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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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Author information

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.

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

Informed Consent

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, H., 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) doi:10.1007/s13139-017-0493-6

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Keywords

  • Metabolic tumor volume
  • 18F-fluorodeoxyglucose
  • Positron emission tomography
  • Tumor
  • Segmentation