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Cited by 282

The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.

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

Open access
Im, Hyung-Jun, Bradshaw, Tyler, Solaiyappan, Meiyappan, Cho, Steve Y.·2018·Nuclear Medicine and Molecular Imaging

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Show all 49 references

Research works printed but not linked: 16

  1. 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. · doi:10.1007/s13139-013-0232-6
  2. 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. · doi:10.2214/ajr.14.13847
  3. 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. · doi:10.1097/mnm.0000000000000382
  4. 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. · doi:10.2967/jnumed.115.167684
  5. 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. · doi:10.1097/rlu.0b013e3181d18eb0
  6. 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. · doi:10.1016/j.ijrobp.2010.06.044
  7. 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. · doi:10.1097/mnm.0000000000000317
  8. Sharif MS, Abbod M, Amira A, Zaidi H. Artificial neural network-based system for PET volume segmentation. Int J Biomed Imaging. 2010;2010:105610. · doi:10.1155/2010/105610
  9. 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. · doi:10.1007/s00259-010-1688-6
  10. 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. · doi:10.2967/jnumed.114.146381
  11. 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. · doi:10.1016/j.crad.2013.10.010
  12. 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. · doi:10.1007/s12149-011-0478-y
  13. 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.
  14. 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. · doi:10.1097/coc.0000000000000070
  15. 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. · doi:10.1016/j.radonc.2013.08.033
  16. 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. · doi:10.1016/j.ijrobp.2009.01.019

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Metadata

Authors
Im, H.-J., Bradshaw, T., Solaiyappan, M., & Cho, S. Y.
Venue
Nuclear Medicine and Molecular Imaging
Published
2017-09-19
Volume
52
Issue
1
Pages
5-15
ISSN
  • 1869-3474 (ISSN-L)
  • 1869-3482
OpenAlex
W2756233751
Cited by
282
References
49
FWCI
6.82
Type
journal article
Language
en
Open access
green
Funders
National Research Foundation of Korea (490-20170035)

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CoChat. (2026). Citation check: Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? https://app.cochat.ai/papers/10.1007/s13139-017-0493-6
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Im, H.-J., Bradshaw, T., Solaiyappan, M., & Cho, S. Y. (2018). Current Methods to Define Metabolic Tumor Volume in Positron Emission Tomography: Which One is Better? Nuclear Medicine and Molecular Imaging. https://doi.org/10.1007/s13139-017-0493-6

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