Advertisement

European Radiology

, Volume 29, Issue 2, pp 915–923 | Cite as

Prognostic value of radiomic analysis of iodine overlay maps from dual-energy computed tomography in patients with resectable lung cancer

  • Jooae Choe
  • Sang Min LeeEmail author
  • Kyung-Hyun Do
  • Jung Bok Lee
  • Sang Min Lee
  • June-Goo Lee
  • Joon Beom Seo
Chest
  • 303 Downloads

Abstract

Objectives

To investigate whether radiomics on iodine overlay maps from dual-energy computed tomography (DECT) can predict survival outcomes in patients with resectable lung cancer.

Methods

Ninety-three lung cancer patients eligible for curative surgery were examined with DECT at the time of diagnosis. The median follow-up was 60.4 months. Radiomic features of the entire primary tumour were extracted from iodine overlay maps generated by DECT. A Cox proportional hazards regression model was used to determine independent predictors of overall survival (OS) and disease-free survival (DFS), respectively.

Results

Forty-two patients (45.2%) had disease recurrence and 39 patients (41.9%) died during the follow-up period. The mean DFS was 49.8 months and OS was 55.2 months. Univariate analysis revealed that significant predictors of both OS and DFS were stage and radiomic parameters, including histogram energy, histogram entropy, grey-level co-occurrence matrix (GLCM) angular second moment, GLCM entropy and homogeneity. The multivariate analysis identified stage and entropy as independent risk factors predicting both OS (stage, hazard ratio (HR) = 2.020 [95% CI 1.014–4.026], p = 0.046; entropy, HR = 1.543 [95% CI 1.069–2.228], p = 0.021) and DFS (stage, HR = 2.132 [95% CI 1.060–4.287], p = 0.034; entropy, HR = 1.497 [95% CI 1.031–2.173], p = 0.034). The C-index showed that adding entropy improved prediction of OS compared to stage only (0.720 and 0.667, respectively; p = 0.048).

Conclusions

Radiomic features extracted from iodine overlay map reflecting heterogeneity of tumour perfusion can add prognostic information for patients with resectable lung cancer.

Key Points

• Radiomic feature (histogram entropy) from DECT iodine overlay maps was an independent risk factor predicting both overall survival and disease-free survival.

• Adding histogram entropy to clinical stage improved prediction of overall survival compared to stage only (0.720 and 0.667, respectively; p = 0.048).

• DECT can be a good option for comprehensive pre-operative evaluation in cases of resectable lung cancer.

Keywords

Lung neoplasms Prognosis Multidetector computed tomography Diagnostic imaging 

Abbreviations

ASM

Angular second moment

CT

Computed tomography

DECT

Dual-energy computed tomography

GLCM

Grey level co-occurrence matrix

GRAD

Absolute gradient

HIST

Histogram analysis

ICC

Interclass correlation coefficient.

OS

Overall survival

PET

Positron emission tomography

RL

Run-length encoding

ROC

Receiver operating characteristic

SUVmax

Maximal standard uptake values

Notes

Funding

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number: NRF-2016R1A2B1016355).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Dr. Joon Beom Seo.

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

One of the authors (Jung Bok Lee, PhD) has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Prognostic study

• Performed at one institution

References

  1. 1.
    Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67:7–30CrossRefGoogle Scholar
  2. 2.
    Mitsudomi T, Suda K, Yatabe Y (2013) Surgery for NSCLC in the era of personalized medicine. Nat Rev Clin Oncol 10:235–244CrossRefGoogle Scholar
  3. 3.
    al-Kattan K, Sepsas E, Fountain SW, Townsend ER (1997) Disease recurrence after resection for stage I lung cancer. Eur J Cardiothorac Surg 12:380–384CrossRefGoogle Scholar
  4. 4.
    Uramoto H, Nakanishi R, Nagashima A et al (2010) A randomized phase II trial of adjuvant chemotherapy with bi-weekly carboplatin plus paclitaxel versus carboplatin plus gemcitabine in patients with completely resected non-small cell lung cancer. Anticancer Res 30:4695–4699Google Scholar
  5. 5.
    Gerlinger M, Rowan AJ, Horswell S et al (2012) Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883–892CrossRefGoogle Scholar
  6. 6.
    Nitadori J-i, Bograd AJ, Kadota K et al (2013) Impact of micropapillary histologic subtype in selecting limited resection vs lobectomy for lung adenocarcinoma of 2cm or Smaller. J Natl Cancer Inst 105:1212–1220CrossRefGoogle Scholar
  7. 7.
    Patnaik SK, Kannisto E, Knudsen S, Yendamuri S (2010) Evaluation of microRNA expression profiles that may predict recurrence of localized stage I non-small cell lung cancer after surgical resection. Cancer Res 70:36–45CrossRefGoogle Scholar
  8. 8.
    Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefGoogle Scholar
  9. 9.
    Straume O, Chappuis PO, Salvesen HB et al (2002) Prognostic importance of glomeruloid microvascular proliferation indicates an aggressive angiogenic phenotype in human cancers. Cancer Res 62:6808–6811Google Scholar
  10. 10.
    Maeda R, Ishii G, Ito M et al (2012) Number of circulating endothelial progenitor cells and intratumoral microvessel density in non-small cell lung cancer patients: differences in angiogenic status between adenocarcinoma histologic subtypes. J Thorac Oncol 7:503–511CrossRefGoogle Scholar
  11. 11.
    Zhao YY, Xue C, Jiang W et al (2012) Predictive value of intratumoral microvascular density in patients with advanced non-small cell lung cancer receiving chemotherapy plus bevacizumab. J Thorac Oncol 7:71–75CrossRefGoogle Scholar
  12. 12.
    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 26:43–54CrossRefGoogle Scholar
  13. 13.
    Kim YN, Lee HY, Lee KS et al (2012) Dual-Energy CT in Patients Treated with Anti-Angiogenic Agents for Non-Small Cell Lung Cancer: New Method of Monitoring Tumor Response? Korean J Radiol 13:702–710CrossRefGoogle Scholar
  14. 14.
    Bae JM, Jeong JY, Lee HY et al (2017) Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images. Oncotarget 8:523–535Google Scholar
  15. 15.
    Sobin LH, Gospodarowicz MKCW (2009) UICC International Union Against Cancer. TNM Classification of Malignant Tumours. Lung and pleural tumours. Wiley-Blackwell, Oxford England, pp 138–146Google Scholar
  16. 16.
    Chae EJ, Song J-W, Seo JB, Krauss B, Jang YM, Song K-S (2008) Clinical Utility of Dual-Energy CT in the Evaluation of Solitary Pulmonary Nodules: Initial Experience. Radiology 249:671–681CrossRefGoogle Scholar
  17. 17.
    Chae EJ, Kim N, Seo JB et al (2013) Prediction of Postoperative Lung Function in Patients Undergoing Lung Resection: Dual-Energy Perfusion Computed Tomography Versus Perfusion Scintigraphy. Investigative Radiology 48:622–627CrossRefGoogle Scholar
  18. 18.
    Wu K, Garnier C, Coatrieux J-L, Shu H (2010) A preliminary study of moment-based texture analysis for medical images. Conf Proc IEEE Eng Med Biol Soc 2010:5581–5584PubMedPubMedCentralGoogle Scholar
  19. 19.
    Soh L., C. T (1999) Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37:780–795Google Scholar
  20. 20.
    Eo S, Kang HJ, Hong S, Cho HJ (2014) K-Adaptive Partitioning for Survival Data, with an Application to Cancer StagingGoogle Scholar
  21. 21.
    Portney LG, M.P. W (2000) Foundations of clinical research: applications to practice, 3rd edn. Prentice Hall, New JerseyGoogle Scholar
  22. 22.
    Yoon SH, Park CM, Park SJ, Yoon JH, Hahn S, Goo JM (2016) Tumor Heterogeneity in Lung Cancer: Assessment with Dynamic Contrast-enhanced MR Imaging. Radiology 280:940–948CrossRefGoogle Scholar
  23. 23.
    Hayano K, Kulkarni NM, Duda DG, Heist RS, Sahani DV (2016) Exploration of Imaging Biomarkers for Predicting Survival of Patients With Advanced Non-Small Cell Lung Cancer Treated With Antiangiogenic Chemotherapy. AJR Am J Roentgenol 206:987–993CrossRefGoogle Scholar
  24. 24.
    Huang Y, Liu Z, He L et al (2016) Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer. Radiology 281:947–957CrossRefGoogle Scholar
  25. 25.
    Johnson TR, Krauss B, Sedlmair M et al (2007) Material differentiation by dual energy CT: initial experience. Eur Radiol 17:1510–1517CrossRefGoogle Scholar
  26. 26.
    Swinson DE, Jones JL, Richardson D, Cox G, Edwards JG, O'Byrne KJ (2002) Tumour necrosis is an independent prognostic marker in non-small cell lung cancer: correlation with biological variables. Lung Cancer 37:235–240CrossRefGoogle Scholar
  27. 27.
    Park SY, Lee HS, Jang HJ, Lee GK, Chung KY, Zo JI (2011) Tumor necrosis as a prognostic factor for stage IA non-small cell lung cancer. Ann Thorac Surg 91:1668–1673CrossRefGoogle Scholar
  28. 28.
    Kilicgun A, Turna A, Sayar A, Solak O, Urer N, Gurses A (2010) Very important histopathological factors in patients with resected non-small cell lung cancer: necrosis and perineural invasion. Thorac Cardiovasc Surg 58:93–97CrossRefGoogle Scholar
  29. 29.
    Kang M-J, Park CM, Lee C-H, Goo JM, Lee HJ (2010) Dual-Energy CT: Clinical Applications in Various Pulmonary Diseases. Radiographics 30:685–698CrossRefGoogle Scholar
  30. 30.
    Parmar C, Rios Velazquez E, Leijenaar R et al (2014) Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9:e102107CrossRefGoogle Scholar
  31. 31.
    Velazquez ER, Parmar C, Jermoumi M et al (2013) Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep 3:3529CrossRefGoogle Scholar
  32. 32.
    Liu J, Dong M, Sun X, Li W, Xing L, Yu J (2016) Prognostic Value of 18F-FDG PET/CT in Surgical Non-Small Cell Lung Cancer: A Meta-Analysis. PLoS One 11:e0146195CrossRefGoogle Scholar
  33. 33.
    Satoh Y, Onishi H, Nambu A, Araki T (2014) Volume-based parameters measured by using FDG PET/CT in patients with stage I NSCLC treated with stereotactic body radiation therapy: prognostic value. Radiology 270:275–281CrossRefGoogle Scholar
  34. 34.
    Kumar V, Nath K, Berman CG et al (2013) Variance of Standardised Uptake Values for FDG-PET/CT Greater in Clinical Practice than Under Ideal Study Settings. Clin Nucl Med 38:175–182CrossRefGoogle Scholar
  35. 35.
    Nahmias C, Wahl LM (2008) Reproducibility of standardised uptake value measurements determined by 18F-FDG PET in malignant tumors. J Nucl Med 49:1804–1808CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Department of Radiology and Research Institute of RadiologyUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea
  2. 2.Department of Medical StatisticsAsan Medical Center, University of Ulsan College of MedicineSeoulKorea
  3. 3.Department of Convergence MedicineUniversity of Ulsan College of Medicine, Asan Medical CenterSeoulKorea

Personalised recommendations