Skip to main content

Advertisement

SpringerLink
Log in

A systematic review of the prognostic value of texture analysis in 18F-FDG PET in lung cancer

  • Original Article
  • Published:
Annals of Nuclear Medicine Aims and scope Submit manuscript

Abstract

Objective

The aim of this study was to perform a systematic review of the prognostic value of texture parameters derived by 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) in patients with lung cancer.

Methods

PubMed and EMBASE databases were searched up to March 12, 2018, for original articles involving texture analysis for the prediction of prognosis in patients with lung cancer. Risk of bias in the studies was critically assessed using the QUIPS tool. The results of survival analysis in the included studies were compared.

Results

Of the 446 articles retrieved, 17 studies were eligible for inclusion. Our review suggests that the prognostic value of texture parameters in lung cancer remains unproven. Most studies had a moderate to high risk of bias. Texture parameters that described prognosis were not replicated across studies. Conflicting results on hazard ratios were found among the studies. This discrepancy is partly explained by false-positive findings originating from statistical error and variability caused by different methodologies used for image acquisition and processing in the included studies.

Conclusion

Based on currently available evidence, there is insufficient evidence to support the prognostic value of texture analysis in 18F-FDG PET in lung cancer. Further studies implementing well-established methodologies and statistical evidence are warranted for proper validation of these promising imaging biomarkers.

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. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017;67:7–30.

    Article  Google Scholar 

  2. Detterbeck FC, Boffa DJ, Kim AW, Tanoue LT. The eighth edition lung cancer stage classification. Chest. 2017;151:193–203.

    Article  Google Scholar 

  3. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92.

    Article  CAS  Google Scholar 

  4. Michor F, Polyak K. The origins and implications of intratumor heterogeneity. Cancer Prev Res (Phila). 2010;3:1361–4.

    Article  Google Scholar 

  5. Vansteenkiste J, Fischer BM, Dooms C, Mortensen J. Positron-emission tomography in prognostic and therapeutic assessment of lung cancer: systematic review. Lancet Oncol. 2004;5:531–40.

    Article  Google Scholar 

  6. Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44:151–65.

    Article  Google Scholar 

  7. Bashir U, Siddique MM, McLean E, Goh V, Cook GJ. Imaging heterogeneity in lung cancer: techniques, applications, and challenges. AJR Am J Roentgenol. 2016;207:534–43.

    Article  Google Scholar 

  8. Cook GJ, Siddique M, Taylor BP, Yip C, Chicklore S, Goh V. Radiomics in PET: principles and applications. Clin Transl Imaging. 2014;2:269–76.

    Article  Google Scholar 

  9. Nardone V, Tini P, Nioche C, Mazzei MA, Carfagno T, Battaglia G, et al. Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med. 2018;123:415–23.

    Article  Google Scholar 

  10. Apostolova I, Ego K, Steffen IG, Buchert R, Wertzel H, Achenbach HJ, et al. The asphericity of the metabolic tumour volume in NSCLC: correlation with histopathology and molecular markers. Eur J Nucl Med Mol Imaging. 2016;43:2360–73.

    Article  CAS  Google Scholar 

  11. Carvalho S, Leijenaar RTH, Troost EGC, van Timmeren JE, Oberije C, van Elmpt W, et al. 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC): a prospective externally validated study. PLoS One. 2018;13:e0192859.

    Article  Google Scholar 

  12. Cheng NM, Fang YH, Tsan DL, Hsu CH, Yen TC. Respiration-averaged CT for attenuation correction of pet images—impact on pet texture features in non-small cell lung cancer patients. PLoS One. 2016;11:e0150509.

    Article  Google Scholar 

  13. 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  Google Scholar 

  14. Cook GJ, O’Brien ME, Siddique M, Chicklore S, Loi HY, Sharma B, et al. Non-small cell lung cancer treated with erlotinib: heterogeneity of (18)F-FDG uptake at PET-association with treatment response and prognosis. Radiology. 2015;276:883–93.

    Article  Google Scholar 

  15. Dong X, Sun X, Sun L, Maxim PG, Xing L, Huang Y, et al. Early change in metabolic tumor heterogeneity during chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer. PLoS One. 2016;11:e0157836.

    Article  Google Scholar 

  16. Grootjans W, Tixier F, van der Vos CS, Vriens D, Le Rest CC, Bussink J, et al. The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG pet imaging of lung cancer. J Nucl Med. 2016;57:1692–8.

    Article  Google Scholar 

  17. Jensen GL, Yost CM, Mackin DS, Fried DV, Zhou S, Court LE, et al. Prognostic value of combining a quantitative image feature from positron emission tomography with clinical factors in oligometastatic non-small cell lung cancer. Radiother Oncol. 2018;126:362–7.

    Article  Google Scholar 

  18. Kirienko M, Cozzi L, Antunovic L, Lozza L, Fogliata A, Voulaz E, et al. Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery. Eur J Nucl Med Mol Imaging. 2018;45:207–17.

    Article  Google Scholar 

  19. Koh YW, Park SY, Hyun SH, Lee SJ. Associations between PET textural features and GLUT1 expression, and the prognostic significance of textural features in lung adenocarcinoma. Anticancer Res. 2018;38:1067–71.

    PubMed  Google Scholar 

  20. Lapa P, Marques M, Isidoro J, Barata F, Costa G, de Lima JP. (18)F-FDG PET/CT in lung cancer. The added value of quantification. Rev Esp Med Nucl Imagen Mol. 2017;36:342–9.

    CAS  PubMed  Google Scholar 

  21. Lovinfosse P, Janvary ZL, Coucke P, Jodogne S, Bernard C, Hatt M, et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging. 2016;43:1453–60.

    Article  CAS  Google Scholar 

  22. Ohri N, Duan F, Snyder BS, Wei B, Machtay M, Alavi A, et al. Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med. 2016;57:842–8.

    Article  CAS  Google Scholar 

  23. Park S, Ha S, Lee SH, Paeng JC, Keam B, Kim TM, et al. Intratumoral heterogeneity characterized by pretreatment PET in non-small cell lung cancer patients predicts progression-free survival on EGFR tyrosine kinase inhibitor. PLoS One. 2018;13:e0189766.

    Article  Google Scholar 

  24. Pyka T, Bundschuh RA, Andratschke N, Mayer B, Specht HM, Papp L, et al. Textural features in pre-treatment [F18]-FDG-PET/CT are correlated with risk of local recurrence and disease-specific survival in early stage NSCLC patients receiving primary stereotactic radiation therapy. Radiat Oncol. 2015;10:100.

    Article  Google Scholar 

  25. Takeda K, Takanami K, Shirata Y, Yamamoto T, Takahashi N, Ito K, et al. Clinical utility of texture analysis of 18F-FDG PET/CT in patients with stage I lung cancer treated with stereotactic body radiotherapy. J Radiat Res. 2017;58:862–9.

    Article  Google Scholar 

  26. Tixier F, Hatt M, Valla C, Fleury V, Lamour C, Ezzouhri S, et al. Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med. 2014;55:1235–41.

    Article  CAS  Google Scholar 

  27. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. J Clin Epidemiol. 2009;62:e1–34.

    Article  Google Scholar 

  28. Hayden JA, van der Windt DA, Cartwright JL, Cote P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158:280–6.

    Article  Google Scholar 

  29. Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, et al. Guidance on the conduct of narrative synthesis in systematic reviews: A product from the ESRC methods programme. London: Institute for Health Research; 2006. p. b92.

    Google Scholar 

  30. Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST, et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. J Nucl Med. 2015;56:1667–73.

    Article  CAS  Google Scholar 

  31. Bashir U, Azad G, Siddique MM, Dhillon S, Patel N, Bassett P, et al. The effects of segmentation algorithms on the measurement of (18)F-FDG PET texture parameters in non-small cell lung cancer. EJNMMI Res. 2017;7:60.

    Article  Google Scholar 

  32. 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  Google Scholar 

  33. 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  Google Scholar 

  34. Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10:e0124165.

    Article  Google Scholar 

  35. Coroller TP, Agrawal V, Huynh E, Narayan V, Lee SW, Mak RH, et al. Radiomic-based pathological response prediction from primary tumors and lymph nodes in NSCLC. J Thorac Oncol. 2017;12:467–76.

    Article  Google Scholar 

  36. Koksal D, Demirag F, Bayiz H, Ozmen O, Tatci E, Berktas B, et al. The correlation of SUVmax with pathological characteristics of primary tumor and the value of tumor/lymph node SUVmax ratio for predicting metastasis to lymph nodes in resected NSCLC patients. J Cardiothorac Surg. 2013;8:63.

    Article  Google Scholar 

  37. Talmadge JE, Fidler IJ. AACR centennial series: the biology of cancer metastasis: historical perspective. Cancer Res. 2010;70:5649–69.

    Article  CAS  Google Scholar 

  38. Gomez-Roca C, Raynaud CM, Penault-Llorca F, Mercier O, Commo F, Morat L, et al. Differential expression of biomarkers in primary non-small cell lung cancer and metastatic sites. J Thorac Oncol. 2009;4:1212–20.

    Article  Google Scholar 

  39. Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014;55:37–42.

    Article  CAS  Google Scholar 

Download references

Funding

None.

Author information

Authors and Affiliations

  1. Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Sangwon Han, Jungsu S. Oh & Jong Jin Lee

  2. Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea

    Sungmin Woo

  3. Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea

    Chong Hyun Suh

  4. Department of Radiation Oncology, Kangwon National University Hospital, Chuncheon, South Korea

    Yeon Joo Kim

Authors
  1. Sangwon Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sungmin Woo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chong Hyun Suh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yeon Joo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jungsu S. Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jong Jin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jong Jin Lee.

Ethics declarations

Conflict of interest

The authors have nothing to disclose.

Additional information

Sangwon Han, Sungmin Woo and Chong Hyun Suh belong to Meta-analysis for Imaging studies on Diagnostic test Accuracy and prognosis (MIDAS) Group.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, S., Woo, S., Suh, C.H. et al. A systematic review of the prognostic value of texture analysis in 18F-FDG PET in lung cancer. Ann Nucl Med 32, 602–610 (2018). https://doi.org/10.1007/s12149-018-1281-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12149-018-1281-9

Keywords

Search

Navigation