Comparison of three freeware software packages for 18F-FDG PET texture feature calculation

Abstract

Purpose

To compare texture feature estimates obtained from 18F-FDG-PET images using three different software packages.

Methods

PET images from 15 patients with head and neck cancer were processed with three different freeware software: CGITA, LIFEx, and Metavol. For each lesion, 38 texture features were extracted from each software package. To evaluate the statistical agreement among the features across packages a non-parametric Kruskal–Wallis test was used. Differences in the features between each couple of software were assessed using a subsequent Dunn test. Correlation between texture features was evaluated via the Spearman coefficient.

Results

Twenty-three of 38 features showed a significant agreement across the three software (P < 0.05). The agreement was better between LIFEx vs. Metavol (36 of 38) and worse between CGITA and Metavol (24 of 38), and CGITA vs. LIFEx (23 of 38). All features resulted correlated (ρ >  = 0.70, P < 0.001) in comparing LIFEx vs. Metavol. Seven of 38 features were found not in agreement and slightly or not correlated (ρ < 0.70, P < 0.001) in comparing CGITA vs. LIFEx, and CGITA vs. Metavol.

Conclusion

Some texture discrepancies across software packages exist. Our findings reinforce the need to continue the standardization process, and to succeed in building a reference dataset to be used for comparisons.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Gonzalez RC, Woods RE. Digital image processing. Truskey: Third Edition - Pearson Prentice Hall; 2008. p. 827–56.

    Google Scholar 

  2. 2.

    Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;3(5):4006.

    Article  Google Scholar 

  3. 3.

    Heppner GH. Tumor heterogeneity. Cancer Res. 1984;44(6):2259–65.

    CAS  PubMed  Google Scholar 

  4. 4.

    Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81–94.

    CAS  Article  Google Scholar 

  5. 5.

    Kitajima K, Suenaga Y, Sugimura K. Present and future role of FDG-PET/CT imaging in the management of head and neck carcinoma. Jpn J Radiol. 2015;33(12):776–89.

    Article  Google Scholar 

  6. 6.

    Buvat I, Orlhac F, Soussan M. Tumor texture analysis in pet: where do we stand. J Nucl Med. 2015;56(11):1642–4.

    CAS  Article  Google Scholar 

  7. 7.

    O’Connor JP, Rose CJ, Waterton JC, Carano RA, Parker GJ, Jackson A. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res. 2015;21(2):249–57.

    CAS  Article  Google Scholar 

  8. 8.

    Nyflot MJ, Yang F, Byrd D, Bowen SR, Sandison GA, Kinahan PE. Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards. J Med Imaging (Bellingham). 2015;2(4):041002.

    Article  Google Scholar 

  9. 9.

    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(1):151–65.

    Article  Google Scholar 

  10. 10.

    van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B. Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging. 2020;11(1):91.

    Article  Google Scholar 

  11. 11.

    Image biomarker standardization initiative reference manual. https://arxiv.org/pdf/1612.07003.pdf. Accessed 2 Oct 2020.

  12. 12.

    Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging. 2019;46(13):2638–55.

    Article  Google Scholar 

  13. 13.

    Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328–38.

    Article  Google Scholar 

  14. 14.

    Fang YH, Lin CY, Shih MJ, Wang HM, Ho TY, Liao CT, et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed Res Int. 2014;2014:248505.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9.

    CAS  Article  Google Scholar 

  16. 16.

    Hirata K, Kobayashi K, Wong KP, Manabe O, Surmak A, Tamaki N, et al. A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS ONE. 2014;9(8):e105682.

    Article  Google Scholar 

  17. 17.

    van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

    Article  Google Scholar 

  18. 18.

    Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42(3):1341–53.

    Article  Google Scholar 

  19. 19.

    Apte AP, Iyer A, Crispin-Ortuzar M, Pandya R, van Dijk LV, Spezi E, et al. Technical note: extension of CERR for computational radiomics: a comprehensive MATLAB platform for reproducible radiomics research. Med Phys. 2015. https://doi.org/10.1002/mp.13046.

    Article  Google Scholar 

  20. 20.

    Foy JJ, Robinson KR, Li H, Giger ML, Al-Hallaq H, Armato SG. Variation in algorithm implementation across radiomics software. J Med Imaging (Bellingham). 2018;5(4):044505.

    Google Scholar 

  21. 21.

    Liang ZG, Tan HQ, Zhang F, Rui Tan LK, Lin L, Lenkowicz J, et al. Comparison of radiomics tools for image analyses and clinical prediction in nasopharyngeal carcinoma. Br J Radiol. 2019;92(1102):20190271.

    Article  Google Scholar 

  22. 22.

    Bogowicz M, Leijenaar RTH, Tanadini-Lang S, Riesterer O, Pruschy M, Studer G, et al. Post-radiochemotherapy PET radiomics in head and neck cancer—The influence of radiomics implementation on the reproducibility of local control tumor models. Radiother Oncol. 2017;125(3):385–91.

    Article  Google Scholar 

  23. 23.

    Im HJ, Bradshaw T, Solaiyappan M, Cho SY. Current methods to define metabolic tumor volume in positron emission tomography: which one is better? Nucl Med Mol Imaging. 2018;52(1):5–15.

    Article  Google Scholar 

  24. 24.

    Song F, Guo Z, Mei D. Feature selection using principal component analysis. International conference on system science, engineering design and manufacturing informatization, Yichang, 2010. p. 27–30.

  25. 25.

    Lu L, Sun SH, Yang H, Guo P, Schwartz LH, et al. Radiomics prediction of EGFR status in lung cancer-our experience in using multiple feature extractors and the cancer imaging archive data. Tomography. 2020;6(2):223–30.

    Article  Google Scholar 

  26. 26.

    Fornacon-Wood I, Mistry H, Ackermann CJ, Blackhall F, McPartlin A, Faivre-Finn C, et al. Reliability and prognostic value of radiomic features are highly dependent on choice of feature extraction platform. Eur Radiol. 2020;30(11):6241–50.

    Article  Google Scholar 

  27. 27.

    Foy JJ, Armato SG, Al-Hallaq H. Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis. J Med Imaging. 2020;7(1):014504.

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Contributions

ML and RM conceptualized the paper; ML and RM evaluated and reported the imaging findings; RM carried out the statistical analysis; RS provided medical advice; ML, RM, and RS drafted the manuscript; all the authors revised the paper and approved its final version.

Corresponding author

Correspondence to Rosario Megna.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

All the procedures performed in this study involving human participants were in accordance with international ethical standards detailed in the 1964 Declaration of Helsinki and its later amendments, and according to the Italian Personal Data Protection Code for scientific research.

Informed consent

Nothing to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

About this article

Verify currency and authenticity via CrossMark

Cite this article

Larobina, M., Megna, R. & Solla, R. Comparison of three freeware software packages for 18F-FDG PET texture feature calculation. Jpn J Radiol (2021). https://doi.org/10.1007/s11604-021-01100-0

Download citation

Keywords

  • Positron emission tomography
  • Oncology
  • Radiomics
  • Texture