Advertisement

European Radiology

, Volume 29, Issue 11, pp 6069–6079 | Cite as

Clinical T categorization in stage IA lung adenocarcinomas: prognostic implications of CT display window settings for solid portion measurement

  • Hyungjin Kim
  • Jin Mo Goo
  • Young Tae Kim
  • Chang Min ParkEmail author
Chest
  • 118 Downloads

Abstract

Objectives

Our study aimed at evaluating the prognostic implications of lung and mediastinal CT display window settings for solid portion measurements on the eighth-edition lung cancer staging system’s clinical T (cT) categorization.

Methods

We retrospectively analyzed 691 surgically treated patients from 2009 to 2015 for clinical stage IA lung adenocarcinomas. Solid portions were measured at the lung and mediastinal window settings, respectively, and cT categories were determined for each measurement (cTlung and cTmediastinum). The prognostic power of the two cT factors for disease-free survival (DFS) was assessed using Cox regression, and concordance indices (C-indices) were compared using the Student t test. Subsequently, the patients were split into training and validation cohorts to calculate optimal cutoffs for the cT categorization of mediastinal window–based solid portions (cToptimal) and validate its prognostic performance.

Results

Both cTlung ((cT1b: adjusted HR, 3.547; p = 0.017), (cT1c: adjusted HR, 9.439; p < 0.001)) and cTmediastinum ((cT1b: adjusted HR, 4.635; p < 0.001), (cT1c: adjusted HR, 11.235; p < 0.001)) were significantly associated with DFS for each multivariable Cox model. The C-indices were 0.772 (95% CI, 0.702–0.842) for cTlung and 0.787 (95% CI, 0.726–0.848) for cTmediastinum (p = 0.789). The optimal cutoffs for cT categorization of the mediastinal window–based solid portions were 0.9 cm and 1.8 cm. However, there were no significant differences in the C-indices among cTlung, cTmediastinum, and cToptimal (p > 0.05).

Conclusions

The prognostic performances of the cT categorizations at the lung and mediastinal windows were not significantly different. The current cT categorization based on the lung window measurement is appropriate as it stands.

Key Points

• Discriminatory power of the eighth-edition clinical T category was not significantly affected by the CT display window settings.

• Given the facts that the lung window setting enables more sensitive detection of the solid portions and higher correlation with the pathological invasive components, our findings may support adherence to the usage of the lung window setting for the solid portion measurement per the current recommendations.

Keywords

Non–small cell lung carcinoma Adenocarcinoma Multidetector computed tomography Neoplasm staging Disease-free survival 

Abbreviations

AIC

Akaike’s information criterion

C-index

Concordance index

cTlung

Clinical T categorization based on solid portion measurement with the lung window setting using the eighth-edition T coding system

cTmediastinum

Clinical T categorization based on solid portion measurement with the mediastinal window setting using the eighth-edition T coding system

cToptimal

Clinical T categorization for the mediastinal window–based solid portion using optimal cutoffs

DFS

Disease-free survival

EMR

Electronic medical record

IQR

Interquartile range

TDR

Tumor disappearance ratio

Notes

Acknowledgements

We sincerely express our gratitude to Myunghee Lee and Ju Young Jeong for their help in data acquisition.

Funding

This study was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (grant number: 2017R1A2B4008517).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Chang Min Park.

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

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in a journal article (Kim et al; in press).

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

330_2019_6216_MOESM1_ESM.docx (15 kb)
ESM 1 (DOCX 15 kb)

References

  1. 1.
    Cheng TY, Cramb SM, Baade PD, Youlden DR, Nwogu C, Reid ME (2016) The international epidemiology of lung cancer: latest trends, disparities, and tumor characteristics. J Thorac Oncol 11:1653–1671CrossRefGoogle Scholar
  2. 2.
    Austin JH, Garg K, Aberle D et al (2013) Radiologic implications of the 2011 classification of adenocarcinoma of the lung. Radiology 266:62–71CrossRefGoogle Scholar
  3. 3.
    Lee KH, Goo JM, Park SJ et al (2014) Correlation between the size of the solid component on thin-section CT and the invasive component on pathology in small lung adenocarcinomas manifesting as ground-glass nodules. J Thorac Oncol 9:74–82CrossRefGoogle Scholar
  4. 4.
    Hwang EJ, Park CM, Ryu Y et al (2015) Pulmonary adenocarcinomas appearing as part-solid ground-glass nodules: is measuring solid component size a better prognostic indicator? Eur Radiol 25:558–567CrossRefGoogle Scholar
  5. 5.
    Tsutani Y, Miyata Y, Nakayama H et al (2013) Solid tumor size on high-resolution computed tomography and maximum standardized uptake on positron emission tomography for new clinical T descriptors with T1 lung adenocarcinoma. Ann Oncol 24:2376–2381CrossRefGoogle Scholar
  6. 6.
    Burt BM, Leung AN, Yanagawa M et al (2015) Diameter of solid tumor component alone should be used to establish T stage in lung adenocarcinoma. Ann Surg Oncol 22(Suppl 3):S1318–S1323CrossRefGoogle Scholar
  7. 7.
    Murakawa T, Konoeda C, Ito T et al (2013) The ground glass opacity component can be eliminated from the T-factor assessment of lung adenocarcinoma. Eur J Cardiothorac Surg 43:925–932CrossRefGoogle Scholar
  8. 8.
    Travis WD, Asamura H, Bankier AA et al (2016) The IASLC lung cancer staging project: proposals for coding T categories for subsolid nodules and assessment of tumor size in part-solid tumors in the forthcoming eighth edition of the TNM classification of lung cancer. J Thorac Oncol 11:1204–1223CrossRefGoogle Scholar
  9. 9.
    Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP (2017) Recommendations for measuring pulmonary nodules at CT: a statement from the Fleischner Society. Radiology 285:584–600CrossRefGoogle Scholar
  10. 10.
    Naidich DP, Bankier AA, MacMahon H et al (2013) Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology 266:304–317CrossRefGoogle Scholar
  11. 11.
    Revel MP, Mannes I, Benzakoun J et al (2018) Subsolid lung nodule classification: a CT criterion for improving interobserver agreement. Radiology 286:316–325CrossRefGoogle Scholar
  12. 12.
    Ahn H, Lee KW, Lee KH et al (2018) Effect of computed tomography window settings and reconstruction plane on 8th edition T-stage classification in patients with lung adenocarcinoma manifesting as a subsolid nodule. Eur J Radiol 98:130–135CrossRefGoogle Scholar
  13. 13.
    Yoo RE, Goo JM, Hwang EJ et al (2017) Retrospective assessment of interobserver agreement and accuracy in classifications and measurements in subsolid nodules with solid components less than 8mm: which window setting is better? Eur Radiol 27:1369–1376CrossRefGoogle Scholar
  14. 14.
    Kim H, Goo JM, Kim YT, Park CM (2019) Clinical T category of non-small cell lung cancers: prognostic performance of unidimensional versus bidimensional measurements at CT. Radiology.  https://doi.org/10.1148/radiol.2019182068:182068
  15. 15.
    Xie W, Regan MM, Buyse M et al (2017) Metastasis-free survival is a strong surrogate of overall survival in localized prostate cancer. J Clin Oncol 35:3097–3104CrossRefGoogle Scholar
  16. 16.
    Travis WD, Brambilla E, Noguchi M et al (2011) International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 6:244–285CrossRefGoogle Scholar
  17. 17.
    Hunt KK, Karakas C, Ha MJ et al (2017) Cytoplasmic cyclin E predicts recurrence in patients with breast cancer. Clin Cancer Res 23:2991–3002CrossRefGoogle Scholar
  18. 18.
    Harrell FE (2015) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  19. 19.
    Weiss A, Chavez-MacGregor M, Lichtensztajn DY et al (2018) Validation study of the American Joint Committee on Cancer eighth edition prognostic stage compared with the anatomic stage in breast cancer. JAMA Oncol 4:203–209CrossRefGoogle Scholar
  20. 20.
    Chang C, Hsieh MK, Chang WY, Chiang AJ, Chen J (2017) Determining the optimal number and location of cutoff points with application to data of cervical cancer. PLoS One 12:e0176231CrossRefGoogle Scholar
  21. 21.
    Haraguchi N, Satoh H, Kikuchi N, Kagohashi K, Ishikawa H, Ohtsuka M (2007) Prognostic value of tumor disappearance rate on computed tomography in advanced-stage lung adenocarcinoma. Clin Lung Cancer 8:327–330CrossRefGoogle Scholar
  22. 22.
    Kim D, Kim HK, Kim SH et al (2018) Prognostic significance of histologic classification and tumor disappearance rate by computed tomography in lung cancer. J Thorac Dis 10:388–397CrossRefGoogle Scholar
  23. 23.
    Okada M, Nishio W, Sakamoto T et al (2004) Correlation between computed tomographic findings, bronchioloalveolar carcinoma component, and biologic behavior of small-sized lung adenocarcinomas. J Thorac Cardiovasc Surg 127:857–861CrossRefGoogle Scholar
  24. 24.
    Shimizu K, Yamada K, Saito H et al (2005) Surgically curable peripheral lung carcinoma: correlation of thin-section CT findings with histologic prognostic factors and survival. Chest 127:871–878CrossRefGoogle Scholar
  25. 25.
    Yoshida J, Ishii G, Hishida T et al (2015) Limited resection trial for pulmonary ground-glass opacity nodules: case selection based on high-resolution computed tomography-interim results. Jpn J Clin Oncol 45:677–681CrossRefGoogle Scholar
  26. 26.
    Chen PA, Huang EP, Shih LY et al (2018) Qualitative CT criterion for subsolid nodule subclassification: improving interobserver agreement and pathologic correlation in the adenocarcinoma spectrum. Acad Radiol 25:1439–1445CrossRefGoogle Scholar
  27. 27.
    Arenas-Jimenez J (2013) Measurement of solid component in part-solid lesions with a mediastinal window setting? Radiology 268:305–306CrossRefGoogle Scholar
  28. 28.
    MacMahon H, Naidich DP, Goo JM et al (2017) Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 284:228–243CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologySeoul National University College of MedicineSeoulSouth Korea
  2. 2.Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulSouth Korea
  3. 3.Cancer Research InstituteSeoul National UniversitySeoulSouth Korea
  4. 4.Department of Thoracic and Cardiovascular SurgerySeoul National University College of MedicineSeoulSouth Korea

Personalised recommendations