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Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?

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Abstract

Numerous methods to segment tumors using 18F-fluorodeoxyglucose positron emission tomography (FDG PET) have been introduced. Metabolic tumor volume (MTV) refers to the metabolically active volume of the tumor segmented using FDG PET, and has been shown to be useful in predicting patient outcome and in assessing treatment response. Also, tumor segmentation using FDG PET has useful applications in radiotherapy treatment planning. Despite extensive research on MTV showing promising results, MTV is not used in standard clinical practice yet, mainly because there is no consensus on the optimal method to segment tumors in FDG PET images. In this review, we discuss currently available methods to measure MTV using FDG PET, and assess the advantages and disadvantages of the methods.

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References

  1. Vanderhoek M, Perlman SB, Jeraj R. Impact of the definition of peak standardized uptake value on quantification of treatment response. J Nucl Med. 2012;53:4–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Larson SM, Erdi Y, Akhurst T, 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.

  3. Im HJ, Kim TS, Park SY, et al. Prediction of tumour necrosis fractions using metabolic and volumetric 18F-FDG PET/CT indices, after one course and at the completion of neoadjuvant chemotherapy, in children and young adults with osteosarcoma. Eur J Nucl Med Mol Imaging. 2012;39:39–49.

    Article  CAS  PubMed  Google Scholar 

  4. Im HJ, Kim YK, Kim YI, Lee JJ, Lee WW, Kim SE. Usefulness of combined metabolic-volumetric indices of (18)F-FDG PET/CT for the early prediction of neoadjuvant chemotherapy outcomes in breast cancer. Nucl Med Mol Imaging. 2013;47:36–43.

  5. Lee JW, Kang CM, Choi HJ, 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.

  6. Byun BH, Kong CB, Lim I, et al. Early response monitoring to neoadjuvant chemotherapy in osteosarcoma using sequential (1)(8)F-FDG PET/CT and MRI. Eur J Nucl Med Mol Imaging. 2014;41:1553–62.

    Article  CAS  PubMed  Google Scholar 

  7. Cheebsumon P, van Velden FHP, Yaqub M, et al. Effects of image characteristics on performance of tumor delineation methods: a test–retest assessment. J Nucl Med. 2011;52:1550–8.

    Article  CAS  PubMed  Google Scholar 

  8. Moon SH, Hyun SH, Choi JY. Prognostic significance of volume-based PET parameters in cancer patients. Korean J Radiol. 2013;14:1–12.

    Article  PubMed  Google Scholar 

  9. JH O, Choi WH, Han EJ, et al. The prognostic value of (18)F-FDG PET/CT for early recurrence in operable breast cancer: comparison with TNM stage. Nucl Med Mol Imaging. 2013;47:263–7.

    Article  Google Scholar 

  10. Costelloe CM, Macapinlac HA, Madewell JE, et al. 18F-FDG PET/CT as an indicator of progression-free and overall survival in osteosarcoma. J Nucl Med. 2009;50:340–7.

  11. Hyun SH, Choi JY, Shim YM, et al. Prognostic value of metabolic tumor volume measured by 18F-fluorodeoxyglucose positron emission tomography in patients with esophageal carcinoma. Ann Surg Oncol. 2009;17:115–22.

  12. Yoo J, Choi JY, Moon SH, et al. Prognostic significance of volume-based metabolic parameters in uterine cervical cancer determined using 18F-fluorodeoxyglucose positron emission tomography. Int J Gynecol Cancer. 2012;22:1226–33.

    Article  PubMed  Google Scholar 

  13. Chen HH, Chiu NT, WC S, Guo HR, Lee BF. Prognostic value of whole-body total lesion glycolysis at pretreatment FDG PET/CT in non-small cell lung cancer. Radiology. 2012;264:559–66.

    Article  PubMed  Google Scholar 

  14. Hyun SH, Ahn HK, Ahn MJ, et al. Volume-based assessment with 18F-FDG PET/CT improves outcome prediction for patients with stage IIIA-N2 non-small cell lung cancer. AJR Am J Roentgenol. 2015;205:623–8.

    Article  PubMed  Google Scholar 

  15. Hyun SH, Ahn HK, Kim H, et al. Volume-based assessment by (18)F-FDG PET/CT predicts survival in patients with stage III non-small-cell lung cancer. Eur J Nucl Med Mol Imaging. 2014;41:50–8.

    Article  CAS  PubMed  Google Scholar 

  16. Kim DH, Son SH, Kim CY, et al. Prediction for recurrence using F-18 FDG PET/CT in pathologic N0 lung adenocarcinoma after curative surgery. Ann Surg Oncol. 2014;21:589–96.

    Article  PubMed  Google Scholar 

  17. Zaizen Y, Azuma K, Kurata S, et al. Prognostic significance of total lesion glycolysis in patients with advanced non-small cell lung cancer receiving chemotherapy. Eur J Radiol. 2012;81:4179–84.

    Article  PubMed  Google Scholar 

  18. Byun BH, Kong C-B, Park J, et al. Initial metabolic tumor volume measured by 18F-FDG PET/CT can predict the outcome of Osteosarcoma of the extremities. J Nucl Med. 2013;54:1725–32.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. Pak K, Cheon GJ, Nam HY, et al. Prognostic value of metabolic tumor volume and total lesion glycolysis in head and neck cancer: a systematic review and meta-analysis. J Nucl Med. 2014;55:884–90.

    Article  CAS  PubMed  Google Scholar 

  21. Han EJ, Yang YJ, Park JC, Park SY, Choi WH, Kim SH. Prognostic value of early response assessment using 18F-FDG PET/CT in chemotherapy-treated patients with non-small-cell lung cancer. Nucl Med Commun. 2015;36:1187–94.

    Article  CAS  PubMed  Google Scholar 

  22. Huang W, Fan M, Liu B, et al. Value of metabolic tumor volume on repeated 18F-FDG PET/CT for early prediction of survival in locally advanced non-small cell lung cancer treated with concurrent chemoradiotherapy. J Nucl Med. 2014;55:1584–90.

    Article  CAS  PubMed  Google Scholar 

  23. Burger IA, Casanova R, Steiger S, et al. FDG-PET/CT of non-small cell lung carcinoma under neo-adjuvant chemotherapy: background based adaptive volume metrics outperform TLG and MTV in predicting histopathological response. J Nucl Med. 2016;57:849–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Braendengen M, Hansson K, Radu C, Siegbahn A, Jacobsson H, Glimelius B. Delineation of gross tumor volume (GTV) for radiation treatment planning of locally advanced rectal cancer using information from MRI or FDG-PET/CT: a prospective study. Int J Radiat Oncol Biol Phys. 2011;81:e439–45.

    Article  PubMed  Google Scholar 

  25. Spratt DE, Diaz R, McElmurray J, et al. Impact of FDG PET/CT on delineation of the gross tumor volume for radiation planning in non-small-cell lung cancer. Clin Nucl Med. 2010;35:237–43.

    Article  PubMed  Google Scholar 

  26. Heron DE, Andrade RS, Flickinger J, et al. Hybrid PET-CT simulation for radiation treatment planning in head-and-neck cancers: a brief technical report. Int J Radiat Oncol Biol Phys. 2004;60:1419–24.

    Article  PubMed  Google Scholar 

  27. Terezakis SA, Hunt MA, Kowalski A, et al. [(1)(8)F]FDG-positron emission tomography coregistration with computed tomography scans for radiation treatment planning of lymphoma and hematologic malignancies. Int J Radiat Oncol Biol Phys. 2011;81:615–22.

    Article  PubMed  Google Scholar 

  28. Nestle U, Kremp S, Grosu AL. Practical integration of [18F]-FDG-PET and PET-CT in the planning of radiotherapy for non-small cell lung cancer (NSCLC): the technical basis, ICRU-target volumes, problems, perspectives. Radiother Oncol. 2006;81:209–25.

    Article  CAS  PubMed  Google Scholar 

  29. Ashamalla H, Rafla S, Parikh K, et al. The contribution of integrated PET/CT to the evolving definition of treatment volumes in radiation treatment planning in lung cancer. Int J Radiat Oncol Biol Phys. 2005;63:1016–23.

    Article  PubMed  Google Scholar 

  30. Mah K, Caldwell CB, Ung YC, et al. The impact of (18)FDG-PET on target and critical organs in CT-based treatment planning of patients with poorly defined non-small-cell lung carcinoma: a prospective study. Int J Radiat Oncol Biol Phys. 2002;52:339–50.

    Article  PubMed  Google Scholar 

  31. Paulino AC, Johnstone PA. FDG-PET in radiotherapy treatment planning: Pandora's box? Int J Radiat Oncol Biol Phys. 2004;59:4–5.

    Article  PubMed  Google Scholar 

  32. Erdi YE, Mawlawi O, Larson SM, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer. 1997;80:2505–9.

    Article  CAS  PubMed  Google Scholar 

  33. Biehl KJ, Kong FM, Dehdashti F, et al. 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate? J Nucl Med. 2006;47:1808–12.

    PubMed  Google Scholar 

  34. Hyun SH, Choi JY, Kim K, et al. Volume-based parameters of (18)F-fluorodeoxyglucose positron emission tomography/computed tomography improve outcome prediction in early-stage non-small cell lung cancer after surgical resection. Ann Surg. 2013;257:364–70.

    Article  PubMed  Google Scholar 

  35. Nestle U, Kremp S, Schaefer-Schuler A, 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 

  36. Graves EE, Quon A, Loo BW, Jr. RT_Image: an open-source tool for investigating PET in radiation oncology. Technol Cancer Res Treat 2007;6:111-121.

  37. Werner-Wasik M, Nelson AD, Choi W, et al. What is the best way to contour lung tumors on PET scans? Multiobserver validation of a gradient-based method using a NSCLC digital PET phantom. Int J Radiat Oncol Biol Phys. 2012;82:1164–71.

    Article  PubMed  Google Scholar 

  38. Geets X, Lee JA, Bol A, Lonneux M, Gregoire VA. Gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging. 2007;34:1427–38.

    Article  PubMed  Google Scholar 

  39. Sridhar P, Mercier G, Tan J, Truong MT, Daly B, Subramaniam RM FDG-PET. Metabolic tumor volume segmentation and pathologic volume of primary human solid tumors. AJR Am J Roentgenol. 2014;202:1114–9.

  40. Liao S, Penney BC, Zhang H, Suzuki K, Prognostic PY. Value of the quantitative metabolic volumetric measurement on 18F-FDG PET/CT in stage IV nonsurgical small-cell lung cancer. Acad Radiol. 2012;19:69–77.

    Article  PubMed  Google Scholar 

  41. Obara P, Liu H, Wroblewski K, et al. Quantification of metabolic tumor activity and burden in patients with non-small-cell lung cancer: is manual adjustment of semiautomatic gradient-based measurements necessary? Nucl Med Commun. 2015;36:782–9.

    Article  CAS  PubMed  Google Scholar 

  42. Xu C, Prince JL. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process. 1998;7:359–69.

    Article  CAS  PubMed  Google Scholar 

  43. Abdoli M, Dierckx RA, Zaidi H. Contourlet-based active contour model for PET image segmentation. Med Phys. 2013;40:082507.

    Article  CAS  PubMed  Google Scholar 

  44. Hatt M, Cheze-Le Rest C, Aboagye EO, et al. Reproducibility of 18F-FDG and 3′-deoxy-3′-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010;51:1368–76.

    Article  CAS  PubMed  Google Scholar 

  45. Lapuyade-Lahorgue J, Visvikis D, Pradier O, Cheze Le Rest C, Hatt M. SPEQTACLE: an automated generalized fuzzy C-means algorithm for tumor delineation in PET. Med Phys. 2015;42:5720–34.

  46. Sharif MS, Abbod M, Amira A, Zaidi H. Artificial neural network-based system for PET volume segmentation. Int J Biomed Imaging. 2010;2010:105610.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Hatt M, Cheze le Rest C, Descourt P, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys. 2010;77:301–8.

    Article  PubMed  Google Scholar 

  48. 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.

  49. Hatt M, Groheux D, Martineau A, et al. Comparison between 18F-FDG PET image-derived indices for early prediction of response to neoadjuvant chemotherapy in breast cancer. J Nucl Med. 2013;54:341–9.

    Article  CAS  PubMed  Google Scholar 

  50. Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern. 1979;9:62–6.

  51. Huang E, Solaiyappan M, Cho S. Improved stability and performance of 18F-FDG PET automated tumor segmentation using multi-level maximization of inter-class variance method. J Nucl Med. 2015;56:452.

    Article  Google Scholar 

  52. Mehta G, Chander A, Huang C, Kelly M, Fielding P. Feasibility study of FDG PET/CT-derived primary tumour glycolysis as a prognostic indicator of survival in patients with non-small-cell lung cancer. Clin Radiol. 2014;69:268–74.

    Article  CAS  PubMed  Google Scholar 

  53. Arslan N, Tuncel M, Kuzhan O, et al. Evaluation of outcome prediction and disease extension by quantitative 2-deoxy-2-[18F] fluoro-D-glucose with positron emission tomography in patients with small cell lung cancer. Ann Nucl Med. 2011;25:406–13.

    Article  PubMed  Google Scholar 

  54. Yoo Ie R, Chung SK, Park HL, et al. Prognostic value of SUVmax and metabolic tumor volume on 18F-FDG PET/CT in early stage non-small cell lung cancer patients without LN metastasis. Biomed Mater Eng. 2014;24:3091–103.

    PubMed  Google Scholar 

  55. Lin Y, Lin WY, Kao CH, Yen KY, Chen SW, Yeh JJ. Prognostic value of preoperative metabolic tumor volumes on PET-CT in predicting disease-free survival of patients with stage I non-small cell lung cancer. Anticancer Res. 2012;32:5087–91.

    PubMed  Google Scholar 

  56. Abelson JA, Murphy JD, Trakul N, et al. Metabolic imaging metrics correlate with survival in early stage lung cancer treated with stereotactic ablative radiotherapy. Lung Cancer. 2012;78:219–24.

    Article  PubMed  Google Scholar 

  57. Kim K, Kim SJ, Kim IJ, Kim YS, Pak K, Kim H. Prognostic value of volumetric parameters measured by F-18 FDG PET/CT in surgically resected non-small-cell lung cancer. Nucl Med Commun. 2012;33:613–20.

    Article  PubMed  Google Scholar 

  58. Harris JP, Chang-Halpenny CN, Maxim PG, et al. Outcomes of modestly Hypofractionated radiation for lung tumors: pre- and mid-treatment positron emission tomography-computed tomography metrics as prognostic factors. Clin Lung Cancer. 2015;16:475–85.

    Article  PubMed  Google Scholar 

  59. Carvalho S, Leijenaar RT, Velazquez ER, et al. Prognostic value of metabolic metrics extracted from baseline positron emission tomography images in non-small cell lung cancer. Acta Oncol. 2013;52:1398–404.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Lee VH, Chan WW, Lee EY, et al. Prognostic significance of standardized uptake value of lymph nodes on survival for stage III non-small cell lung cancer treated with definitive concurrent chemoradiotherapy. Am J Clin Oncol. 2014;39:355–62.

  61. Park SY, Yoon JK, Park KJ, Lee SJ. Prediction of occult lymph node metastasis using volume-based PET parameters in small-sized peripheral non-small cell lung cancer. Cancer Imaging. 2015;15:21.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Burger IA, Vargas HA, Apte A, et al. PET quantification with a histogram derived total activity metric: superior quantitative consistency compared to total lesion glycolysis with absolute or relative SUV thresholds in phantoms and lung cancer patients. Nucl Med Biol. 2014;41:410–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Chen GH, Yao ZF, Fan XW, et al. Variation in background intensity affects PET-based gross tumor volume delineation in non-small-cell lung cancer: the need for individualized information. Radiother Oncol. 2013;109:71–6.

    Article  PubMed  Google Scholar 

  64. Yu J, Li X, Xing L, et al. Comparison of tumor volumes as determined by pathologic examination and FDG-PET/CT images of non-small-cell lung cancer: a pilot study. Int J Radiat Oncol Biol Phys. 2009;75:1468–74.

    Article  PubMed  Google Scholar 

  65. Laffon E, de Clermont H, Lamare F, Marthan R. Variability of total lesion glycolysis by 18F-FDG-positive tissue thresholding in lung cancer. J Nucl Med Technol. 2013;41:186–91.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Hyung-Jun Im, Tyler Bradshaw & Steve Y. Cho

  2. Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea

    Hyung-Jun Im

  3. Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Meiyappan Solaiyappan & Steve Y. Cho

  4. University of Wisconsin Carbone Cancer Center, Madison, WI, USA

    Steve Y. Cho

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  1. Hyung-Jun Im
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Correspondence to Steve Y. Cho.

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Conflict of Interest

Hyung-Jun Im was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (490-20,170,035). Tyler Bradshaw, Meiyappan Solaiyappan, and Steve Y. Cho declare that they have no conflict of interest.

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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 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The institutional review board of our institute approved retrospective studies which were used in this review article, and the requirement to obtain informed consent was waived.

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Im, HJ., Bradshaw, T., Solaiyappan, M. et al. Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better?. Nucl Med Mol Imaging 52, 5–15 (2018). https://doi.org/10.1007/s13139-017-0493-6

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  • DOI: https://doi.org/10.1007/s13139-017-0493-6

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