Accuracy of metabolic volume and total glycolysis among six threshold-based target segmentation algorithms



This study aimed to evaluate the accuracy of six threshold-based segmentation methods with different target-to-background ratios (TBR), images with different voxel sizes and image noise, in measuring metabolic volume (MV) and total glycolysis (TG).


A standard body phantom consisting of six spheres (inner diameters of 37, 28, 22, 17, 13, and 10 mm) was filled with 18F-FDG solution. The background radioactivity level was 2.65 kBq/mL, and the TBRs were 4 and 8. PET data were acquired for 30 min with list mode. PET data for 30 and 3 min were reconstructed with a three-dimensional ordered subset expectation maximization algorithm plus time-of-flight information with images with 2 and 4 mm isotropic voxels. The six methods examined were absolute standardized uptake value (SUV) of 2.5 (SUV2.5), 41%, 50%, adaptive 41%, and adaptive 50% thresholds of maximum SUV (Th41, Th50, ThA41, and ThA50, respectively); and the contrast-oriented algorithm (ThCOA). Segmented MV and TG were compared with the actual inner volume and expressed as percentages (%MVseg and %TGseg, respectively). In addition, the segmented MV was converted to the diameter, and the differences of it from the reference diameter were compared among six methods.


The ThCOA method yielded the most accurate measurements of %MVseg and %TGseg; the difference between %MVseg or %TGseg and its reference were smaller than 10% in 30-min and 15% in 3-min images, but the segmented contour was almost the same as the reference diameter. Measurements with Th50 and ThCOA were highly accurate for both %MVseg and %TGseg in the large spheres, and the adaptive threshold methods, including ThA41, ThA50, and ThCOA, were also highly accurate in the small spheres. The voxel sizes affected the accuracy of %MVseg and %TGseg with a TBR of 4 in any threshold-based methods.


Of the six threshold-based segmentation methods studied, ThCOA was the most accurate method for evaluating MV and TG and had only minor dependence on TBRs and sphere size. The small voxel sizes improved the variation of the accuracy in low TBR.

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  1. 1.

    Schöder H, Erdi YE, Larson SM, Yeung HWD. PET/CT: a new imaging technology in nuclear medicine. Eur J Nucl Med Mol Imaging. 2003;30:1419–37.

    Article  Google Scholar 

  2. 2.

    Townsend DW. Dual-modality imaging: combining anatomy and function. J Nucl Med. 2008;49:938–55.

    Article  Google Scholar 

  3. 3.

    Liu Y. FDG PET/CT for metastatic squamous cell carcinoma of unknown primary of the head and neck. Oral Oncol. 2019;92:46–51.

    Article  Google Scholar 

  4. 4.

    Groheux D, Cochet A, Humbert O, Alberini J-L, Hindie E, Mankoff D. 18F-FDG PET/CT for staging and restaging of breast cancer. J Nucl Med. 2016;57:17S–26S.

    CAS  Article  Google Scholar 

  5. 5.

    Duch J, Fuster D, Muñoz M, Fernández PL, Paredes P, Fontanillas M, et al. 18F-FDG PET/CT for early prediction of response to neoadjuvant chemotherapy in breast cancer. Eur J Nucl Med Mol Imaging. 2009;36:1551–7.

    CAS  Article  Google Scholar 

  6. 6.

    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.

    CAS  Article  Google Scholar 

  7. 7.

    Lee JW, Kang CM, Choi HJ, Lee WJ, Song SY, Lee J-H, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis on preoperative 18F-FDG PET/CT in patients with pancreatic cancer. J Nucl Med. 2014;55:898–904.

    Article  Google Scholar 

  8. 8.

    Chung HW, Lee KY, Kim HJ, Kim WS, So Y. FDG PET/CT metabolic tumor volume and total lesion glycolysis predict prognosis in patients with advanced lung adenocarcinoma. J Cancer Res Clin Oncol. 2014;140:89–988.

    CAS  Article  Google Scholar 

  9. 9.

    Im HJ, Pak K, Cheon GJ, Kang KW, Kim SJ, Kim IJ, et al. Prognostic value of volumetric parameters of 18F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging. 2015;42:241–51.

    CAS  Article  Google Scholar 

  10. 10.

    Troost EGC, Schinagl DAX, Bussink J, Oyen WJG, Kaanders JHAM. Clinical evidence on PET-CT for radiation therapy planning in head and neck tumours. Radiother Oncol. 2010;96:328–34.

    Article  Google Scholar 

  11. 11.

    Ben-Haim S, Ell P. 18F-FDG PET and PET/CT in the evaluation of cancer treatment response. J Nucl Med. 2009;50:88–99.

    Article  Google Scholar 

  12. 12.

    Boellaard R, Krak NC, Hoekstra OS, Lammertsma AA. Effects of noise, image resolution, and ROI definition on the accuracy of standard uptake values: a simulation study. J Nucl Med. 2004;45:1519–27.

    PubMed  Google Scholar 

  13. 13.

    Cheebsumon P, van Velden FHP, Yaqub M, Frings V, de Langen AJ, Hoekstra OS, et al. Effects of image characteristics on performance of tumor delineation methods: a test-retest assessment. J Nucl Med. 2011;52:1550–8.

    CAS  Article  Google Scholar 

  14. 14.

    Firouzian A, Kelly MD, Declerck JM. Insight on automated lesion delineation methods for PET data. EJNMMI Res. 2014;4:1–12.

    CAS  Article  Google Scholar 

  15. 15.

    Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch CM, Nestle U. A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: Derivation from phantom measurements and validation in patient data. Eur J Nucl Med Mol Imaging. 2008;35:1989–99.

    Article  Google Scholar 

  16. 16.

    Cheebsumon P, Yaqub M, Van Velden FHP, Hoekstra OS, Lammertsma AA, Boellaard R. Impact of 18F-FDG PET imaging parameters on automatic tumour delineation: need for improved tumour delineation methodology. Eur J Nucl Med Mol Imaging. 2011;38:2136–44.

    Article  Google Scholar 

  17. 17.

    Ketabi A, Ghafarian P, Mosleh-Shirazi MA, Mahdavi SR, Rahmim A, Ay MR. Impact of image reconstruction methods on quantitative accuracy and variability of FDG-PET volumetric and textural measures in solid tumors. Eur Radiol. 2019;29:2146–56.

    Article  Google Scholar 

  18. 18.

    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  Google Scholar 

  19. 19.

    Parkinson C, Evans M, Guerrero-Urbano T, Michaelidou A, Pike L, Barrington S, et al. Machine-learned target volume delineation of 18F-FDG PET images after one cycle of induction chemotherapy. Phys Med. 2019;61:85–93.

    Article  Google Scholar 

  20. 20.

    Conti M. Focus on time-of-flight PET: the benefits of improved time resolution. Eur J Nucl Med Mol Imaging. 2011;38:1147–57.

    Article  Google Scholar 

  21. 21.

    Boellaard R, Delgado-Bolton R, Oyen WJG, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328–54.

    CAS  Article  Google Scholar 

  22. 22.

    Koopman D, van Dalen JA, Lagerweij MCM, Arkies H, de Boer J, Oostdijk AHJ, et al. Improving the detection of small lesions using a state-of-the-art time-of-flight PET/CT system and small-voxel reconstructions. J Nucl Med Technol. 2015;43:21–7.

    Article  Google Scholar 

  23. 23.

    Li C-Y, Klohr S, Sadick H, Weiss C, Hoermann K, Schoenberg SO, et al. Effect of time-of-flight technique on the diagnostic performance of 18F-FDG PET/CT for assessment of lymph node metastases in head and neck squamous cell carcinoma. J Nucl Med Technol. 2014;42:181–7.

    Article  Google Scholar 

  24. 24.

    Morey AM, Noo F, Kadrmas DJ. Effect of using 2 mm voxels on observer performance for pet lesion detection. IEEE Trans Nucl Sci. 2016;63:1359–66.

    CAS  Article  Google Scholar 

  25. 25.

    Schaefer A, Kim YJ, Kremp S, Mai S, Fleckenstein J, Bohnenberger H, et al. PET-based delineation of tumour volumes in lung cancer: comparison with pathological findings. Eur J Nucl Med Mol Imaging. 2013;40:1233–44.

    CAS  Article  Google Scholar 

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Correspondence to Masayuki Sasaki.

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Nakaichi, T., Yamashita, S., Kawakami, W. et al. Accuracy of metabolic volume and total glycolysis among six threshold-based target segmentation algorithms. Ann Nucl Med 34, 583–594 (2020).

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  • PET/CT
  • Threshold-based segmentation method
  • Metabolic volume
  • Total glycolysis
  • Reconstruction voxel sizes