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Quantitative Analysis in Nuclear Oncologic Imaging

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Quantitative Analysis in Nuclear Medicine Imaging

6. Summary

The role of PET during the past decade has evolved rapidly from that of a pure research tool to a methodology of enormous clinical potential. FDG-PET is widely used in the diagnosis, staging, and assessment of tumor response to therapy, since metabolic changes generally precede the more conventionally measured parameter of change in tumor size. Data are accumulating rapidly to validate the efficacy of FDG imaging in a wide variety of malignant tumors with sensitivities and specificities often in the high 90-percentile range. Although metabolic imaging is an obvious choice, the design of specific clinical protocols is still under development. The tracers or combinations of tracers to be used, when the imaging should be done after therapy, the selection of optimal acquisition and processing protocols, the method of accurately performing quantitative or semi-quantitative analysis of data are still undetermined. Moreover, each tumor-therapy combination may need to be independently optimized and validated.

We expect that whole-body FDG-PET-based techniques may be accurate and cost-effective for staging or restaging of different cancer types and can contribute to the improvement of cancer patients’ management and monitoring with respect to medical cost. In addition to improving overall scanner performance, further work will focus on implementing practical corrections for patient-related perturbations such as non-homogeneous scatter and photon attenuation. During the next few years, it is believed that such sophisticated techniques will become more widely available in clinical settings and not only limited to research studies in nuclear medicine departments with advanced scientific and technical support. Therefore, it is expected that commercial hardware and software for accurate quantitative analysis will undertake major revisions in the future to be capable of facing emerging clinical applications and challenging research perspectives

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References

  1. Sullivan D. C., Challenges and opportunities for in vivo imaging in oncology. Technol Cancer Res Treat 1: 419–422 (2002).

    Google Scholar 

  2. Dewaraja Y. K., Ljungberg, M. and Koral, K. F., Characterization of scatter and penetration using Monte Carlo simulation in 131I imaging. J Nucl Med 41: 123–130 (2000).

    Google Scholar 

  3. Siegel J. A., Zeiger, L. S., Order, S. E. et al., Quantitative bremsstrahlung single photon emission computed tomographic imaging: use for volume, activity, and absorbed dose calculations. Int J Radiat Oncol Biol Phys 31: 953–958 (1995).

    Article  Google Scholar 

  4. Hoffman E., van-der-Stee, M., Ricci, A. et al., Prospects for both precision and accuracy in positron emission tomography. Ann Neurol 15: S25–34 (1984).

    Article  Google Scholar 

  5. Weinberg I. N., Huang, S. C., Hoffman, E. J. et al., Validation of PET-acquired input functions for cardiac studies. J Nucl Med 29: 241–247 (1988).

    Google Scholar 

  6. Phelps M. E., Huang, S. C., Hoffman, E. J. et al., Tomographic measurement of local cerebral glucose metabolic rate in humans with (F-18)2-fluoro-2-deoxy-Dglucose: validation of method. Ann Neurol 6: 371–388 (1979).

    Article  Google Scholar 

  7. Eary J. F. and Mankoff, D. A., Determination of tumor metabolic rates in sarcoma using FDG PET: practical approaches. J Nucl Med 39: 250–254 (1998).

    Google Scholar 

  8. Olshen A. B. and O’Sullivan, F., Camouflaged deconvolution with application to blood curve modeling in FDG PET studies. J Am Stat Assoc 92: 1293–1303 (1997).

    Article  MATH  Google Scholar 

  9. Shields A. F., Graham, M. M., Kozawa, S. M. et al., Contribution of labeled carbon dioxide to PET imaging of carbon-11-labeled compounds. J Nucl Med 33: 581–584 (1992).

    Google Scholar 

  10. Minn H., Zasadny, K. R., Quint, L. E. et al., Lung cancer: reproducibility of quantitative measurements for evaluating 2-[F-18]-fluoro-2-deoxy-D-glucose uptake at PET. Radiology 196: 167–173 (1995).

    Google Scholar 

  11. Weber W. A., Ziegler, S. I., Thodtmann, R. et al., Reproducibility of metabolic measurements in malignant tumors using FDG PET. J Nucl Med 40: 1771–7 (1999).

    Google Scholar 

  12. Maquet P., Dive, D., Salmon, E. et al., Cerebral glucose utilization during sleep-wake cycle in man determined by positron emission tomography and [18F]2-fluoro-2-deoxy-D-glucose method. Brain Res 513: 136–143 (1990).

    Article  Google Scholar 

  13. Mankoff D. A., Shields, A. F., Graham, M. M. et al., Kinetic analysis of 2-[C-11]-thymidine PET imaging studies: compartmental model and mathematical analysis. J Nucl Med 39: 1043–1055 (1998).

    Google Scholar 

  14. Bassingthwaighte J. B. and Chaloupa, J. M., Sensitivity functions in the estimation of parameters of cellular exchange. Fed Proc 43: 181–184 (1984).

    Google Scholar 

  15. Press W., Flannery, B., Teukolsky, S. et al., Numerical Recipes in C., Cambridge University Press, New York, (1988).

    MATH  Google Scholar 

  16. Sokoloff L., Reivich, M., Kennedy, C. et al., The [14C]deoxyglucose method for the measurement of local cerebral glucose utilization: theory, procedure, and normal values in the conscious and anesthetized albino rat. J Neurochem 28:897–916 (1977).

    Google Scholar 

  17. Reivich M., Alavi, A., Wolf, A. et al., Glucose metabolic rate kinetic model parameter determination in humans: the lumped constant and rate constants for [18F]fluorodeoxyglucose and [11C]deoxyglucose. J Cereb Blood Flow Metabol 5: 179–192 (1985).

    Google Scholar 

  18. Spence A., Muzi, M., Graham, M. et al., Glucose metabolism in human malignant gliomas measured quantitatively with PET, 1-[C-11]glucose and FDG: analysis of the FDG lumped constant. J Nucl Med 39: 440–448 (1998).

    Google Scholar 

  19. Wells J. M., Mankoff, D. A., Muzi, M. et al., Kinetic analysis of 2-[C-11]-thymidine PET imaging studies of malignant brain tumors: Compartmental model investigation and mathematical analysis. Mol Imaging 3: 145–150 (2002).

    Article  Google Scholar 

  20. Graham M. M., Kinetic evaluation using sensitivity functions and correlation matrices. J Nucl Med 36: 268P (1995).

    Google Scholar 

  21. Oldendorf W. H. and Szabo, J., Amino acid assignment to one of three blood-brain barrier amino acid carriers. Am J Physiol 230: 94–98 (1976).

    Google Scholar 

  22. Cornford E. M. and Oldendorf, W. H., Independent blood-brain barrier transport systems for nucleic acid precursors. Biochim Biophys Acta 394: 211–219 (1975).

    Article  Google Scholar 

  23. Mankoff D. A., Shields, A. F., Link, J. M. et al., Kinetic analysis of 2-[C-11] thymidine PET imaging studies: validation studies. J Nucl Med 40: 614–624 (1999).

    Google Scholar 

  24. Jonson S. D., Bonasera, T. A., Dehdashti, F. et al., Comparative breast tumor imaging and comparative in vitro metabolism of 16α-[18F]fluoroestradiol-17β and 16β-[18F]fluoromoxestrol in isolated hepatocytes. Nucl Med Biol 26: 123–130 (1999).

    Article  Google Scholar 

  25. Graham M. M., Muzi, M., Spence, A. M. et al., The FDG lumped constant in normal human brain. J Nucl Med 43: 1157–1166 (2002).

    Google Scholar 

  26. Mintun M. A., Welch, M. J., Siegel, B. A. et al., Breast cancer: PET imaging of estrogen receptors. Radiology 169: 45–48 (1988).

    Google Scholar 

  27. Vesselle H., Grierson, J., Muz, i. M. et al., In vivo validation of 3′deoxy-3′-[(18)F]fluorothymidine ([(18)F]FLT) as a proliferation imaging tracer in humans: correlation of [(18)F]FLT uptake by positron emission tomography with Ki-67 immunohistochemistry and flow cytometry in human lung tumors. Clin Cancer Res 8: 3315–3323 (2002).

    Google Scholar 

  28. Bos R., van Der Hoeven, J. J., van Der Wall, E. et al., Biologic correlates of (18)fluorodeoxyglucose uptake in human breast cancer measured by positron emission tomography. J Clin Oncol 20: 379–387 (2002).

    Article  Google Scholar 

  29. Shields A. F., Mankoff, D. A., Graham, M. M. et al., Analysis of 2-[C-11]-thymidine blood metabolites in PET imaging. J Nucl Med 37: 290–296 (1996).

    Google Scholar 

  30. Huang S.-C., Yu, D.-C., Barrio, J. R. et al., Kinetics and modeling of L-6-[F-18]Fluoro-DOPA in human Positron Emission Tomographic studies. J Cereb Blood Flow Metab 11: 898–913 (1991).

    Google Scholar 

  31. Mankoff D. A., Dunnwald, L. K., Gralow, J. R. et al., Blood flow and metabolism in locally advanced breast cancer: relationship to response to therapy. J Nucl Med 43: 500–509 (2002).

    Google Scholar 

  32. Zasadny K. R., Tatsumi, M. and Wahl, R. L., FDG metabolism and uptake versus blood flow in women with untreated primary breast cancers. Eur J Nucl Med Mol Imaging 30: 274–280 (2003).

    Article  Google Scholar 

  33. O’Sullivan F., Metabolic images from dynamic positron emission tomography studies. Stat Methods Med Res 3: 87–101 (1994).

    Google Scholar 

  34. Lassen N. A., Iida, H. and Kanno, I., Parametric imaging in nuclear medicine. Ann Nucl Med 9: 167–170 (1995).

    Article  Google Scholar 

  35. Cunningham V. and Jones, T., Spectral analysis of dynamic PET studies. Cereb Blood Flow Metab 13: 15–23 (1993).

    Google Scholar 

  36. O’Sullivan F., Imaging radiotracer model parameters in PET: a mixture analysis approach. IEEE Trans Med Imag 12: 399–412 (1993).

    Article  Google Scholar 

  37. Cunningham V., Ashburner, J., Byrne, H. et al., “Use of spectral analysis to obtain images from dynamic PET studies.” in: Quantification of Brain Function: Tracer Kinetics and Image Analysis in PET, edited by Uemura K, NA Lasson, T Jones et al. Elsevier, Amsterdam, (1993), pp 101–108.

    Google Scholar 

  38. Patlak C. S., Blasberg, R. G. and Fenstermacher, J. D., Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. J Cereb Blood Flow Metab 3: 1–7 (1983).

    Google Scholar 

  39. Gjedde A., Calculation of cerebral glucose phosphorylation from brain uptake of glucose analogs in vivo: A re-examination. Brain Res Rev 4: 237–274 (1982).

    Article  Google Scholar 

  40. Patlak C. S. and Blasberg, R. G., Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations J Cereb Blood Flow Metab 5: 584–590 (1985).

    Google Scholar 

  41. Logan J., Graphical analysis of PET data applied to reversible and irreversible tracers. Nucl Med Biol 27: 661–670 (2000).

    Article  Google Scholar 

  42. Patlak C. S., Dhawan, V., Takikawa, S. et al., “Estimation of striatal uptake rate constant of FDOPA using PET: methodologic issues.” in: Quantification of Brain Function. Tracer Kinetics and Image Analysis in Brain PET, edited by K. Uemera et al Elsevier Science Publishers, (1993).

    Google Scholar 

  43. Mankoff D. A., Shields, A. F., Graham, M. M. et al., A graphical analysis method to estimate blood-to-tissue transfer constants for tracers with labeled metabolites. J Nucl Med 37: 2049–2057 (1996).

    Google Scholar 

  44. Zaidi H., Montandon, M.-L. and Slosman, D. O., Attenuation compensation in cerebral 3D PET: effect of the attenuation map on absolute and relative quantitation. Eur J Nucl Med Mol Imaging 31: 52–63 (2004).

    Article  Google Scholar 

  45. Woodward H. Q., Bigler, R. E., Freed, B. et al., Expression of tissue isotope distribution. J Nucl Med 16: 958–959 (1975).

    Google Scholar 

  46. Zasadny K. R. and Wahl, R. L., Standardized uptake values of normal tissues at PET with 2-[fluorine-18]-fluoro-2-deoxy-D-glucose: variations with body weight and a method for correction. Radiology 189: 847–850 (1993).

    Google Scholar 

  47. Kim C. K., Gupta, N., Chandramouli, B. et al., Stardized uptake values of FDG: body surface area correction is preferable to body weight correction. J Nucl Med 35: 164–167 (1994).

    Google Scholar 

  48. Hunter G., Hamberg, L., Alpert, N. et al., Simplified measurement of deoxyglucose utilization rate. J Nucl Med 37: 950–955 (1996).

    Google Scholar 

  49. Keyes J. W. J., SUV: standard uptake or silly useless value? J Nucl Med 36: 1836–1839 (1995).

    Google Scholar 

  50. Huang S.-C., Anatomy of SUV. Nucl Med Biol 27: 643–646 (2000).

    Article  Google Scholar 

  51. Hamberg L. M., Hunter, G. J., Alpert, N. M. et al., The dose uptake ratio as an index of glucose metabolism: useful parameter or oversimplification? J Nucl Med 35: 1308–1312 (1994).

    Google Scholar 

  52. Thie J. A., Hubner, K. F. and Smith, G. T., Optimizing imaging time for improved performance in oncology PET studies. Mol Imaging Biol 4: 238–244 (2002).

    Article  Google Scholar 

  53. Beaulieu S., Kinahan, P., Tseng, J. et al., SUV varies with time after injection in FDG PET of breast cancer: characterization and method to adjust for time differences. J Nucl Med 44: 1044–1050 (2003).

    Google Scholar 

  54. Hoffman E. J., Huang, S. C. and Phelps, M. E., Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr 3: 299–308 (1979).

    Google Scholar 

  55. Geworski L., Knoop, B. O., de_Cabrejas, M. L. et al., Recovery correction for quantitation in emission tomography: a feasibility study. Eur J Nucl Med 27: 161–169 (2000).

    Article  Google Scholar 

  56. Chen C. H., Muzic, R. F., Nelson, A. D. et al., Simultaneous recovery of size and radioactivity concentration of small spheroids with PET data. J Nucl Med 40: 118–130 (1999).

    Google Scholar 

  57. Schoenahl F. and Zaidi, H., “Towards optimal model-based partial volume effect correction in oncological PET.” Proc. IEEE Nuclear Science Symposium and Medical Imaging Conference, Oct. 19–22, Rome, Italy (2004) in press

    Google Scholar 

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Author information

Authors and Affiliations

  1. Division of Nuclear Medicine, University of Washington Medical Center, Box 346113, Seattle, WA, 98195, USA

    Dr D. A. Mankoff & Dr M. Muzi

  2. Division of Nuclear Medicine, Geneva University Hospital, CH- 1211, Geneva, Switzerland

    PD Dr H. Zaidiy

Authors
  1. Dr D. A. Mankoff
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  2. Dr M. Muzi
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  3. PD Dr H. Zaidiy
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Editors and Affiliations

  1. Division of Nuclear Medicine, Geneva University Hospital, Geneva, CH-1211, Switzerland

    Habib Zaidi Ph.D.

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Mankoff, D.A., Muzi, M., Zaidiy, H. (2006). Quantitative Analysis in Nuclear Oncologic Imaging. In: Zaidi, H. (eds) Quantitative Analysis in Nuclear Medicine Imaging. Springer, Boston, MA. https://doi.org/10.1007/0-387-25444-7_16

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