Skip to main content
SpringerLink
Log in

Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method

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

Abstract

Purpose

Sphericity has been proposed as a parameter for characterizing PET tumour volumes, with complementary prognostic value with respect to SUV and volume in both head and neck cancer and lung cancer. The objective of the present study was to investigate its dependency on tumour delineation and the resulting impact on its prognostic value.

Methods

Five segmentation methods were considered: two thresholds (40% and 50% of SUVmax), ant colony optimization, fuzzy locally adaptive Bayesian (FLAB), and gradient-aided region-based active contour. The accuracy of each method in extracting sphericity was evaluated using a dataset of 176 simulated, phantom and clinical PET images of tumours with associated ground truth. The prognostic value of sphericity and its complementary value with respect to volume for each segmentation method was evaluated in a cohort of 87 patients with stage II/III lung cancer.

Results

Volume and associated sphericity values were dependent on the segmentation method. The correlation between segmentation accuracy and sphericity error was moderate (|ρ| from 0.24 to 0.57). The accuracy in measuring sphericity was not dependent on volume (|ρ| < 0.4). In the patients with lung cancer, sphericity had prognostic value, although lower than that of volume, except for that derived using FLAB for which when combined with volume showed a small improvement over volume alone (hazard ratio 2.67, compared with 2.5). Substantial differences in patient prognosis stratification were observed depending on the segmentation method used.

Conclusion

Tumour functional sphericity was found to be dependent on the segmentation method, although the accuracy in retrieving the true sphericity was not dependent on tumour volume. In addition, even accurate segmentation can lead to an inaccurate sphericity value, and vice versa. Sphericity had similar or lower prognostic value than volume alone in the patients with lung cancer, except when determined using the FLAB method for which there was a small improvement in stratification when the parameters were combined.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recogn. 2009;42:1162–71.

    Article  Google Scholar 

  2. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.

    Article  PubMed  PubMed Central  Google Scholar 

  3. 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  PubMed  Google Scholar 

  4. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJ. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging. 2011;38:1636–47.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 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  PubMed Central  Google Scholar 

  7. Apostolova I, Rogasch J, Buchert R, Wertzel H, Achenbach HJ, Schreiber J, et al. Quantitative assessment of the asphericity of pretherapeutic FDG uptake as an independent predictor of outcome in NSCLC. BMC Cancer. 2014;14:896.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Apostolova I, Steffen IG, Wedel F, Lougovski A, Marnitz S, Derlin T, et al. Asphericity of pretherapeutic tumour FDG uptake provides independent prognostic value in head-and-neck cancer. Eur Radiol. 2014;24:2077–87.

    Article  PubMed  Google Scholar 

  9. Hofheinz F, Lougovski A, Zöphel K, Hentschel M, Steffen IG, Apostolova I, et al. Increased evidence for the prognostic value of primary tumor asphericity in pretherapeutic FDG PET for risk stratification in patients with head and neck cancer. Eur J Nucl Med Mol Imaging. 2015;42:429–37.

    Article  PubMed  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  PubMed  Google Scholar 

  11. Hofheinz F, Pötzsch C, Oehme L, Beuthien-Baumann B, Steinbach J, Kotzerke J, et al. Automatic volume delineation in oncological PET. Evaluation of a dedicated software tool and comparison with manual delineation in clinical data sets. Nuklearmedizin. 2012;51:9–16.

    Article  CAS  PubMed  Google Scholar 

  12. Kim D-H, Jung J-H, Son SH, Kim C-Y, Jeong SY, Lee S-W, et al. Quantification of Intratumoral metabolic macroheterogeneity on 18F-FDG PET/CT and its prognostic significance in pathologic N0 squamous cell lung carcinoma. Clin Nucl Med. 2016;41:e70–e75.

    Article  PubMed  Google Scholar 

  13. Ben Bouallègue F, Al Tabaa Y, Kafrouni M, Cartron G, Vauchot F, Mariano-Goulart D. Association between textural and morphological tumor indices on baseline PET-CT and early metabolic response on interim PET-CT in bulky malignant lymphomas. Med Phys. 2017;44:4608–19.

    Article  PubMed  Google Scholar 

  14. Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging. 2009;28:881–93.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Fayad H, Hatt M, Visvikis D. PET functional volume delineation using an ant colony segmentation approach. J Nucl Med. 2015;56 Suppl 3:1745.

    Google Scholar 

  16. Jaouen V, Hatt M, Fayad H, Tauber C, Visvikis D. Gradient-aided localized deformable model for PET image segmentation. Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference, Strasbourg, France, 29 Oct – 6 Nov 2016.

  17. Hatt M, Cheze Le Rest C, Albarghach N, Pradier O, Visvikis D. PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging. 2011;38:663–72.

    Article  PubMed  Google Scholar 

  18. Hatt M, Lee J, Schmidtlein CR, El Naqa I, Caldwell C, De Bernardi E, et al. Classification and evaluation strategies of auto-segmentation approaches for PET: report of AAPM task group no. 211. Med Phys. 2017;44:e1–e42.

  19. Berthon B, Spezi E, Galavis P, Shepherd T, Apte A, Hatt M, et al. Towards a standard for the evaluation of PET auto-segmentation methods: requirements and implementation. Med Phys. 2017;44:4098–111.

  20. Lorensen WE, Cline HE. Marching cubes: a high resolution 3D surface construction algorithm. ACM SIGGRAPH Comput Graph. 1987;21:163–9.

    Article  Google Scholar 

  21. Desseroit MC, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R, et al. Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging. 2016;43:1477–85.

  22. 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  PubMed  PubMed Central  Google Scholar 

  23. 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  PubMed  Google Scholar 

  24. Orlhac F, Nioche C, Soussan M, Buvat I. Understanding changes in tumor texture indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med. 2017;58:387–92.

    Article  PubMed  Google Scholar 

  25. Hatt M, Tixier F, Visvikis D, Cheze Le Rest C. Radiomics in PET/CT: more than meets the eye? J Nucl Med. 2017;58:365–6.

    Article  PubMed  Google Scholar 

  26. Yip SSF, Liu Y, Parmar C, Li Q, Liu S, Qu F, et al. Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep. 2017;7:3519.

    Article  PubMed  PubMed Central  Google Scholar 

  27. van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol. 2016;18:788–95.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Desseroit M-C, Tixier F, Weber WA, Siegel BA, Cheze Le Rest C, Visvikis D, et al. Reliability of PET/CT shape and heterogeneity features in functional and morphologic components of non-small cell lung cancer tumors: a repeatability analysis in a prospective multicenter cohort. J Nucl Med. 2017;58:406–11.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, et al. Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol. 2013;52:1391–7.

    Article  CAS  PubMed  Google Scholar 

  30. Hatt M, Visvikis D, Albarghach NM, Tixier F, Pradier O, Cheze-le RC. Prognostic value of 18F-FDG PET image-based parameters in oesophageal cancer and impact of tumour delineation methodology. Eur J Nucl Med Mol Imaging. 2011;38:1191–202.

    Article  PubMed  Google Scholar 

  31. Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One. 2015;10:e0118261.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank AAPM task group 211 members who contributed datasets: Assen Kirov, John Lee, Michalis Aristophanous, Emiliano Spezi, Béatrice Berthon, Elisabetta De Bernardi.

Funding

This study was funded in part by the National Institute of Cancer (INCa project PRT-K15–119). Assen Kirov’s contribution was funded in part by NIH/NCI grant P30CA008748.

Author information

Authors and Affiliations

  1. LaTIM, INSERM, UMR 1101, IBSAM, UBO, UBL, Brest, France

    Mathieu Hatt, Baptiste Laurent, Hadi Fayad, Vincent Jaouen, Dimitris Visvikis & Catherine Cheze Le Rest

  2. Department of Nuclear Medicine, CHU Milétrie, Poitiers, France

    Catherine Cheze Le Rest

Authors
  1. Mathieu Hatt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baptiste Laurent
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hadi Fayad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Vincent Jaouen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dimitris Visvikis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Catherine Cheze Le Rest
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mathieu Hatt.

Ethics declarations

Conflicts of interest

None.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Electronic supplementary material

ESM 1

(DOCX 745 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hatt, M., Laurent, B., Fayad, H. et al. Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method. Eur J Nucl Med Mol Imaging 45, 630–641 (2018). https://doi.org/10.1007/s00259-017-3865-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-017-3865-3

Keywords

Search

Navigation