Skip to main content

Advertisement

SpringerLink
Log in

Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy

  • Oncology
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To assess whether tumour heterogeneity, quantified by texture analysis (TA) on contrast-enhanced computed tomography (CECT), can predict response to chemotherapy in advanced non-small cell lung cancer (NSCLC).

Methods

Fifty-three CECT studies of patients with advanced NSCLC who had undergone first-line chemotherapy were retrospectively reviewed. Response to chemotherapy was evaluated according to RECIST1.1. Tumour uniformity was assessed by a TA method based on Laplacian of Gaussian filtering. The resulting parameters were correlated with treatment response and overall survival by multivariate analysis.

Results

Thirty-one out of 53 patients were non-responders and 22 were responders. Average overall survival was 13 months (4–35), minimum follow-up was 12 months. In the adenocarcinoma group (n = 31), the product of tumour uniformity and grey level (GL*U) was the unique independent variable correlating with treatment response. Dividing the GL*U (range 8.5-46.6) into tertiles, lesions belonging to the second and the third tertiles had an 8.3-fold higher probability of treatment response compared with those in the first tertile. No association between texture features and response to treatment was observed in the non-adenocarcinoma group (n = 22). GL*U did not correlate with overall survival.

Conclusions

TA on CECT images in advanced lung adenocarcinoma provides an independent predictive indicator of response to first-line chemotherapy.

Key Points

• Contrast enhanced computed tomography is currently used to stage lung cancer.

• Texture analysis allows tumour heterogeneity to be quantified on CT images.

• Texture parameters seem to predict chemotherapy response in advanced NSCLC.

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.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Spiro SG, Rudd RM, Souhami RL et al (2004) Chemotherapy versus supportive care in advanced non-small cell lung cancer: improved survival without detriment to quality of life. Thorax 59:828–836

    Article  CAS  PubMed  Google Scholar 

  2. Shanafelt TD, Loprinzi C, Marks R et al (2004) Are chemotherapy response rates related to treatment-induced survival prolongations in patients with advanced cancer? J Clin Oncol 22:1966–1974

    Article  PubMed  Google Scholar 

  3. Gadgeel SM, Cote ML, Schwartz AG et al (2010) Parameters for individualizing systemic therapy in non-small cell lung cancer. Drug Resist Updat 13:196–204

    Article  CAS  PubMed  Google Scholar 

  4. Song X, Liu X, Chi W et al (2006) Hypoxia-induced resistance to cisplatin and doxorubicin in non-small cell lung cancer is inhibited by silencing of HIF-1 gene. Cancer Chemother Pharmacol 58:776–784

    Article  CAS  PubMed  Google Scholar 

  5. Caroli P, Nanni C, Rubello D et al (2010) Non-FDG PET in the practice of oncology. Indian J Cancer 47:120–125

    Article  CAS  PubMed  Google Scholar 

  6. Tateishi U, Kusumoto M, Nishihara H et al (2002) Contrast-enhanced dynamic computed tomography for the evaluation of tumor angiogenesis in patients with lung carcinoma. Cancer 95:835–842

    Article  PubMed  Google Scholar 

  7. Li Y, Yang ZG, Chen TW et al (2008) Peripheral lung carcinoma: correlation of angiogenesis and first-pass perfusion parameters of 64-detector row CT. Lung Cancer 61:44–53

    Article  PubMed  Google Scholar 

  8. 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 233:191–199

    Article  PubMed  Google Scholar 

  9. Mandeville HC, Ng QS, Daley FM et al (2012) Operable non-small cell lung cancer: correlation of volumetric helical dynamic contrast-enhanced CT parameters with immunohistochemical markers of tumour hypoxia. Radiology 264:581–589

    Article  PubMed  Google Scholar 

  10. Eisenhauer EA, Therasse P, Bogaerts J et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45:228–247

    Article  CAS  PubMed  Google Scholar 

  11. Miles KA, Ganeshan B, Griffiths MR et al (2012) Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology 250:444–452

    Article  Google Scholar 

  12. Ganeshan B, Skogen K, Pressney I et al (2012) Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. Clin Radiol 67:157–164

    Article  CAS  PubMed  Google Scholar 

  13. Ganeshan B, Panayiotou E, Burnand K et al (2012) Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival. Eur Radiol 22:796–802

    Article  PubMed  Google Scholar 

  14. Ganeshan B, Goh V, Mandeville HC et al (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336

    Article  PubMed  Google Scholar 

  15. Padhani AR (2003) MRI for assessing antivascular cancer treatments. Br J Radiol 76:S60–S80

    Article  PubMed  Google Scholar 

  16. Ng F, Kozarski R, Ganeshan B et al (2013) Assessment of tumour heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumour analysis? Eur J Radiol 82:342–348

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Radiology, University of Brescia, Brescia, Italy

    Marco Ravanelli, Davide Farina, Mauro Morassi & Roberto Maroldi

  2. Department of Pneumology, University of Brescia, Brescia, Italy

    Elisa Roca & Gianfranco Tassi

  3. Department of Electronic Engineering, University of Brescia, Brescia, Italy

    Giuseppe Cavalleri

  4. Piazzale Spedali Civili 1, 25123, Brescia, Italy

    Marco Ravanelli

Authors
  1. Marco Ravanelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davide Farina
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mauro Morassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elisa Roca
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Giuseppe Cavalleri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gianfranco Tassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Roberto Maroldi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Ravanelli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ravanelli, M., Farina, D., Morassi, M. et al. Texture analysis of advanced non-small cell lung cancer (NSCLC) on contrast-enhanced computed tomography: prediction of the response to the first-line chemotherapy. Eur Radiol 23, 3450–3455 (2013). https://doi.org/10.1007/s00330-013-2965-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-013-2965-0

Keywords

Search

Navigation