancer by using low-dose spiral computed tomography. Clin Lung Cancer 14:139–148

    Article  PubMed  Google Scholar 

  7. Müller-Hermelink HK, Ströbel P, Zettl A et al (2004) Combined thymic epithelial tumours. In: Travis WD, Brambilla E, Müller-Hermelink HK, Harris CC (eds) Pathology and genetics of tumours of the lung, pleura, thymus and heart (WHO classification of tumours series). IARC Press, Lyon, pp 196–198

    Google Scholar 

  8. Masaoka A, Monden Y, Nakahara K, Tanioka T (1981) Follow-up study of thymomas with special reference to their clinical stages. Cancer 48:2485–2492

    Article  CAS  PubMed  Google Scholar 

  9. 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 183:283–289

    Article  PubMed  Google Scholar 

  10. Okuma Y, Hosomi Y, Watanabe K et al (2014) Clinicopathological analysis of thymic malignancies with a consistent retrospective database in a single institution: from Tokyo Metropolitan Cancer Center. BMC Cancer 14:349–357

    Article  PubMed  PubMed Central  Google Scholar 

  11. Priola AM, Priola SM (2014) Imaging of thymus in myasthenia gravis: from thymic hyperplasia to thymic tumor. Clin Radiol 69:e230–e245

    Article  CAS  PubMed  Google Scholar 

  12. Landwehr P, Schulte O, Lackner K (1999) MR imaging of the chest: mediastinum and chest wall. Eur Radiol 9:1737–1744

    Article  CAS  PubMed  Google Scholar 

  13. Priola SM, Priola AM, Cardinale L, Perotto F, Fava C (2006) The anterior mediastinum: anatomy and imaging procedures. Radiol Med 111:295–311

    Article  CAS  PubMed  Google Scholar 

  14. Shin KE, Yi CA, Kim TS et al (2014) Diffusion-weighted MRI for distinguishing non-neoplastic cysts from solid masses in the mediastinum: problem-solving in mediastinal masses of indeterminate internal characteristics on CT. Eur Radiol 24:677–684

    Article  PubMed  Google Scholar 

  15. Gümüștaș S, Inan N, Sarisoy HT (2011) Malignant versus benign mediastinal lesions: quantitative assessment with diffusion weighted MR imaging. Eur Radiol 21:2255–2260

    Article  PubMed  Google Scholar 

  16. Abdel Razek A, Soliman N, Elashery R (2012) Apparent diffusion coefficient values of mediastinal masses in children. Eur J Radiol 81:1311–1314

    Article  PubMed  Google Scholar 

  17. Abdel Razek A, Elmorsy A, Elshafey M, Elhadedy T, Hamza O (2009) Assessment of mediastinal tumors with diffusion weighted single shot echo planar MR imaging. J Magn Reson Imaging 30:535–540

    Article  Google Scholar 

  18. Seki S, Koyama H, Ohno Y et al (2014) Diffusion-weighted MR imaging vs. multi-detector row-CT: direct comparison of capability for assessment of management needs for anterior mediastinal solitary tumors. Eur J Radiol 83:835–842

    Article  PubMed  Google Scholar 

  19. Abdel Razek AA, Khairy M, Nada N (2015) Diffusion-weighted MR imaging in thymic epithelial tumors: correlation with World Health Organization classification and clinical staging. Radiology 273:268–275

    Article  Google Scholar 

  20. Priola AM, Priola SM (2015) Usefulness of diffusion-weighted MR imaging in predicting Masaoka-Koga clinical staging of thymic epithelial tumors by using the apparent diffusion coefficient. Radiology 274:936–937

    Article  PubMed  Google Scholar 

  21. Filli L, Wurnig MC, Luechinger R, Eberhardt C, Guggenberger R, Boss A (2015) Whole-body intravoxel incoherent motion imaging. Eur Radiol. doi:10.1007/s00330-014-3577-z

    Google Scholar 

  22. Zhang YD, Wang Q, Wu CJ (2015) The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer. Eur Radiol 25:994–1004

    Article  PubMed  Google Scholar 

  23. Padhani AR, Guoying L, Mu-Koh D et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11:102–125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Koh DM, Collins DJ (2007) Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am J Roentgenol 188:1622–1635

    Article  PubMed  Google Scholar 

  25. Priola AM, Priola SM (2013) Influence of selected b values on ADC quantification in diffusion-weighted MRI. Comment on Punwani et al.: Diffusion-weighted MRI of lymphoma: prognostic utility and implications for PET/MRI? Eur J Nucl Med Mol Imaging 40:1108–1809

    Article  CAS  PubMed  Google Scholar 

  26. Priola AM, Priola SM, Giraudo MT et al (2015) Chemical-shift and diffusion-weighted magnetic resonance imaging of thymus in myasthenia gravis: usefulness of quantitative assessment. Investig Radiol 50:228–238

    Article  Google Scholar 

  27. Priola AM, Priola SM, Ciccone G et al (2015) Differentiation of rebound and lymphoid thymic hyperplasia from anterior mediastinal tumors with dual-echo chemical-shift magnetic resonance imaging in adulthood: reliability of the chemical-shift ratio and signal-intensity index. Radiology 274:238–249

    Article  PubMed  Google Scholar 

  28. Inchingolo R, De Gaetano AM, Curione D et al (2015) Role of diffusion-weighted imaging, apparent diffusion coefficient and correlation with hepatobiliary phase findings in the differentiation of hepatocellular carcinoma from dysplastic nodules in cirrhotic liver. Eur Radiol 25:1087–1096

    Article  PubMed  Google Scholar 

  29. Fischer MA, Nanz D, Hany T et al (2011) Diagnostic accuracy of whole-body MRI/DWI image fusion for detection of malignant tumours: a comparison with PET/CT. Eur Radiol 21:246–255

    Article  PubMed  Google Scholar 

  30. Priola AM, Galetto G, Priola SM (2014) Diagnostic and functional imaging of thymic and mediastinal involvement in lymphoproliferative disorders. Clin Imaging 38:771–784

    Article  PubMed  Google Scholar 

  31. Rieker RJ, Hoegel J, Morresi-Hauf A et al (2002) Histologic classification of thymic epithelial tumors: comparison of established classification schemes. Int J Cancer 98:900–906

    Article  CAS  PubMed  Google Scholar 

  32. Schmidt H, Gatidis S, Schwenzer NF, Martirosian P (2015) Impact of measurement parameters on apparent diffusion coefficient quantification in diffusion-weighted-magnetic resonance imaging. Investig Radiol 50:46–56

    Article  Google Scholar 

  33. Ackman JB (2014) A practical guide to nonvascular thoracic magnetic resonance imaging. J Thorac Imaging 29:17–29

    Article  PubMed  Google Scholar 

  34. Abdel Razek AAK (2012) Diffusion magnetic resonance imaging of chest tumors. Cancer Imaging 12:452–463

    Article  PubMed Central  Google Scholar 

  35. Padhani AR, Koh DM, Collins DJ (2011) Whole-body diffusion-weighted MR imaging in cancer: current status and research directions. Radiology 261:700–718

    Article  PubMed  Google Scholar 

  36. Roden AC, Yi ES, Cassivi SD, Jenkins SM, Garces YI, Aubry MC (2013) Clinicopathological features of thymic carcinomas and the impact of histopathological agreement on prognostical studies. Eur J Cardiothorac Surg 43:1131–1139

    Article  PubMed  Google Scholar 

  37. Hatakenaka M, Nakamura K, Yabuuchi U et al (2014) Apparent diffusion coefficient is a prognostic factor of head and neck squamous cell carcinoma treated with radiotherapy. Jpn J Radiol 32:80–89

    Article  PubMed  Google Scholar 

  38. Giganti F, Orsenigo E, Esposito A et al (2015) Prognostic role of diffusion-weighted MR imaging for resectable gastric cancer. Radiology. doi:10.1148/radiol.15141900

    PubMed  Google Scholar 

  39. Nakamura K, Joja I, Nagasaka T et al (2012) The mean apparent diffusion coefficient value (ADCmean) on primary cervical cancer is a predictive marker for disease recurrence. Gynecol Oncol 127:478–483

    Article  PubMed  Google Scholar 

  40. Seki N, Sakamoto S, Karube Y, Oyaizu T, Ishihama H, Chida M (2014) 18F-fluorodeoxyglucose positron emission tomography for evaluation of thymic epithelial tumors: utility for World Health Organization classification and predicting recurrence-free survival. Ann Nucl Med 28:257–262

    Article  CAS  PubMed  Google Scholar 

  41. Kaira K, Murakami H, Miura S et al (2011) 18F-FDG uptake on PET helps predict outcome and response after treatment in unresectable thymic epithelial tumors. Ann Nucl Med 25:247–253

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The scientific guarantor of this publication is Adriano M. Priola. 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. The authors state that this work has not received any funding. One of the authors (Maria T. Giraudo) has significant statistical expertise. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: prospective, observational/diagnostic or prognostic study, performed at one institution.

Author information

Authors and Affiliations

  1. Department of Diagnostic Imaging, San Luigi Gonzaga University Hospital, Regione Gonzole 10, 10043, Orbassano (Torino), Italy

    Adriano Massimiliano Priola, Sandro Massimo Priola, Dario Gned, Lorena Ducco & Andrea Veltri

  2. Department of Pathology, San Luigi Gonzaga University Hospital, Regione Gonzole 10, 10043, Orbassano (Torino), Italy

    Alessandro Fornari

  3. Department of Neurology, San Luigi Gonzaga University Hospital, Regione Gonzole 10, 10043, Orbassano (Torino), Italy

    Bruno Ferrero

  4. Department of Mathematics “Giuseppe Peano”, University of Torino, Via Carlo Alberto 10, 10123, Torino, Italy

    Maria Teresa Giraudo

Authors
  1. Adriano Massimiliano Priola
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sandro Massimo Priola
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maria Teresa Giraudo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dario Gned
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alessandro Fornari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bruno Ferrero
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lorena Ducco
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Andrea Veltri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adriano Massimiliano Priola.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Priola, A.M., Priola, S.M., Giraudo, M.T. et al. Diffusion-weighted magnetic resonance imaging of thymoma: ability of the Apparent Diffusion Coefficient in predicting the World Health Organization (WHO) classification and the Masaoka-Koga staging system and its prognostic significance on disease-free survival. Eur Radiol 26, 2126–2138 (2016). https://doi.org/10.1007/s00330-015-4031-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-015-4031-6

Keywords

Search

Navigation