Skip to main content

Advertisement

Log in

Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

The question as to whether the regional textural features extracted from PET images predict prognosis in oropharyngeal squamous cell carcinoma (OPSCC) remains open. In this study, we investigated the prognostic impact of regional heterogeneity in patients with T3/T4 OPSCC.

Methods

We retrospectively reviewed the records of 88 patients with T3 or T4 OPSCC who had completed primary therapy. Progression-free survival (PFS) and disease-specific survival (DSS) were the main outcome measures. In an exploratory analysis, a standardized uptake value of 2.5 (SUV 2.5) was taken as the cut-off value for the detection of tumour boundaries. A fixed threshold at 42 % of the maximum SUV (SUVmax 42 %) and an adaptive threshold method were then used for validation. Regional textural features were extracted from pretreatment 18F-FDG PET/CT images using the grey-level run length encoding method and grey-level size zone matrix. The prognostic significance of PET textural features was examined using receiver operating characteristic (ROC) curves and Cox regression analysis.

Results

Zone-size nonuniformity (ZSNU) was identified as an independent predictor of PFS and DSS. Its prognostic impact was confirmed using both the SUVmax 42 % and the adaptive threshold segmentation methods. Based on (1) total lesion glycolysis, (2) uniformity (a local scale texture parameter), and (3) ZSNU, we devised a prognostic stratification system that allowed the identification of four distinct risk groups. The model combining the three prognostic parameters showed a higher predictive value than each variable alone.

Conclusion

ZSNU is an independent predictor of outcome in patients with advanced T-stage OPSCC, and may improve their prognostic stratification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM. Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010;127:2893–917.

    Article  CAS  PubMed  Google Scholar 

  2. Applebaum KM, Furniss CS, Zeka A, Posner MR, Smith JF, Bryan J, et al. Lack of association of alcohol and tobacco with HPV16-associated head and neck cancer. J Natl Cancer Inst. 2007;99:1801–10.

    Article  PubMed  Google Scholar 

  3. Bouvard V, Baan RSK, Grosse Y, Secretan B, El Ghissassi F, Benbrahim-Tallaa L, et al. A review of human carcinogens - Part B: biological agents. Lancet Oncol. 2009;10:321–2.

    Article  PubMed  Google Scholar 

  4. Ang KK, Harris J, Wheeler R, Weber R, Rosenthal DI, Nguyen-Tân PF, et al. Human papillomavirus and survival of patients with oropharyngeal cancer. N Engl J Med. 2010;363:24–35.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  5. Fakhry C, Westra WH, Li S, Cmelak A, Ridge JA, Pinto H, et al. Improved survival of patients with human papillomavirus-positive head and neck squamous cell carcinoma in a prospective clinical trial. J Natl Cancer Inst. 2008;100:261–9.

    Article  CAS  PubMed  Google Scholar 

  6. Sedaghat AR, Zhang Z, Begum S, Palermo R, Best S, Ulmer KM, et al. Prognostic significance of human papillomavirus in oropharyngeal squamous cell carcinomas. Laryngoscope. 2009;119:1542–9.

    Article  PubMed  Google Scholar 

  7. Wu Y, Posner MR, Schumaker LM, Nikitakis N, Goloubeva O, Tan M, et al. Novel biomarker panel predicts prognosis in human papillomavirus-negative oropharyngeal cancer: an analysis of the TAX 324 trial. Cancer. 2011;118:1811–7.

    Article  PubMed  Google Scholar 

  8. Granata R, Miceli R, Orlandi E, Perrone F, Cortelazzi B, Franceschini M, et al. Tumor stage, human papillomavirus and smoking status affect the survival of patients with oropharyngeal cancer: an Italian validation study. Ann Oncol. 2012;23:1832–7.

    Article  CAS  PubMed  Google Scholar 

  9. Cheng NM, Chang JT, Huang CG, Tsan DL, Ng SH, Wang HM, et al. Prognostic value of pretreatment (18)F-FDG PET/CT and human papillomavirus type 16 testing in locally advanced oropharyngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging. 2012;39:1673–84.

    Article  PubMed  Google Scholar 

  10. Dibble EH, Alvarez AC, Truong MT, Mercier G, Cook EF, Subramaniam RM. 18F-FDG metabolic tumor volume and total glycolytic activity of oral cavity and oropharyngeal squamous cell cancer: adding value to clinical staging. J Nucl Med. 2012;53:709–15.

    Article  CAS  PubMed  Google Scholar 

  11. Lim R, Eaton A, Lee NY, Setton J, Ohri N, Rao S, et al. 18F-FDG PET/CT metabolic tumor volume and total lesion glycolysis predict outcome in oropharyngeal squamous cell carcinoma. J Nucl Med. 2012;53:1506–13.

    Article  CAS  PubMed  Google Scholar 

  12. Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges JP, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369–78.

    Article  PubMed Central  PubMed  Google Scholar 

  13. Cheng NM, Fang YH, Chang JT, Huang CG, Tsan DL, Ng SH, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54:1703–9.

    Article  CAS  PubMed  Google Scholar 

  14. Cook GJ, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26.

    Article  PubMed  Google Scholar 

  15. Hatt M, Tixier F, Cheze Le Rest C, Pradier O, Visvikis D. Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma. Eur J Nucl Med Mol Imaging. 2013;40:1662–71.

    Article  PubMed  Google Scholar 

  16. Yang F, Thomas MA, Dehdashti F, Grigsby PW. Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging. 2013;40:716–27.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. O’Sullivan B, Shah J. New TNM staging criteria for head and neck tumors. Semin Surg Oncol. 2003;21:30–42.

    Article  PubMed  Google Scholar 

  18. Therasse P, Arbuck SG, Eisenhauer EA, Wanders J, Kaplan RS, Rubinstein L, et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst. 2000;92:205–16.

    Article  CAS  PubMed  Google Scholar 

  19. Huang SL, Chao A, Hsueh S, Chao FY, Huang CC, Yang JE, et al. Comparison between the hybrid capture II test and an SPF1/GP6+ PCR-based assay for detection of human papillomavirus DNA in cervical swab samples. J Clin Microbiol. 2006;44:1733–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Lin CY, Chen HC, Lin RW, You SL, You CM, Chuang LC, et al. Quality assurance of genotyping array for detection and typing of human papillomavirus. J Virol Methods. 2007;140:1–9.

    Article  CAS  PubMed  Google Scholar 

  21. Luo CW, Roan CH, Liu CJ. Human papillomaviruses in oral squamous cell carcinoma and pre-cancerous lesions detected by PCR-based gene-chip array. Int J Oral Maxillofac Surg. 2007;36:153–8.

    Article  PubMed  Google Scholar 

  22. Al-Swiahb JN, Huang CC, Fang FM, Chuang HC, Huang HY, Luo SD, et al. Prognostic impact of p16, p53, epidermal growth factor receptor, and human papillomavirus in oropharyngeal cancer in a betel nut-chewing area. Arch Otolaryngol Head Neck Surg. 2010;136:502–8.

    Article  PubMed  Google Scholar 

  23. Hatt M, Cheze-le Rest C, van Baardwijk A, Lambin P, Pradier O, Visvikis D. Impact of tumor size and tracer uptake heterogeneity in (18)F-FDG PET and CT non-small cell lung cancer tumor delineation. J Nucl Med. 2011;52:1690–7.

    Article  PubMed Central  PubMed  Google Scholar 

  24. La TH, Filion EJ, Turnbull BB, Chu JN, Lee P, Nguyen K, et al. Metabolic tumor volume predicts for recurrence and death in head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2009;74:1335–41.

    Article  PubMed Central  PubMed  Google Scholar 

  25. Tang C, Murphy JD, Khong B, La TH, Kong C, Fischbein NJ, et al. Validation that metabolic tumor volume predicts outcome in head-and-neck cancer. Int J Radiat Oncol Biol Phys. 2012;83:1514–20.

    Article  PubMed Central  PubMed  Google Scholar 

  26. Chang KP, Tsang NM, Liao CT, Hsu CL, Chung MJ, Lo CW, et al. Prognostic significance of 18F-FDG PET parameters and plasma Epstein-Barr virus DNA load in patients with nasopharyngeal carcinoma. J Nucl Med. 2012;53:21–8.

    Article  CAS  PubMed  Google Scholar 

  27. Tan S, Kligerman S, Chen W, Lu M, Kim G, Feigenberg S, et al. Spatial-temporal [18F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. Int J Radiat Oncol Biol Phys. 2013;85:1375–82.

    Article  PubMed Central  PubMed  Google Scholar 

  28. Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med. 2005;46:1342–8.

    PubMed  Google Scholar 

  29. Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging. 1999;2:159–71.

    Article  PubMed  Google Scholar 

  30. Loh HH, Leu JG, Luo RC. The analysis of natural textures using run length features. IEEE Trans Ind Electron. 1988;35:323–8.

    Article  Google Scholar 

  31. Thibault G, Fertil B, Navarro C, Pereira S, Cau P, Levy N, et al. Texture indexes and gray level size zone matrix: application to cell nuclei classification. In: Krasnoproshin V, Ablameyko S, Sadykhov R, editors. Pattern Recognition and Information Processing: Proceedings of the Tenth International Conference, 19–21 May 2009, Minsk, Belarus. Minsk: Belarusian State University; 2009. p. 140–5.

    Google Scholar 

  32. Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53:693–700.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.

    Article  CAS  PubMed  Google Scholar 

  34. Gerlinger M, Swanton C. How darwinian models inform therapeutic failure initiated by clonal heterogeneity in cancer medicine. Br J Cancer. 2010;103:1139–43.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  35. Orlhac F, Soussan M, Maisonobe JA, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 2014;55:414–22.

    Article  CAS  PubMed  Google Scholar 

  36. Zhang H, Tan S, Chen W, Kligerman S, Kim G, D’Souza WD, et al. Modeling pathologic response of esophageal cancer to chemoradiation therapy using spatial-temporal 18F-FDG PET features, clinical parameters, and demographics. Int J Radiat Oncol Biol Phys. 2014;88:195–203.

    Article  PubMed  Google Scholar 

  37. Schinagl DA, Span PN, van den Hoogen FJ, Merkx MA, Slootweg PJ, Oyen WJ, et al. Pathology-based validation of FDG PET segmentation tools for volume assessment of lymph node metastases from head and neck cancer. Eur J Nucl Med Mol Imaging. 2013;40:1828–35.

    Article  PubMed  Google Scholar 

  38. Arens AI, Troost EG, Hoeben BA, Grootjans W, Lee JA, Grégoire V, et al. Semiautomatic methods for segmentation of the proliferative tumour volume on sequential FLT PET/CT images in head and neck carcinomas and their relation to clinical outcome. Eur J Nucl Med Mol Imaging. 2014;41:915–24.

    Article  PubMed  Google Scholar 

Download references

Conflicts of interest

None.

Financial support

This study was supported by the grant NMRPG102-2221-E-182-074-MY2 from the National Science Council, Taiwan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tzu-Chen Yen.

Additional information

Ching-Han Hsu and Tzu-Chen Yen contributed equally to this work.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, NM., Fang, YH.D., Lee, Ly. et al. Zone-size nonuniformity of 18F-FDG PET regional textural features predicts survival in patients with oropharyngeal cancer. Eur J Nucl Med Mol Imaging 42, 419–428 (2015). https://doi.org/10.1007/s00259-014-2933-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-014-2933-1

Keywords

Navigation