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Multilobulated thymoma with an acute angle: a new predictor of lung invasion

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Abstract

Objective

Imaging features of thymomas such as lobulation, infiltration into lung, and adjacent lung abnormality have been associated with lung invasion but are unreliable. The goal of this study was to develop a more objective and reproducible method for predicting lung invasion by thymomas.

Subjects and methods

Fifty-four thymomas resected from 2007 to 2017 were included for analysis. Pre-operative CT scans for these thymomas were reviewed, and multiple features were evaluated, including the interface of each thymoma with the adjacent lung. A multilobulated thymoma with at least one acute angle between lobulations was considered suspicious for lung invasion. Two blinded radiologists then tested this hypothesis by reviewing all 54 CT scans and using this single criterion to predict lung invasion.

Results

Twelve thymomas invaded the lung. All lung-invasive thymomas were multilobulated. Twenty-nine thymomas had a multilobulated interface with the lung. Multilobulated thymomas were more likely to invade the lung than thymomas with a single lobulation or no lobulation (p = 0.0008). Using the criterion of multilobulation with at least one acute angle between lobulations to predict lung invasion, the two readers achieved a sensitivity of 67–83%, specificity of 93–98%, positive predictive value of 77–89%, and negative predicted value of 91–95%. Nine lung-invasive thymomas also invaded mediastinal structures or disseminated to the pleura.

Conclusions

A multilobulated thymoma with at least one acute angle between lobulations predicts lung invasion with a high degree of accuracy. When lung invasion is suspected, the findings are indicative of a locally aggressive tumor, and the pleura and mediastinal structures should also be closely inspected for invasion.

Key Points

• A multilobulated thymoma with at least one acute angle between lobulations is predictive of lung invasion.

• Coronal and sagittal reformations and thin sections are helpful in challenging cases.

• Lung invasion indicates a locally aggressive tumor, and the pleura and other mediastinal structures should also be closely inspected for invasion.

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Abbreviations

IASLC:

International Association for the Study of Lung Cancer

ITMIG:

International Thymic Malignancies Interest Group

LI:

Lung invasion

TET:

Thymic epithelial tumor

WHO:

World Health Organization

References

  1. 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 

  2. Koga K, Matsuno Y, Noguchi M et al (1994) A review of 79 thymomas: modification of staging system and reappraisal of conventional division into invasive and non-invasive thymoma. Pathol Int 44:359–367

    Article  CAS  PubMed  Google Scholar 

  3. Detterbeck FC, Stratton K, Giroux D et al (2014) The IASLC/ITMIG Thymic Epithelial Tumors Staging Project: proposal for an evidence-based stage classification system for the forthcoming (8th) edition of the TNM Classification of Malignant Tumors. J Thorac Oncol 9(9 Suppl 2):S65–S72

    Article  PubMed  Google Scholar 

  4. Nicholson AG, Detterbeck FC, Marino M et al (2014) The IASLC/ITMIG Thymic Epithelial Tumors Staging Project: proposals for the T component for the forthcoming (8th) edition of the TNM Classification of Malignant Tumors. J Thorac Oncol 9(9 Suppl 2):S73–S80

    Article  PubMed  Google Scholar 

  5. Kondo K, Van Schil P, Detterbeck FC et al (2014) The IASLC/ITMIG Thymic Epithelial Tumors Staging Project: proposals for the N and M components for the forthcoming (8th) edition of the TNM Classification of Malignant Tumors. J Thorac Oncol 9(9 Suppl 2):S81–S87

    Article  PubMed  Google Scholar 

  6. Toker A, Sonett J, Zielinski M, Rea F, Tomulescu V, Detterbeck FC (2011) Standard terms, definitions, and policies for minimally invasive resection of thymoma. J Thorac Oncol 6(7 Suppl 3):S1739–S1742

    Article  PubMed  Google Scholar 

  7. Blumberg D, Port JL, Weksler B et al (1995) Thymoma: a multivariate analysis of factors predicting survival. Ann Thorac Surg 60:908–913

  8. Rea F, Marulli G, Girardi R et al (2004) Long-term survival and prognostic factors in thymic epithelial tumours. Eur J Cardiothorac Surg 26:412–418

  9. Hayes SA, Huang J, Plodkowski AJ et al (2014) Preoperative computed tomography findings predict surgical resectability of thymoma. J Thorac Oncol 9(7):1023–1030

    Article  PubMed  Google Scholar 

  10. Ströbel P, Bauer A, Puppe B et al (2004) Tumor recurrence and survival in patients treated for thymomas and thymic squamous cell carcinomas: a retrospective analysis. J Clin Oncol 22(8):1501–1509

    Article  PubMed  Google Scholar 

  11. Marulli G, Lucchi M, Margaritora S et al (2011) Surgical treatment of stage III thymic tumors: a multi-institutional review from four Italian centers. Eur J Cardiothorac Surg 39(3):e1–e7

  12. Haniuda M, Kondo R, Numanami H, Makiuchi A, Machida E, Amano J (2001) Recurrence of thymoma: clinicopathological features, re-operation, and outcome. J Surg Oncol 78:183–188

    Article  CAS  PubMed  Google Scholar 

  13. 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):1–21

    Article  CAS  PubMed  Google Scholar 

  14. Qu YJ, Liu GB, Shi HS, Liao MY, Yang GF, Tian ZX (2013) Preoperative CT findings of thymoma are correlated with postoperative Masaoka clinical stage. Acad Radiol 20(1):66–72

    Article  PubMed  Google Scholar 

  15. Zhao Y, Chen H, Shi J, Fan L, Hu D, Zhao H (2015) The correlation of morphological features of chest computed tomographic scans with clinical characteristics of thymoma. Eur J Cardiothorac Surg 48(5):698–704

    Article  PubMed  Google Scholar 

  16. Ozawa Y, Hara M, Shimohira M, Sakurai K, Nakagawa M, Shibamoto Y (2016) Associations between computed tomography features of thymomas and their pathological classification. Acta Radiol 57(11):1318–1325

    Article  PubMed  Google Scholar 

  17. Padda SK, Terrone D, Tian L et al (2018) Computed tomography features associated with the eighth edition TNM stage classification for thymic epithelial tumors. J Thorac Imaging 33(3):176–183

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yang WT, Lei KI, Metreweli C (1997) Plain radiography and computed tomography of invasive thymomas: clinico-radiologic-pathologic correlation. Australas Radiol 41(2):118–124

    Article  CAS  PubMed  Google Scholar 

  19. Tomiyama N, Müller NL, Johkoh T et al (2001) Acute respiratory distress syndrome and acute interstitial pneumonia: comparison of thin-section CT findings. J Comput Assist Tomogr 25(1):28–33

    Article  CAS  PubMed  Google Scholar 

  20. Marom EM, Milito MA, Moran CA et al (2011) Computed tomography findings predicting invasiveness of thymoma. J Thorac Oncol 6(7):1274–1281

    Article  PubMed  Google Scholar 

  21. Marom EM (2013) Advances in thymoma imaging. J Thorac Imaging 28(2):69–80

    Article  PubMed  Google Scholar 

  22. Zerhouni EA, Scott WW Jr, Baker RR, Wharam MD, Siegelman SS (1982) Invasive thymomas: diagnosis and evaluation by computed tomography. J Comput Assist Tomogr 6(1):92–100

    Article  CAS  PubMed  Google Scholar 

  23. Shen Y, Ye J, Fang W et al (2017) Efficacy of computed tomography features in predicting stage III thymic tumors. Oncol Lett 13(1):29–36

    Article  PubMed  Google Scholar 

  24. Detterbeck FC, Nicholson AG, Kondo K, Van Schil P, Moran C (2011) The Masaoka-Koga stage classification for thymic malignancies: clarification and definition of terms. J Thorac Oncol 6(7 Suppl 3):S1710–S1716

    Article  PubMed  Google Scholar 

  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(5):799–805

    Article  PubMed  Google Scholar 

  26. 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(2):283–289

    Article  PubMed  Google Scholar 

  27. Kim DJ, Yang WI, Choi SS, Kim KD, Chung KY (2005) Prognostic and clinical relevance of the World Health Organization schema for the classification of thymic epithelial tumors: a clinicopathologic study of 108 patients and literature review. Chest 127(3):755–761

    Article  PubMed  Google Scholar 

  28. 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(4):881–886

    Article  PubMed  Google Scholar 

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Funding

This study received no funding.

Author information

Authors and Affiliations

  1. Department of Radiology, Weill Cornell Medicine, New York-Presbyterian Hospital, 525 E. 68th St, Box 141, New York, NY, 10065, USA

    Daniel B. Green, Sarah Eliades, Alan C. Legasto & James F. Gruden

  2. Department of Healthcare Policy & Research, Division of Biostatistics and Epidemiology, Weill Cornell Medicine, 402 E. 67th St, New York, NY, 10065, USA

    Gulce Askin

  3. Department of Cardiothoracic Surgery, Weill Cornell Medicine, New York-Presbyterian Hospital, 525 East 68th Street M-404, New York, NY, 10065, USA

    Jeffrey L. Port

Authors
  1. Daniel B. Green
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  2. Sarah Eliades
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  3. Alan C. Legasto
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  4. Gulce Askin
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  5. Jeffrey L. Port
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  6. James F. Gruden
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Corresponding author

Correspondence to Daniel B. Green.

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Guarantor

The scientific guarantor of this publication is Daniel Green, MD.

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

Gulce Askin, MPH, kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was not required for this study because it was a retrospective review of medical records.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• observational

• performed at one institution

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Green, D.B., Eliades, S., Legasto, A.C. et al. Multilobulated thymoma with an acute angle: a new predictor of lung invasion. Eur Radiol 29, 4555–4562 (2019). https://doi.org/10.1007/s00330-019-06059-1

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  • DOI: https://doi.org/10.1007/s00330-019-06059-1

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