Advertisement

European Radiology

, Volume 23, Issue 12, pp 3450–3455 | Cite as

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

  • Marco Ravanelli
  • Davide Farina
  • Mauro Morassi
  • Elisa Roca
  • Giuseppe Cavalleri
  • Gianfranco Tassi
  • Roberto Maroldi
Oncology

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.

Keywords

Lung cancer Computed tomography Texture analysis Chemotherapy Multivariate analysis 

References

  1. 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 59:828–836CrossRefPubMedGoogle Scholar
  2. 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 22:1966–1974CrossRefPubMedGoogle Scholar
  3. 3.
    Gadgeel SM, Cote ML, Schwartz AG et al (2010) Parameters for individualizing systemic therapy in non-small cell lung cancer. Drug Resist Updat 13:196–204CrossRefPubMedGoogle Scholar
  4. 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 58:776–784CrossRefPubMedGoogle Scholar
  5. 5.
    Caroli P, Nanni C, Rubello D et al (2010) Non-FDG PET in the practice of oncology. Indian J Cancer 47:120–125CrossRefPubMedGoogle Scholar
  6. 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 95:835–842CrossRefPubMedGoogle Scholar
  7. 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 61:44–53CrossRefPubMedGoogle Scholar
  8. 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 233:191–199CrossRefPubMedGoogle Scholar
  9. 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 264:581–589CrossRefPubMedGoogle Scholar
  10. 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 45:228–247CrossRefPubMedGoogle Scholar
  11. 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 250:444–452CrossRefGoogle Scholar
  12. 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 67:157–164CrossRefPubMedGoogle Scholar
  13. 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 22:796–802CrossRefPubMedGoogle Scholar
  14. 14.
    Ganeshan B, Goh V, Mandeville HC et al (2013) Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 266:326–336CrossRefPubMedGoogle Scholar
  15. 15.
    Padhani AR (2003) MRI for assessing antivascular cancer treatments. Br J Radiol 76:S60–S80CrossRefPubMedGoogle Scholar
  16. 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 82:342–348CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2013

Authors and Affiliations

  • Marco Ravanelli
    • 1
    • 4
  • Davide Farina
    • 1
  • Mauro Morassi
    • 1
  • Elisa Roca
    • 2
  • Giuseppe Cavalleri
    • 3
  • Gianfranco Tassi
    • 2
  • Roberto Maroldi
    • 1
  1. 1.Department of RadiologyUniversity of BresciaBresciaItaly
  2. 2.Department of PneumologyUniversity of BresciaBresciaItaly
  3. 3.Department of Electronic EngineeringUniversity of BresciaBresciaItaly
  4. 4.BresciaItaly

Personalised recommendations