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

被引:187
作者
Im H.-J. [1 ,2 ]
Bradshaw T. [1 ]
Solaiyappan M. [3 ]
Cho S.Y. [1 ,3 ,4 ]
机构
[1] Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
[2] Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul
[3] Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD
[4] University of Wisconsin Carbone Cancer Center, Madison, WI
基金
新加坡国家研究基金会;
关键词
!sup]18[!/sup]F-fluorodeoxyglucose; Metabolic tumor volume; Positron emission tomography; Segmentation; Tumor;
D O I
10.1007/s13139-017-0493-6
中图分类号
学科分类号
摘要
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. © 2017, Korean Society of Nuclear Medicine.
引用
收藏
页码:5 / 15
页数:10
相关论文
共 65 条
  • [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, 36, pp. 782-789, (2015)
  • [42] Xu C., Prince J.L., Snakes, shapes, and gradient vector flow, IEEE Trans Image Process, 7, pp. 359-369, (1998)
  • [43] Abdoli M., Dierckx R.A., Zaidi H., Contourlet-based active contour model for PET image segmentation, Med Phys, 40, (2013)
  • [44] Hatt M., Cheze-Le Rest C., Aboagye E.O., Et al., Reproducibility of 18F-FDG and 3′-deoxy-3′-18F-fluorothymidine PET tumor volume measurements, J Nucl Med, 51, pp. 1368-1376, (2010)
  • [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, 42, pp. 5720-5734, (2015)
  • [46] Sharif M.S., Abbod M., Amira A., Zaidi H., Artificial neural network-based system for PET volume segmentation, Int J Biomed Imaging, 2010, (2010)
  • [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, 77, pp. 301-308, (2010)
  • [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, 38, pp. 663-672, (2011)
  • [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, 54, pp. 341-349, (2013)
  • [50] Otsu N., A threshold selection method from gray-level histograms, IEEE Trans Syst Man Cybern, 9, pp. 62-66, (1979)